However, adopting artificial intelligence for the workplace may feel intimidating. So let us make it easier for you. We’ve gathered in-depth expertise while helping companies integrate AI to drive efficiency, innovation, and growth. 

To assist you with this transformation, we have analyzed hundreds of workplace AI implementation use cases and identified the top ten ways AI can revolutionize your workplace in 2025.

Why Use AI in the Workplace

Artificial Intelligence in the workplace refers to using artificial intelligence technologies to optimize business functions, remove biases, automate tasks, and boost workplace productivity. Here are some reasons why you should adopt AI technologies in your workplace too:  

• AI-powered tools automate repetitive tasks, boosting efficiency and convenience. 
• It gets the mundane tasks off your employees’ hands and lets them focus on strategic tasks, leading to more innovation. 
• You can train the AI systems to identify employees’ work patterns and preferences and gauge their needs. This leads to a more personalized employee experience, boosting satisfaction. 
• AI in the workplace can predict equipment failures, reducing your system downtime and maintenance costs. 
• AI offers predictive analysis and can forecast demand. This helps you manage inventory, optimize shipping routes, and eliminate lingering logistical challenges before they escalate. 
• You can identify trends early on and adapt quickly to market changes, delivering better business outcomes.

AI in the workplace statistics

Here are some data points to show you the importance of implementing AI in the workplace: 

• 72% of business leaders believe that AI can improve their work-life balance.
• 55% of knowledge workers note that AI tools can make their job “more interesting.”
• According to 74% of US executives, generative AI will benefit their employees.
• Research also found that companies that deploy AI for operations outperformed their peers by 44% in metrics like employee retention and revenue growth.

What are the 10 ways you can use AI in the workplace?

1. More productive and satisfied employees

Are repetitive tasks hogging up a big chunk of your employees’ workday? Integrating AI in the workplace can free up their time, allowing them to focus on high-value work. 

With the right AI-powered productivity tools, employees can automate repetitive tasks like data entry, scheduling, and basic customer service inquiries. With mundane tasks out of the way, they get to engage in more creative aspects of their job, innovate, and bring out-of-the-box ideas to the table. This not only boosts productivity but also increases job satisfaction. 

In fact, 61% of employees say that using artificial intelligence helps them have a more efficient and productive work day. 68% even want their employers to integrate more AI technology in the workplace. 

AI can also handle common employee questions about company policies, compensation, and benefits. This way, instead of reaching out to HR, employees can get instant answers, saving time and improving satisfaction.

For instance, ClickUp Brain is helping teams save time by providing quick insights on project status, and team tasks, and even answering queries without context switching. Moreover, the AI tool automatically generates updates, summarizes key information, and helps employees stay organized, all in one place.

“With the help of this powerful AI tool inside my work area—I can research, write, summarize, digest information, and project manage—all under one roof.” Amrita Mathur, VP, Marketing | Zapier

2. Better decision making

Relatively few companies have a set of hard metrics to measure the intricacies of workforce productivity at the team or department level. 

A recent study published by MIT Sloan Management Review noted that traditional KPIs increasingly fail to deliver the required information and insights. They fall short in tracking progress, identifying strategic misalignments, prioritizing resources, and ensuring accountability. 

Such a lack of detailed quantitative data reduces operational efficiencies, compromising the desired outcomes.

Even If they do, it’s sometimes unclear how productivity gains translate into dollars and cents. Plus, employees often struggle to analyze large volumes of data effectively. This delays decision-making, hampers workflow, and causes missed opportunities. 

AI can decode vast datasets, calculate predetermined metrics, and generate reports in real-time. Within mere seconds, you will have actionable insights into employee performance, the company’s operations, and how your strategies translate into profits. 

Skillsoft, an educational tech company, receives thousands of NPS datasets. Reading through them and understanding what customers think about their products is crucial. But doing it manually is an extremely tedious process. 

Using AI helped them overcome this problem. Apratim Purakayastha, chief product and technology officer and general manager of enterprise solutions at Skillsoft noted, 

“Now we can pass them through generative AI and it produces a pretty nice summary for us to process, ‘Okay, we need to do this better, this better, and this better.”

AI tools can also suggest optimal solutions based on facts, reducing bias. Your team can even create simulations, run A/B tests for every strategy, and choose the most effective one.

For example, Team-GPT helps you make the right decisions faster by combining data science and AI. 

We let you outline A/B testing methodology and run design experiments without any hassle. You can interpret results with our actionable insights and break down complex model outputs in simple words. That’s why using our tool can increase your productivity 12X by making complicated data analytics a cakewalk!

3. Streamlined collaboration

An organization’s success depends on how well its workforce works together. However, now that remote and hybrid work models have become quite common in workplaces, they pose certain collaboration challenges. 

Teams, especially remote ones, often struggle with effective and timely communication. Companies with global teams also face difficulties in coordinating meetings across time zones and accommodating multilingual teams. 

All in all, with work models getting more nuanced, workplace collaboration has become time-consuming, and error-prone. With AI, you can remove these roadblocks. Facilitate smooth collaboration, and leverage the full potential of a diverse team. 

AI tools like chatbots, smart assistants, and real-time transcription ensure clear and instant communication, reducing misunderstandings. Such tools can also suggest optimal meeting times for all participants based on availability and send reminders, making sure everyone is aware of upcoming meetings. 

Want seamless collaboration for your team? We at Team GPT offer a unified AI-powered workspace to transform how your team communicates across channels. 

With Team-GPT every stage of your workflow will be connected with the other, and no tasks will be siloed from the entire process.

Be it messages, or files, you won’t have to spend hours digging them up from unorganized manual collaboration tools. With our platform you can maintain a clutter-free workspace, easily locate files, and save time. You can also use translation tools for real-time communication between team members speaking different languages.

4. Cost savings

AI automates time-consuming repetitive tasks, which reduces labor costs and allows employees to focus on higher-value work. As discussed before, it reduces system downtime and maintains continuous workflow, making sure you get the best out of your resources. Plus, you can detect and address potential system threats and issues, saving you frequent maintenance costs. 

Besides making HR operations more efficient, AI-powered recruitment saves expenses by shortening the hiring cycle. You can also screen candidates more thoroughly, ensuring only the best ones come to your team. This avoids the cost of churn. 

Even the smallest mistakes in data entry, auditing, or system security can land you in compliance issues. Deploying AI to handle these aspects reduces errors in financial reporting, compliance checks, and inventory management, avoiding penalties and waste.

Chatbots can assist customers with queries, saving you the expense of maintaining a large support team. Moreover, AI in the workplace enables faster decision-making, and eliminates delays, leading to higher output at lower costs.

You can save significantly on advertising costs by using tools like Foreplay and Spyder to analyze trends and track competitor ads automatically. With AI Briefs, you can quickly generate scripts and storyboards based on your saved creative assets, while MidJourney allows you to create ad visuals tailored to diverse styles.

Finally, tools like ChatGPT help you draft multiple ad copies, headlines, and call-to-action (CTAs) in seconds. Using these tools reduces dependency on large creative teams and shortens turnaround times, enabling you to create impactful ad campaigns while cutting costs.

For example, Digital Lights, a part of Wiser Technology which crafts and operates advanced mission-critical software solutions, struggled with the high cost of AI tools. They also faced challenges in driving company-wide AI adoption. 

Once they chose Team-GPT as their AI solution, they got:

• Cost-effective access to various LLMs 
• Advanced collaborative platform for team-wide adoption
• Real-time usage analytics for tracking AI adoption

Our team:

• Conducted an internal survey to understand their AI adoption rates
• Ran usage analysis to identify fast-adopting teams
• Deployed these teams to spread awareness for quick company-wide adoption

As a result, Digital Lights saw a 4x Cost Reduction, accelerated AI adoption in teams, and improved productivity. 

5. Efficient HR operations 

From rigorous hiring cycles to addressing employee grievances, days in the HR department aren’t easy. However, the right use of AI can make their jobs much more streamlined. 

HR managers often sift through hundreds of resumes to find the right person for the positions. This delays hiring decisions and increases the risk of missing out on top talent. 

With AI, they can set up predefined criteria and automate resume screening and candidate shortlisting. Your HRs can even conduct initial interviews through chatbots. This reduces time-to-hire and ensures that only qualified candidates move forward in the process.

The selection process often gets compromised due to unconscious biases. AI-driven recruitment tools can eliminate them, guaranteeing the integrity of candidate selection where qualifications and experience matter, not the demographics. Result? A fair and more diverse recruitment process. 

AI can help your HRs monitor performance more efficiently, and track employee performance in real-time, providing managers with continuous feedback and data-driven insights. This helps HR managers identify high performers, address potential issues early, and personalize employee’s professional development plans.

For example, L’Oréal has integrated AI to handle the initial stages of their hiring process. Their AI tool, Maya, automated resume screening and initial candidate conversations, significantly reducing the time spent on these tasks. Additionally, Maya helps identify candidates who align with the company’s values, contributing to a more efficient and inclusive hiring process.

“A recruiter used to spend 45 minutes on a CV screening and phone interview. Maya handles this in just 4 minutes.” Niilesh Bhoite; Chief Digital Officer – Global HR, L’Oréal

Want to leverage AI to make HR operations easier and streamlined? Our platform, Team-GPT offers AI-powered HR solutions that:

• Analyzes employee data, predicting turnover, and identifying skill gaps
• Generates compelling job descriptions
• Automates resume screening
• Personalizes onboarding.

Here’s how you can create job ads and interview questions in about 4 minutes with Team-GPT:

6. Office safety

A safe workplace is fundamental for a healthy, motivated, and productive workforce. In the office, you are responsible for maintaining the utmost safety of your workers — and AI can help with that. 

AI-powered tools can identify risks and improve emergency responses by detecting potential hazards. Predictive analytics can gauge incidents like equipment malfunctions and fire risks before they occur. 

You can also deploy AI-driven surveillance to monitor real-time activity, identifying unauthorized access or unusual behavior. Virtual assistants and chatbots help employees report incidents quickly and access safety protocols.

Plus, providing employees with wearable AI devices can track their health and stress levels, letting them prioritize wellness and creating a safer and more comfortable workplace.

For example, Amazon—an American multinational technology company—has integrated AI into its warehouse safety protocols with real-time tracking systems that use computer vision and machine learning. These systems alert workers and managers to potential dangers, reducing injury risks. This approach has led to greater efficiency and helped Amazon maintain growth despite its already dominant position in the market.

“Robotics technology enables us to work smarter, not harder, to operate efficiently and safely,” said an Amazon spokesperson.

7. Predictive IT maintenance

Even the smallest issues in IT can snowball and cause prolonged workflow disruptions and system downtimes. As a growing business, you can’t afford such a loss of time. 

Artificial intelligence offers predictive analysis that can detect vulnerabilities in the systems early. You can address them early on, maintaining business continuity by preventing unexpected failures. This approach increases the efficiency of IT teams, eliminating frequent reactive troubleshooting. 

Deploying chatbots lets employees report IT issues, leading to prompt resolution and uninterrupted workflow. 

For example, a major aircraft manufacturer is using MathWorks for deep learning. They take images of components from their aircraft and run them through deep neural networks to detect defects or faults in these components. This use of image-based data is an improvement over the older method of manually extracting features from the images.

“AI is incredibly valuable when it comes to predictive maintenance. It helps answer critical questions like: Is my machine behaving abnormally? Is there a fault in my system, and what’s causing it? And most importantly, when is my system going to fail?” Aditya Baru, Senior Product Manager at MathWorks

8. Personalized employee training 

To stay ahead of the competition, you must upskill your employees constantly so that they can adapt to changing market needs and produce work accordingly. But as your workforce grows, it will become very difficult to identify their individual upskilling needs. Plus, a one-size-fits-all training approach doesn’t work well for diverse workforces.

Implementing AI in the workplace lets you see where employees need improvement through performance data and feedback. You can understand individual strengths and weaknesses and design training frequencies and styles accordingly. AI tools can generate personalized training modules based on roles, goals, and individual learning styles.

Plus, you can: 

• Automate training reminders.
• Create engaging and immersive training simulations with VR tools.
• Deliver real-time feedback.
• Monitor employee progress, provide actionable tips, and help them hone their skills at regular intervals. 

According to a PWC report, “72% of employees believe that AI-driven training tools are more engaging than traditional methods.”

For example, Beyond Retro—a vintage clothing retailer—faced the challenge of quickly upskilling their sales team after COVID-19 downsized its staff. To address this, they used AI-powered tools like Synthesia to create training courses in a short time.

9. Trendjacking and sentiment analysis

To reach the right audience and hold their attention, you must participate in trending industry conversations and showcase your expertise. However, the topics you choose have to be relevant to your niche and target audience. AI tools can help you identify the right trends for your company through media monitoring, keyword analysis, and competitor analysis. 

AI-powered media monitoring tools track viral topics and conversations across platforms, providing insights into trending hashtags and themes. Your marketing team can hop on these conversations and engage with your audience. Plus, your sales team can modify their lead nurturing approach to cater to the current needs of your target market. 

For example, Marriott International uses sentiment analysis and media monitoring tools to track customer reviews across its vast network of over 7,000 properties.

Analyzing feedback lets Marriott: 

• Quickly identify common pain points, like room cleanliness or staff friendliness.
• Take immediate action to address them. 

This approach not only helps in improving individual guest experiences but also assists the corporate team in identifying patterns across properties. 

10. Streamline marketing initiatives 

AI tools analyze customer behavior, segment audiences, and predict trends. By processing large amounts of data, AI provides insights that help you make informed decisions, optimize campaigns, and track performance more accurately.

A report found that 40% of marketers use AI for data analysis and reporting, showing how essential it has become in making quick, data-backed decisions.

Moreover, AI personalizes customer interactions by analyzing past behaviors and tailoring content to individual preferences. This means customers receive the most relevant offers, improving engagement. 

Netflix is a prime example of AI-driven marketing in action. The streaming platform uses machine learning algorithms to analyze users’ viewing history, preferences, and even how long they watch certain content. 

Based on this data, Netflix then shows recommendations for each user as “your next watch” as per their tastes and habits. Then they also send marketing communications such as emails based on this data.

Adopt AI Alongside Your Team On Team-GPT

To build a productive, motivated, and creative workforce, the best approach is to combine the humane essence of your employees with the advanced features of AI in the workplace.

That’s where we can help you. Our platform Team GPT offers an AI toolkit that will help you leverage the benefits of AI and build a happy and satisfied workforce. Here, you can: 

• Easily build a personalized library with AI-generated prompts, personas, and use cases.
• Brainstorm, plan, write, and edit any strategy in a streamlined process.
• Refine and summarize chats to transform raw ideas into well-crafted pieces.
• Access features like image generation and customizations.
• Make better and faster decisions with data-driven actionable insights.

Book a demo today and help your staff become a productive, engaged, and unified workforce. 

The post AI in the Workplace: 10 Ways Artificial Intelligence is Revolutionizing Workplaces [2025] appeared first on Team-GPT.

That got me thinking about how easy things would be if I had a virtual assistant to draft your emails, help me with customer engagement, or even assist in content creation.

And then…ChatGPT happened.

In this article, I tell you about the 5 best ChatGPT use cases that eased my workload and freed up time for my loved ones❣️

What are the Top ChatGPT Use Cases?

From content creation to customer service, use cases for ChatGPT are endless, but in this article, I cover five major use case categories:

• Marketing
• Coding
• Education
• Business
• Human Resources

👀 Note: Each category in the article has 4 use cases. Every use case has 1 initial prompt and 1 follow-up prompt.

Every prompt in the article follows a specific structure:

• It starts with me telling ChatGPT what I want it to do 
• I then give more context for personalization
• And then tell the GPT what output format I prefer

I use square brackets ‘[]’ where you must put in information or import data, and curly brackets ‘{}’ for variable information.

Of course, there will be some exceptions.

Let’s start with marketing.

👉 Related: How to Use ChatGPT: Best Guide for Beginners

Use Case Category #1. Marketing

From content creation to engagement, ChatGPT can create strategies to help market your product globally.

#1. Content Creation

Content is the heart of any marketing strategy, so I decided to test ChatGPT and see how good it is at creating content.

But remember, creating content is often a comprehensive process that includes research, outlining, drafting, editing, optimizing, and more. 

It’s something that humans are better at than AI, at least for long-form content.

But if you have limited time and budget, use AI to create first drafts; fact check and edit them thoroughly to improve quality.

Prompt:

Write a comprehensive guide for creating a high-quality, SEO-optimized blog post targeting the keyword ‘e-commerce marketing.’ 

The blog post should resonate with e-commerce store owners and contain persuasive calls to action, encouraging them to sign up for a weekly newsletter on e-commerce marketing tips.

Include best practices like keyword frequency, meta description, optimal title length, internal and external linking, and image optimization. The text should be around 2,000 words.

👀 Note: Instead of a 2,000-word blog post, ChatGPT initially gave me a 750-word article. To get the complete draft, I asked ChatGPT to elaborate further on the headers.

Follow-up prompt:

Based on the comprehensive guide for ‘e-commerce marketing,’ give me a condensed content version to turn into an infographic.

Here’s how you can write articles in about 3 minutes inside of Team-GPT alongside your team:

#2. Social Media Posting

Posting on social media regularly is a great way to build authority on any topic. But to post regularly, you must first develop a social media strategy. 

I leveraged ChatGPT to get:

• The content plan
• The best posting time
• Type of content to post
• Captions for each post

Prompt:

Develop a one-week social media content plan specifically for a new product launch on Instagram. 

The target audience is females aged 20-35 fitness enthusiasts. 

The plan should include optimal posting times, types of content (stories, reels, posts), and specific caption strategies. 

Include a persuasive CTA and encourage the audience to attend a live product demo. Ensure the plan is in a format that a social media manager can immediately implement.

Follow-up prompt:

Using Instagram’s one-week social media content plan, develop additional caption variations for each social media post.

#3. Email Marketing

ChatGPT is a good starting point to craft your email marketing strategy. All you need to do is define your target audience and educate ChatGPT about your product or service.

I did that, and it gave me a step-by-step strategy that has:

• Customer segmentation tips
• Email frequency information
• Email types and content
• Key performance indicators I should track

Prompt:

You are a world-class email marketer. I have a {{What is your product/service? (e.g. SaaS product, marketing agency, e-commerce shop, construction company)}}. 

We are selling {{What do you sell? (e.g. products, services, property)}} which is in the price range of {{What is the price range of your product?}}. 

I want to create an email funnel for {{What is the goal of this email funnel? (e.g. sign-up, checkout, follow-up, cart abandonment, nurture, etc.)}}.

Make me a plan for an email funnel that I should be sending to my clients. My ideal customers are {{Who is your ideal customer profile?}}. 

Whenever you suggest text for the emails, make them sound {{Tone of voice: witty, smart, casual, funny, playful, other}}. 

Make the emails {{Do you prefer the emails: short, medium, or long?}}.

Follow-up prompt:

For the above product and target audience – Craft emails for dormant customers, persuading them to buy from us. Keep the emails short.

#4. Digital Ad Strategy

ChatGPT can be an extended member of your Digital Ads team, helping you with strategy and ad copies. 

Ads can be a great way to bring in customers from day 1.

To get started, choose a platform to run your ads; for this example, I decided to run Meta (earlier Facebook) ads.

I asked ChatGPT for a 1-month ad strategy 👇

Prompt: 

[persona]

During this conversation, please assume the role of an experienced and insightful marketing specialist. 

Your responses should focus on providing guidance and advice on various marketing strategies, techniques, and tools to effectively promote products or services. 

As I ask questions, kindly offer tailored recommendations that suit my business needs, target audience, and industry trends. 

Create a valuable and informative conversation that helps me optimize my marketing efforts.

Here’s my question:

[What you want GPT to do]

Can you design a one-month Meta ad campaign blueprint to increase e-commerce sales for a winter clothing line?

[More context: Target audience + format]

The target audience is young adults living in colder climates. Include recommended budget allocation, target demographics, ad formats, and key metrics. 

The blueprint should be in a format easily understandable by a marketing team.

Follow-up prompt:

From the one-month Facebook ad campaign blueprint, create three ad copy variations for each ad format chosen.

Use Case Category #2. Coding

If you’re a beginner-level coder, consider ChatGPT as your teacher. Ask questions to ChatGPT to explain concepts, write sample codes, and know the logic behind a certain code.

#1. Writing Code

If you’re a developer working with complex algorithms, ask ChatGPT to return basic code snippets that outline the logic you need.

The GPT models are extensively trained in several coding languages. Developers often use ChatGPT to write code and save time to focus on code optimization.

Prompt: 

Write a Python script to automate the scraping process of top news headlines from a website like BBC News. 

The script should use libraries such as BeautifulSoup for HTML parsing and return a JSON file with headlines as keys and their corresponding URLs as values. 

Follow-up prompt: 

Can you modify the above Python script to scrape the top three articles under each headline and save them in the same JSON file?

#2. Debugging

Ever found yourself stuck on a code bug that won’t budge? It happens to me all the time! 😖

But with ChatGPT’s invention, all I have to do is tell ChatGPT to analyze my code and point out bugs.

The AI points out the issue, tells me how to solve it, and gives me the correct code snippet.

Prompt:

Here’s a JavaScript code snippet for a basic To-Do List web application. 

[add code]

The application is not updating the list of items as expected. Identify and debug the issues in the script.

Follow-up prompt:

What best practices can I implement to prevent similar bugs from happening in JavaScript applications?

#3. Code Compilation

I use ChatGPT for code compilation because it can:

• Translate the compiler’s error message into simple English for me to understand.
• Generate correct code snippets for all the errors I encountered during compilation.
• Offer guidance on including or linking necessary libraries, frameworks, and modules.
• Give me tips to improve code efficiency.

Here’s a sample prompt I used when compiling a Rust program.

Prompt:

How do I Compile a Rust program that reads from a CSV file and outputs the data into a newly created SQLite database? 

The program should have separate modules for file reading and database operations. 

Include a Cargo.toml file to manage dependencies and compile the program into a standalone executable.

#4. Code Explanation

❗ Spoiler alert: I am not a pro coder. 

When it comes to coding, there are so many technicalities I am unaware of. 

So how do I understand the code then? 

It’s simple – I ask ChatGPT to dissect complex lines of code and explain them to me in a digestible way.

Prompt:

Here’s a working Java application that sorts an array of integers using the bubble sort algorithm. 

[insert code]

Create comprehensive documentation that explains how the sorting function works, including the logic behind each line of code and how the algorithm’s complexity is determined.

Follow-up prompt:

Can you provide a step-by-step tutorial to convert the Java bubble sort application into a more efficient sorting algorithm, like Quicksort? Keep the original documentation style intact.

Use Case Category #3. Education

When it comes to education, ChatGPT can help teachers design curriculum, check homework, create exercises, and more. 

Here are some prompts ChatGPT’s good with.

👉 Related: Top AI Tools for Education

#1. Curriculum Design

High school teachers can use ChatGPT to outline curriculum designs that:

• Outline week-by-week topics
• Recommend educational resources
• Suggest various teaching methods – lectures, labs, and interactive activities

To help construct a well-rounded and effective learning experience, ChatGPT can further generate assignments, prompts, and quiz questions (more on this later).

Prompt:

Develop a 12-week curriculum outline for a high school-level course on Environmental Science. 

List the weekly topics and learning objectives. Suggest two hands-on activities for each week. Provide guidance on covering the topics for the best educational outcome.

Follow-up prompt:

Create a list of recommended supplementary materials like books, websites, and documentaries that align with the 12-week Environmental Science course.

Here’s how you can create a lesson plan in about 4 minutes with AI inside Team-GPT:

#2. Exercise Creation

Whether you’re looking for MCQs, word problems, or subject answers, ChatGPT can generate a wide range of exercise types for your students.

As a teacher, this saves you the time and hassle of creating exercises for each grade level yourself, thus enhancing both the teaching and learning experience.

Prompt:

Generate a set of 10 algebraic practice exercises designed for 9th-grade students at an intermediate math level. Each exercise should come with a step-by-step solution. Aim for a mix of linear equations and quadratic equations.

Follow-up prompt:

Can you add 3 word problems that require using algebraic equations to solve? Include step-by-step solutions for each.

#3. Proofreading and Grammar Check

I wanted to check if ChatGPT can spot errors in assignments and rectify them, so I used it for proofreading and grammar check.

I pasted the text in ChatGPT and asked it to check for accuracy.

An interesting thing I learned is that ChatGPT can also help with sentence structure and phrasing beyond simple grammar and spelling corrections.

Prompt:

Proofread and check the grammar of a two-page essay on the social impacts of technology. Highlight any ambiguous or unclear sentences and suggest possible rewrites. Make sure the essay follows APA formatting guidelines. Give me the suggested changes in a tabular form.

[insert essay]

Follow-up prompt:

Create a summary paragraph summarizing the essay’s main points while adhering to APA guidelines.

[insert essay]

#4. Homework Assistance

There are endless possibilities for how ChatGPT can help students with their homework. 

💲 My 2 cents: Instead of asking ChatGPT to get the work done directly, it’s better if students ask the artificial intelligence to explain the logic or concept behind the homework teachers give.

ChatGPT can help students with:

• Research 
• Math problems
• Writing support
• Proofreading

And more.

Prompt:

I am a 10th-grade student who needs help with a history assignment on the causes of World War I. 

Provide a structured outline that includes the key events leading up to the war, the main parties involved, and the roles they played. 

Follow-up prompt:

Provide an example thesis statement that I could use to guide my essay on the causes of World War I.

Use Case Category #4. Business

Founders and managers can use ChatGPT to finalize a decision or craft a go-to-market strategy. 

With your expertise and ChatGPT’s brainstorming capabilities, you can take your business to new heights.

#1. Decision-Making

Founders and C-suites are often tasked with making business decisions that shape the company’s future. 

Feeling stuck making tough decisions? Ask ChatGPT about what it thinks. 

Why? 🤔

ChatGPT can help you with multiple scenarios by giving you data-backed insights with strong reasoning. Although not a replacement for human expertise, it can serve as an auxiliary tool.

💡 Pro tip: Ask ChatGPT to act as a C-suite and tell what decision you must make. Explain ChatGPT and what you expect as output; it will help you make a logical decision.

Prompt:

Assume you’re the CEO of a Series A startup specializing in AI for healthcare. 

Conduct a Decision Tree Analysis to navigate a critical strategic fork: either diverting resources to a new R&D initiative or scaling existing solutions. 

Include estimated ROIs, probabilities, and risk factors. 

Follow-up prompt:

Identify and elaborate on three alternative scenarios that might affect the Decision Tree Analysis, such as a change in ROI estimations or a new entrant in the market.

#2. Build Frameworks

Frameworks are a system of rules used to govern a process or decisions, and ChatGPT is pretty good at creating one.

I have created several frameworks for strategic planning, marketing, product development, and organizational structure with ChatGPT myself.

Here’s an example 👇

Prompt:

You’re a senior strategy consultant tasked with stabilizing a declining brick-and-mortar retail store. 

Utilize the Ansoff Matrix to analyze four strategic directions: 

• Market penetration
• Market development
• Product development
• Diversification

For each strategy, outline potential revenue streams, capital requirements, and market risks. 

Follow-up prompt:

Given the strategic action plan from the Ansoff Matrix analysis, provide a three-month implementation roadmap with key milestones.

Provide a step-by-step solution and make sure you arrive at the correct answer.

#3. Goal Setting

With ChatGPT, you can set clear, actionable goals aligned with your overall strategy and vision.

I often use ChatGPT to know what KPIs to track, how to track them, and suggest realistic benchmarks per industry standards.

But you could also ask the model to employ certain methodologies to set your goals, as I have in the prompt below:

Prompt:

You’re the CFO of a multinational corporation eyeing expansion in Southeast Asia. 

Employ the Balanced Scorecard methodology to establish strategic goals for the next fiscal year. 

Analyze key performance indicators (KPIs) across four perspectives: Financial, Customer, Internal Processes, and Learning and growth. 

Follow-up prompt:

Break down one of the key performance indicators from each of the Balanced Scorecard’s four perspectives into tactical initiatives.

#4. Sales Pitch Creation

Feeding ChatGPT your product’s comprehensive details to get a sales pitch from it is a great way to leverage the platform.

When you feed in the right information, ChatGPT can address your client’s pain points and present your product as the go-to solution.

💡 Pro tip: To get a highly specific sales pitch – define your target audience, educate ChatGPT about your product, and give it sample pitches to analyze.

Prompt:

As a VP of Sales for an enterprise cybersecurity firm, you are preparing for a high-stakes pitch to a Fortune 500 company. 

Craft a Value Proposition to dissect clients’ pain points, gains, and jobs to be done. 

Leverage this analysis to formulate a targeted sales pitch that clearly explains your solution’s unique selling points and addresses the client’s specific pain points. 

Follow-up prompt:

Draft a series of follow-up emails to send after the pitch, each tailored to address potential questions or concerns from stakeholders at the Fortune 500 company. Keep the emails short and crisp.

Here’s how you can write a business proposal in about 3 minutes with AI inside Team-GPT:

Use Case Category #5. Human Resources

Your company’s human resource department can leverage ChatGPT to automate repetitive tasks like creating job posts, screening resumes, or creating onboarding materials for an employee.

#1. Create Job Description

ChatGPT is one of the best ways to improve speed and efficiency. 

Creating one job description a month is simple, but if you own a job board business that needs you to create 10 JDs a day, what do you do? 🤔

Train your HR manager to extract job information like role, responsibilities, and qualifications required through ChatGPT.

Here’s how to write job description prompts to get a killer JD for any role:

Prompt:

You’re an HR manager at a FinTech startup in need of a Data Scientist. 

Generate a job description that describes roles, responsibilities, and expectations. 

Specify at least 5 key responsibilities, technical and soft skills required, and desired academic qualifications. 

Follow-up prompt:

Draft an ideal candidate persona based on the Data Scientist job description. Include potential career paths within the company for this role.

Watch our guide on how to create job ads and interview questions in about 4 minutes with AI inside Team-GPT:

#2. Job Interview Questions

To get questions for the interview, upload the job description on ChatGPT and ask it for interview questions.

Alternatively, give ChatGPT the parameters you want to test the candidate, and the AI will give you interview questions accordingly.

Prompt:

You’re a Talent Acquisition Specialist looking for a Chief Marketing Officer for an e-commerce business. Create a set of 10 interview questions from which you can gauge the candidates:

• Domain expertise 
• Leadership capabilities
• Strategic vision
• Cultural fit

Follow-up prompt:

After conducting the interviews for the CMO role, what kind of scoring rubric would you suggest for evaluating candidates’ answers?

#3. Generate Onboarding Materials

I used ChatGPT to generate onboarding materials like:

• Company culture information 
• Tech stack information
• Training materials 

And much more.

Onboarding is the process of introducing a newly hired employee into an organization. 

When you type a prompt like the one below into ChatGPT, it will give you a comprehensive list of all onboarding materials required. 👇

Prompt:

Assume you’re an HR Director at a global consultancy firm. Provide a comprehensive list of onboarding materials needed for new Strategy Consultants. 

The materials should cover:

• Firm culture
• Project management tools
• Proprietary methodologies

Follow-up prompt:

Create a one-week orientation schedule that incorporates the onboarding materials you listed. Include time for meetings, training sessions, and social integration.

#4. Employee Retention

The final step in hiring a new candidate is retaining them. 

In the US, out of 1,000 employees,  31% quit their jobs in less than six months from when they were hired.

Hiring new candidates every 6 months for the same role could slow things down and impact work quality significantly. 

So why not tell ChatGPT to craft an employee retention strategy that helps you retain employees and save money?

Prompt:

You’re the Chief Human Resources Officer in a tech startup experiencing a high attrition rate among software engineers. 

Engage in a root-cause analysis based on existing retention metrics and employee feedback. 

Use the insights to formulate an employee retention strategy that includes career development, work-life balance, and competitive compensation. 

Follow-up prompt:

Based on the executive summary, draft a communications plan to roll out the new retention initiatives to software engineers. Include timelines and responsible parties.

Collaborate with Your Team to Get the Best Out of ChatGPT Prompts

For any business or organization – Two heads are better than one. So working in teams often gets you better results compared to working solo.

Team-GPT lets you share your ChatGPT workspace with your team and make decisions together.

With Team-GPT, you can categorize and organize chats in folders. The software also has 100+ ready-to-use prompts and comes with tips to master ChatGPT.

Sign up for the free trial and discover a new way to collaborate with Team-GPT

Related reading:

ChatGPT Use Cases: Learn about the top use cases chosen by our team

Bard vs ChatGPT: Discover the differences between the two to choose the best

ChatGPT Alternatives: Check out the best alternatives picked by our team

The post 5 Best ChatGPT Use Cases in 2025 [Prompts Included] appeared first on Team-GPT.

Especially when you’re operating in a small team, or a 1-person marketing operation, creating marketing strategies and campaigns for all marketing channels can be time-consuming and hard.

In this guide, I’ll walk you through how to create a generalist marketing strategy for your brand using generative AI tools, and then dive deeper into generating a strategy for the different channels.

Before we begin, let’s go over how AI is being used in marketing strategy:

How is AI Being Used in Marketing Strategy Planning

There are different ways of implementing AI into your marketing strategy, such as automating repetitive tasks, making decisions and predictions, and personalizing your content to your customers.

AI can also be used in creating your marketing strategy by building out a marketing plan to save you time and strategize on your behalf.

A digital marketing strategist would usually take into consideration the following considerations:

• The industry you are in.
• Your product and how it solves customers’ problems.
• Your target audience.
• Your desired growth channels (or come up with the growth channels).
• Your current marketing budget.

All of this is information that can be fed into a generative AI platform to provide you with a tailored marketing strategy.

💡 Even though artificial intelligence cannot produce a marketing strategy that is as refined as the ones from experts and consultants, it can serve as a starting point and a major time-saver.

6 AI Use Cases For Generating Marketing Strategies

In this section, I’ll go over the 6 main use cases of AI for generating marketing strategies, covering the different marketing channels.

#1: Generating A Generalist Marketing Strategy

Similar to a digital marketing strategist, artificial intelligence would require plenty of context about your company to generate a tailored strategy.

For this marketing strategy generation, I’ll be using Team-GPT (our tool) which lets you use different AI models alongside your team, such as ChatGPT, Perplexity, and Claude.

➡️ Even though it’s possible to train Team-GPT on your company’s information, for the sake of this article I’ll be manually inserting our organization’s context so you can use my prompts.

You can use my prompt template:

I want you to generate a digital marketing strategy for my [solution], [company name]. We operate in the [industry], and our solution lets you [use cases]. Our target audience is [target audience]. Our marketing budget is [$$$] a month. Ideally, I’d want you to recommend our growth channels and how to approach each marketing platform.

The platform recommended our AI platform, Team-GPT, to focus on the following growth channels:

• Content marketing, developing blogs, case studies, and white papers.
• LinkedIn marketing in both advertising and organic posting, since we’re trying to reach enterprise clients.
• Webinars and virtual events, where we can offer live demos of Team-GPT so prospects can see the platform in action.
• Google Ads to target high-intent keywords related to our AI platform and AI enterprise solutions.
• Other marketing channels, such as email marketing to nurture leads and strategic partnerships with AI influencers.

The platform also gave me KPIs to track, such as website traffic, conversion rate per marketing channel, cost per lead, CPA, and return on ad spend.

#2: Generating A Social Media Strategy

Now that we’ve generated our generalist marketing strategy, it’s time to dive deeper into the different marketing channels.

Here’s my next prompt for generating a social media marketing strategy:

I want you to generate a social media marketing strategy for my [solution], [company name]. We operate in the [industry], and our solution lets you [use cases]. Our target audience is [target audience]. Our marketing budget is [$$$] a month. Ideally, I’d want you to recommend organic growth channels and how to approach each marketing channel.

The platform used my prompted information and the insights from the generalist marketing strategy to recommend me to build a following on:

• LinkedIn, where we should share articles, and insights, and post regularly to build a following.
• Twitter, where we should tweet about AI trends, and industry events, and share infographics and short-form video content.
• YouTube, where should post educational videos around the AI industry, short-form content on how to use Team-GPT, and use case examples.

#3: Generating An SEO Strategy

The generative AI platform will have plenty of information about your brand by now, which means that we can prompt it to use the above-mentioned information.

I’ll now prompt Team-GPT with the following:

Using the above-mentioned information on the marketing strategy for Team-GPT and our target audience and product, I want you to generate an SEO strategy and dive deeper into what we should be publishing, the keywords to be going after, and how to structure our content for best results.

The platform generated me:

• Keywords to go after, including primary and secondary keywords.
• A content creation strategy, which includes how-to guides and industry insights to grow our organic traffic.
• A recommended content structure that includes title tags, headings, internal links, and alt text.
• Technical SEO considerations, such as site speed optimization, and mobile responsiveness.
• A link-building strategy, which includes guest blogging, partnerships, and listing our platform on online directories.

#4: Generating A PPC Strategy

The next step in our marketing strategy is to generate the PPC marketing plan.

Here’s my prompt:

Using the above-mentioned information on the marketing strategy for Team-GPT and our target audience and product, I want you to generate a PPC strategy and dive deeper into how to structure our pay-per-click campaigns, what keywords to go after, and how to approach the copy creation. Our budget is $5,000 per month for this.

The generative AI platform was able to generate me:

• The campaign structure and ad groups, including what keywords to target.
• Example broad match, phrase match, and exact match keywords.
• Ad copy headlines, descriptions, and CTAs.
• A short guide on how to optimize our landing pages.
• KPIs to track and optimize.
• A retargeting strategy and how to segment my audience.

#5: Generating An Email Marketing Strategy

Next up, I’ll prompt the platform to generate an email marketing strategy to nurture the leads of our platform and build a loyal email list.

Prompt: Using the above-mentioned information on the marketing strategy for Team-GPT and our target audience and product, I want you to generate an email marketing strategy and dive deeper into how to nurture our leads with email and how to build a loyal email list.

The platform gave me some pieces of email marketing advice, including:

• Building our email list with lead magnets, such as white papers and e-books on AI adoption strategies and use cases for enterprises.
• Segmenting our email list by industry, engagement level, and stage in their buying journey to improve personalization.
• Overall email content strategy, which includes newsletters, blog summaries, welcome series, and drip campaigns.
• Engagement and loyalty-building tips, such as customer success stories, and surveys to gather feedback and encourage interaction.
• KPIs to track, including open rates, CTRs, conversion rates, and unsubscribe rates.

#6: Generating A Paid Social Strategy

Last but not least, I want to generate our brand a paid social strategy and to point us in the right direction in terms of what advertising channels to use and how to structure our campaigns.

Here’s the prompt:

Using the above-mentioned information on the marketing strategy for Team-GPT and our target audience and product, I want you to generate a paid social marketing strategy and dive deeper into what digital advertising platforms to use, how to structure our campaigns, and what audiences to target for maximum impact. Our budget is $5,000 per month.

The platform was able to generate me:

• The platforms to go after, which include LinkedIn Ads, where we should do sponsored content and display ads.
• The campaign structure, building out awareness, consideration, and conversion campaigns.
• Audience targeting and demographics, including targeting by industry and using LinkedIn’s Matched Audiences to retarget website visitors.
• Ad creative and copy development strategies, that include demonstrating Team-GPT’s features and benefits.
• Tips for budget allocation and KPIs to measure, such as cost-per-lead, CTR, and conversion rate.

Next Steps: Generating Content For Each Channel

After you generated your marketing strategy and created a plan for each growth channel, the next step is to start generating the content.

In this section, I’ll cover how you can generate SEO content, social media posts, and emails using AI.

#1: Generating SEO Content

You can use the generative AI platforms to not only generate the SEO strategy but also execute it.

As AI technology is now capable of browsing the web to conduct in-depth topic research, and structure content in an SEO-friendly format, enterprises can use AI to write SEO-optimized articles.

💡 Tools like Team-GPT let you further improve your content quality and humanize it by creating custom instructions for your preferred AI model (such as ChatGPT).

Here’s how you can write articles in about 3 minutes inside of Team-GPT alongside your team:

Your marketing team can add instructions for brand voice, language, and writing style to make sure that you can scale your content production without having to heavily edit it.

Alternatively, platforms like Jasper AI let you access pre-made writing prompts to write optimized SEO content with integrations with SEO tools like Surfer SEO.

#2: Generating Social Media Posts

As the technology recommended our team to build a presence on LinkedIn, I’m now planning to generate some LinkedIn posts for our page.

First of all, I’d like to generate 10 more LinkedIn posts, since in our AI-generated LinkedIn strategy we only got a few suggestions.

Here’s my prompt:

Generate 10 LinkedIn post ideas around advice on how to utilize AI for digital marketing with generative AI tools.

My target audience is marketing teams at enterprises and I know that they are struggling with scaling content creation and spending much time on mundane tasks instead of focusing on the strategic aspect of digital marketing.

After that, I’ll go and then ask the platform to generate all LinkedIn posts separately and not on 1 go.

Here’s my prompt:

I want you to take into account the writing instructions provided to you and generate this LinkedIn post:

Post Title: Automate to Innovate: Scaling Content Creation with AI

I want you to start with a hook that amazes the readers with a benefit (e.g., ”Did you know that you do not have to spend hours researching for article content?) Then, limit the emoji use to 3 and make it relevant to the post.

After I receive the LinkedIn post, I remove the ‘’hook’’ and ‘’content’’ wording from the LinkedIn post and double-check if the content makes sense.

This process can then be repeated for all of the LinkedIn post ideas that the platform has recommended to us.

#3: Generating Emails

Finally, you can use artificial intelligence to generate emails based on the AI-powered email strategy that you have generated.

In this example, I’ll generate the template for the welcome series that the platform recommended us to start with.

Here’s my prompt:

Generate the template for our email marketing welcome series where we introduce Team-GPT with a series of emails that explain our platform’s features and benefits.

The tool generated a three-part welcome email sequence that contains:

• A welcome email that greets our new customers and provides an overview of our product and key features.
• A 2nd email that goes over the benefits of the product.
• A 3rd email that dives deeper into industry-specific use cases.

My custom version of ChatGPT on Team-GPT also generated a few additional tips, such as how to personalize the emails to our readers, how to incorporate visuals, and how to follow up after the email sequence.

💡 Even though the platform is not as knowledgeable about our platform as I am, this template for a welcome series provides me with a good starting point that will save me time.

What Are The Best Tools For Generating Marketing Plans?

Finally, I want to introduce you to a few platforms that you can use to generate marketing plans.

To evaluate the effectiveness of each platform, I’ll prompt each one of them with the same prompt for strategy creation that I used on my version of ChatGPT.

#1: Team-GPT: Customize a Better Version of ChatGPT

You can use ChatGPT with your marketing team on Team-GPT’s platform and customize it to your needs to generate marketing plans.

After that, use your version of ChatGPT for marketing tasks, such as:

• Generating marketing strategies for the different marketing growth channels.
• Generating social media posts, captions, and copy for carousels.
• Getting social media, email, and article content ideas.
• Generating advertising copy.
• Editing and finalizing posts with Pages and Editing with AI.

Here’s why marketers use our platform for strategy and content generation:

• Use different AI models for ideating on and generating content: You can use the basic or a customized version of ChatGPT, Claude, Perplexity, and DALL-E 3.
• A shared workspace where your marketing team can collaborate in chats and documents in real-time from a single platform.
• Smart AI-powered editing for improved writing, fixed grammar, and refined text.
• Managing your content by turning any chat into a document or starting a new conversation from a Page.

As I mentioned previously, it is possible to create custom instructions for ChatGPT so that the AI models know how to structure your marketing strategy and what content to generate.

#2: Perplexity

Perplexity is a good option for generating marketing plans and content for your brand.

I like the AI platform as it has above-average trainability and is good at generating article content and social media posts.

💡 It is also possible to bring Perplexity on Team-GPT and collaborate with your team on strategy and content generation together.

The advanced AI model integrates advanced search capabilities with generative AI to provide you with research-backed strategic insights.

For example, you can see in the screenshot above how the platform allocated more budget to content marketing (unlike ChatGPT) since it believed content marketing is more vital for establishing authority than strategic partnerships.

#3: Gemini From Google

Gemini from Google is another platform to consider when building out your digital marketing strategy.

Unlike ChatGPT and Perplexity, Gemini recommended me to focus more on paid advertising and on public relations to grow faster in the beginning.

The platform then suggested we use LinkedIn Ads and Google Ads as my 2 main acquisition channels.

Interestingly, Gemini gave me bonus suggestions on how to position our product to our target audience, such as a cost-saver, and a productivity enhancer.

Adopt AI Alongside Your Team On Team-GPT

You can build your marketing strategy and then plan marketing campaigns by building a customized version of ChatGPT alongside your team on Team-GPT.

Our enterprise AI software lets your team generate content by utilizing various AI models like ChatGPT, DALL-E 3, Claude, and Perplexity.

Apart from that, you can access:

• A comprehensive pre-made prompt library to create efficient workflows.
• Detailed usage analytics to track employee engagement.
• Enterprise-grade security ensures data privacy and the ability to host the platform on your servers.

Sign up for a demo of the platform today!

The post Using AI To Build Out A Marketing Strategy: 6 Use Cases & Software appeared first on Team-GPT.

However, the sudden onset of new technology left many companies wondering how to stay ahead of the innovation curve.

This is precisely why I came up with this detailed guide on AI in advertising, where we’ll explore:

1. Key advantages of using AI in advertising.
2. The challenges of successfully implementing AI in advertising and marketing.
3. The best ways of adopting AI in various advertising processes.

Buckle up, and let’s dive in!

What is AI in Advertising?

The simplest definition of AI in advertising is the integration of artificial intelligence technologies into ad campaigns to optimize, automate, and enhance them in various areas.

Namely, AI can help you make ads more relevant to specific audiences by:

1. Analyzing large datasets.
2. Predicting customer behavior.
3. Personalizing content.
4. Improving targeting and bidding.
5. Tracking and optimizing campaign performance.

Due to their specific capabilities, machine learning (ML) and natural language processing (NLP) are the two most common AI technologies used to enhance advertising.

Machine learning, on the one hand, enables AI to predict consumer behaviors, identify patterns, and improve decision-making processes over time, leading to more accurate audience segmentation and ad placements. 

On the other hand, NLP allows AI to understand and generate human-like text, create personalized content, and even respond to customer inquiries through chatbots.

When it comes to the most common use cases for AI in advertising, these include the following:

• Ad automation – AI automates the process of ad buying and placement, ensuring ads are shown to the right audience at the right time, maximizing relevance and return on investment.
• Ad personalization – AI analyzes user data to deliver tailored ads based on individual preferences and browsing history.
• Dynamic pricing and bidding – AI adjusts ad bids in real-time, optimizing ad spend for maximum results.
• Chatbots – AI-powered chatbots enhance customer engagement by providing real-time support and information, improving the user experience.

And now, let’s look at the key benefits of leveraging AI in your advertising campaigns so you can get a good grasp of what AI can help you with.

What Are the Benefits of AI in Advertising?

In case you were wondering whether investing your precious time and money into adopting AI in advertising is worth the trouble, here’s a quick breakdown of some of the key advantages of using AI in advertising campaigns.

1. Improved targeting and personalization:

As mentioned above, AI can analyze massive amounts of consumer data to identify patterns and predict behavior. 

This enables more precise targeting, ensuring ads are shown to the right audience at the right time. 

Personalization is also enhanced, as AI can tailor ads based on individual user preferences, past behavior, and real-time data.

Pro tip: You can leverage AI-powered platforms like Team-GPT to analyze user data and get actionable reports in minutes.

Upload your data and prompt the AI chat to identify KPIs, patterns, and potential anomalies and suggest further actions based on them.

Moreover, you can prompt the AI to act as a specific persona – an advertising specialist in this case – and provide vital insights into your data from that perspective.

Finally, you can also use the platform to create personalized ad copy. 

Describe your target audience – or let AI identify audience segments based on your data – your product and the goal you want to reach, and you’ll get compelling ad copy for any number of audience segments you need.

2. Automation and efficiency

Although automation was possible even before AI, new technologies have made automation better, smarter, and more powerful.

AI-driven advertising ensures ads are delivered in the most effective way with minimal human intervention. It automates the buying, placement, and optimization of digital ads in real-time without requiring constant human oversight. 

Namely, AI uses advanced algorithms to ensure that ads are delivered to the right audience at the right time, on the right platform, and at the best possible price. 

Here’s a quick overlook of what AI can do, how it works, and why it’s so effective:

1. It automates the ad-buying process – AI determines the most cost-effective way to place bids when buying ad space based on predefined parameters like target audience, budget, and campaign goals, giving you a solid competitive advantage.
2. Data-driven targeting – AI ensures that ads are shown to users most likely to engage with the content based on user data, including browsing history, interests, demographics, and online behaviors.
3. Automated optimization – AI continuously monitors ad performance across various channels, platforms, and devices. It adjusts ad placements, bidding strategies, and budget allocations on the fly to optimize for the highest engagement, conversions, or return on investment (ROI)

Automating all these processes greatly reduces manual work and streamlines processes, allowing marketers to focus on strategy and creative aspects rather than tedious, time-consuming tasks that are better left on autopilot.

3. Real-time optimization

AI continuously monitors ad performance and can make adjustments in real-time, optimizing campaigns on the fly. 

Whether it’s adjusting bidding strategies, reallocating budget, or tweaking targeting parameters, AI helps maximize return on investment by responding to live data.

Achieving this without AI would be more than tricky, as no human would be able to detect all these changes the second they happen and react accordingly.

4. Enhanced analytics

Thanks to its ability to quickly and thoroughly analyze vast amounts of data, AI provides deeper insights into campaign performance, using them to offer actionable recommendations.

This way, marketers can gain a clearer understanding of what’s working and where improvements are needed, making data-driven decisions to enhance future campaigns.

5. Cost efficiency

AI helps businesses reduce wasted budget by optimizing ad spend through intelligent bidding strategies and better targeting. 

When ads are shown to the most relevant audiences, the cost per conversion is lower, and the overall return on ad spend (ROAS) is improved.

6. Better customer experience

AI-powered technologies like chatbots and dynamic ad content can enhance customer experiences by offering personalized interactions and real-time responses. 

This improves engagement and customer satisfaction, leading to higher conversion rates and stronger brand loyalty.

7. Scalability

Finally, all these benefits combined mean that advertisers will be able to scale their campaigns much more efficiently.

With the ability to handle vast amounts of data and make rapid adjustments, AI can manage larger, more complex campaigns across multiple platforms without losing performance quality and, most importantly, without shooting your costs through the roof.

How Does AI Enhance Targeting and Personalization?

Ad targeting and personalization are among the biggest advantages of using AI in advertising, so they deserve a separate chapter.

There are several ways in which AI improves these critical areas, including the following:

1. Data-driven audience segmentation

AI enables marketers to move beyond basic demographic data and analyze deeper behavioral patterns, preferences, and intent signals. 

Machine learning algorithms can process huge amounts of customer data from sources such as browsing history, social media activity, purchase behavior, and engagement with previous ads in just seconds. 

This allows AI to create highly granular audience segments based on real user behavior rather than assumptions, leading to more accurate targeting.

2. Predictive analytics for smarter targeting

AI uses predictive analytics to forecast user behavior, helping marketers target individuals most likely to engage with or convert through their ads. 

By analyzing past actions, AI can predict future behaviors, such as whether a user is likely to make a purchase, sign up for a service, or leave a website. This makes it every advertiser’s best friend with psychic powers.

As a result, you’ll be able to direct your efforts toward high-value prospects and optimize ad spend.

3. Dynamic ad personalization

AI enhances personalization by delivering dynamic ads tailored to individual users in real-time.

Namely, AI can quickly generate personalized ad content that reflects each user’s unique preferences by analyzing relevant user data such as previous purchases, location, and interests. 

For example, an online retailer might show a returning visitor an ad featuring products similar to their last purchase, creating a personalized shopping experience that increases the likelihood of conversion.

This will boost your conversion rates and help you upsell and cross-sell related products.

4. Content personalization at scale

With AI, businesses can personalize content for a large audience without manually creating different versions of ads. 

AI tools can automatically generate different ad creatives or messages based on user characteristics like age, gender, location, or browsing behavior. 

For example, Netflix uses AI to create personalized recommendations and custom thumbnails for individual users, ensuring that the content each viewer sees is aligned with their tastes and preferences.

5. Contextual targeting

AI enhances targeting by ensuring ads appear in relevant contexts, not just for the right audiences. 

Through natural language processing (NLP), AI can analyze the content of web pages or videos where ads might be placed and determine if the context aligns with the brand’s messaging or the audience’s interests. 

For instance, an ad for eco-friendly products might be displayed on a sustainability blog, ensuring that it reaches users already engaged with related content.

6. Real-time adjustment and optimization:

AI continuously learns and adapts to user behavior. AI tools can adjust targeting parameters and ad content in real-time to increase relevance as more data is collected about how users interact with ads. 

For example, if a user who has been browsing for home furniture shifts their interest toward home décor, AI can quickly adjust the ad strategy to show décor-related products, ensuring personalization stays up-to-date.

7. Personalized recommendations

AI excels at generating personalized product recommendations that enhance user experiences. 

E-commerce platforms like Amazon and streaming services like Spotify use AI to analyze past behaviors and suggest products, songs, or services that align with the user’s preferences.

These personalized recommendations can be seamlessly integrated into ads, increasing engagement by offering something highly relevant to a specific user.

8. Hyper-localized targeting

AI can incorporate geolocation data to deliver personalized ads relevant to a user’s immediate environment. 

For example, AI-powered platforms can serve localized ads that promote store sales or events happening in a user’s area. 

This not only enhances personalization but also improves ad performance by delivering content that aligns with the user’s location and local context.

9. Customer journey mapping

AI helps marketers understand where individual users are in the customer journey—from awareness to consideration and conversion. 

By tracking users’ interactions with content across channels, AI can deliver personalized messages appropriate to each stage of their journey. 

This ensures that the content resonates with the user’s current needs, boosting the chances of conversion.

What Are the Biggest Challenges of Using AI in Advertising?

While AI offers significant benefits for advertising, there are also some challenges associated with its use, which you should be aware of before applying it more widely.

Here are the top three hurdles marketers and businesses face when implementing AI in advertising based on my experience:

1. Data privacy and ethical concerns

AI in advertising heavily relies on user data to optimize campaigns. 

However, collecting and processing this data can raise privacy issues, especially with increasingly stringent regulations like the GDPR and CCPA. 

Marketers must ensure they comply with these regulations, which often restrict the extent to which personal data can be collected and used.

Moreover, you have to make sure that the data you collect and use doesn’t leak accidentally, which is why you should always use only those AI-driven tools that offer military-grade security.

Failure to handle data responsibly can lead to legal penalties, reputational damage, and loss of customer trust.

2. Complexity and implementation costs

Implementing AI-driven advertising systems can be complex and costly, especially for small businesses or organizations without extensive technical expertise. 

Building or integrating AI tools requires investment in technology, data infrastructure, and skilled personnel who can manage and optimize AI systems. 

Additionally, ongoing maintenance and fine-tuning of AI models to keep them effective adds to the cost. 

For many companies, these barriers can make adopting AI in advertising challenging.

This is why you should focus on AI providers like Team-GPT that:

• Deliver tailored onboarding.
• Provide constant customer support.
• Drive AI adoption across departments by enabling all team members to collaborate on AI-powered projects in real-time.

3. Integrating AI with existing systems

Many companies already have established advertising and marketing workflows that might not be fully compatible with new AI tools. 

Integrating AI systems with existing platforms, such as customer relationship management (CRM) software or ad tech stacks, can be complicated and time-consuming. 

Marketers must ensure that AI seamlessly integrates into their broader marketing ecosystem to avoid disruptions or inefficiencies.

How to Start Implementing AI in Advertising as a Beginner?

I was in your shoes once, so I know from experience how difficult it is to start adopting a completely new technology into your area of expertise.

As a seasoned AI user, I’d like to share some of the tested tricks and tips I’d like someone had offered me when I was just embarking on my AI journey:

1. Start small and scale gradually

Don’t try to implement AI across all your campaigns at once. 

Start with one or two areas where AI can make an immediate impact, such as automating bidding through programmatic advertising or personalizing ad copy for specific audience segments. 

As you gain confidence and see positive results, you can expand AI usage into other areas, such as audience targeting, creative generation, or predictive analytics.

2. Focus on data quality

AI is only as good as the data you feed it. 

Make sure you have clean, accurate, and comprehensive data from various sources, such as your website analytics, CRM, social media, and past advertising campaigns. 

High-quality data will lead to better insights, targeting, and optimizations. If your data is incomplete or inaccurate, AI will not deliver the results you expect.

3. Choose the right tools for your needs

There are plenty of AI-driven advertising tools available, and not all of them are suitable for every business. 

Take the time to research and select the right tools for your specific goals, whether you’re automating ad placements, creating personalized ads, or using AI to analyze performance. 

Tools like Google’s AI-driven features for Google Ads, Facebook’s AI-based audience targeting, or third-party platforms like Adext or Albert.ai can be great starting points.

4. Test continuously

AI excels in testing and optimization. 

Set up A/B tests for your ads and use AI to analyze the performance. Continuously iterate based on the data and insights AI provides. 

Over time, you’ll be able to fine-tune your campaigns for better results, such as higher click-through rates, conversion rates, and return on ad spend (ROAS).

5. Closely monitor AI performance 

AI automates many processes, but keeping a close eye on performance is crucial. 

Don’t set it and forget it. Regularly check the results, and if something seems off (e.g., ads being shown to irrelevant audiences), step in to adjust parameters or refine the data. 

AI learns over time, but human oversight ensures that it stays on track and aligns with your overall goals.

6. Don’t overlook creativity

While AI excels at automation and data-driven decisions, creativity still plays a crucial role in advertising. 

Use AI to handle repetitive tasks, data analysis, and optimization, but leave room for creative input in your ad messaging and visuals. 

AI can help test variations, but the creative concept should always resonate with your audience on an emotional level.

You can combine AI with human marketers to strike that perfect balance between efficiency and uniqueness for optimal results.

In Team-GPT, your entire team can collaborate on all aspects of building and realizing your marketing strategy, from brainstorming campaign ideas to tweaking killer ad copy and more.

7. Stay updated with AI advancements

AI is constantly evolving, and new tools and features are regularly introduced. 

Keep yourself updated on the latest trends, tools, and best practices in AI advertising. 

Follow industry blogs, attend webinars, and network with other professionals to ensure you use the most effective techniques and stay ahead of the competition.

Good Examples of AI in Advertising

There is no better way to grasp the full power of AI in advertising than by looking at some real-life examples of brands leveraging AI to enhance their advertising campaigns.

Here are my top three picks.

1. Pomelo Fashion leveraging AI to personalize product recommendations

Pomelo Fashion, a South Asian global ecommerce brand, was looking to improve its personalization efforts to win over more customers and boost upselling and cross-selling.

ML algorithms proved key to this, enabling them to provide real-time, hyper-personalized product recommendations to customers based on their past and present buyer decisions.

The brand used Amazon Personalize, part of the Amazon Web Services suite, to achieve this, and the results it got were worth the effort, including:

1. Increased return on investment by 400% within 1 month.
2. Increased gross revenue from category pages by up to 15%.
3. Boosted click-through rate from category to product pages by up to 18%.
4. Increased add-to-cart clicks from the category page by up to 16.

2. JP Morgan used generative AI to create compelling ad copy

Interestingly, JP Morgan was one of the earliest adopters of AI, starting its AI journey way back in 2019.

The company used generative AI to create personalized ad copy that was bound to resonate with specific audience segments.

The results?

A 450% increase in its click-through rate, leading to more conversions and better brand engagement across levels.

3. Nutella played with AI to create quirky and unique jar labels

I decided to include one non-digital business here to show you that AI can be used by a much wider range of businesses than you’d expect.

Nutella used AI to generate 7 million unique jar labels where no two jars were identical.

The brand experienced a massive boost in sales, with every single jar sold in record time.

This is a perfect example of how AI can be used for more creative aspects of advertising rather than just automation or optimization.

Start Leveraging AI in Advertising Today with Team-GPT

Starting to use AI in advertising as a beginner is not easy, but it’s not as difficult as it may initially seem – and I sure hope this guide showed you that it’s absolutely doable.

Team-GPT can help you start your journey as a newbie in the world of AI in advertising, as it enables your entire marketing and advertising team to:

• Collaborate on creating and executing marketing campaigns while leveraging the power of any AI model you may need.
• Generate and edit highly personalized ad copy.
• Create and save custom prompts specialized to your unique brand and use cases.

Book a demo with our team and find out how Team-GPT can help you harness the full power of AI to enhance your advertising efforts across levels.

The post AI in Advertising: Beginners Guide [2025] appeared first on Team-GPT.

I was there too not too long ago – until I found how to use AI to generate (quality) LinkedIn posts without having to spend hours writing them up from scratch.

In this article, I’ll walk you through my prompts for idea generation, generating LinkedIn posts without sacrificing quality, and some of my best practices for creating posts that get engagement.

Before we begin, let’s go over what makes a good post on LinkedIn. 👇

What Makes a Good LinkedIn Post?

Posts on LinkedIn that usually get engagement are posts that:

• Have a hook that makes people want to read more about the post.
• Provide interesting, unique, or informative content that intrigues or educates the readers.
• Has a personal touch to it – through a story or previous successes.
• Makes readers engage with the content by asking questions or being clear on what the next action should be.
• (Bonus) Featuring a good creative that interests people when they are scrolling, such as a video or a carousel.

Even though it will be up to you as the writer to add a personal touch to the LinkedIn posts, artificial intelligence is still capable of automating the bigger part of this process.

➡️ This will let you focus on the strategic part of your LinkedIn profile, such as:

• Measuring results from the content.
• Researching interesting topics that will solve your target audience’s problems.
• Writing personal stories and working on mini case studies to build authority.

How To Create LinkedIn Posts With AI: Step-By-Step

Now that we discussed what makes a good LinkedIn post, I’ll walk you through my process of generating LinkedIn posts that generate engagement. 👇

Step #1: Generate LinkedIn Post Ideas

The first step to generating engaging LinkedIn posts is to come up with the post ideas in the first place.

If you’ve got no problem coming up with content ideas for LinkedIn you can skip this step.

Our goal is going to be to generate 10 post ideas for our niche and target audience and we are going to explain in great detail who we are planning to write for.

Here’s my prompt inside Team-GPT (our platform) that lets you collaborate with your team on ChatGPT:

Generate 10 LinkedIn post ideas around advice on how to utilize AI for digital marketing with generative AI tools.

My target audience is marketing teams at enterprises and I know that they are struggling with scaling content creation and spending much time on mundane tasks instead of focusing on the strategic aspect of digital marketing.

Step #2: Feed Your Writing Style To The Algorithm

Now that we’ve got 10 LinkedIn post ideas for our target audience, we’ll need to feed our writing style into the algorithm.

Otherwise what’s going to happen is that the generative AI tool you’re using will write a generic (often robotic) LinkedIn post.

With social media post generators like Team-GPT, you can add custom instructions to the generative AI platform of your choice (e.g., ChatGPT or Claude).

To do that, you have to:

• Click on ‘’Custom instructions’’ on the bottom right.
• Then click on ‘’Create instructions’’.
• Choose ‘’WritingStyle’’ from the dropdown menu and write your custom instructions.
• Equip the custom instructions whenever you are creating LinkedIn posts.

Step #3: Generate Each LinkedIn Post

Now that we’ve got our content ideas and writing style set in place, we need to start generating some quality LinkedIn content.

Important: You’ll want to generate all LinkedIn posts separately and not in 1 go. 

Do not ask the platform to generate 10 LinkedIn posts at the same time, because the output wouldn’t be as good.

Here’s the prompt I’m using:

I want you to take into account the writing instructions provided to you and generate this LinkedIn post:

Post Title: Automate to Innovate: Scaling Content Creation with AI

Content: Discuss how generative AI can help marketing teams produce large volumes of content quickly, freeing up time for strategic planning. Share tips on integrating AI tools into existing workflows for maximal efficiency.

I want you to start off with a hook that amazes the readers with a benefit (e.g., ”Did you know that you do not have to spend hours researching for article content?) Then, limit the emoji use to 3 and make it relevant to the post.

After that, I’d remove the ‘’hook’’ and ‘’content’’ wording from the LinkedIn post and double-check if the content makes sense.

This process can then be repeated for all of the 10 LinkedIn post ideas.

(Bonus) Step #4: Create Carousels

As LinkedIn carousels have been getting plenty of engagement and traction in the social media platform, I’ve also incorporated carousels into our LinkedIn strategy.

Here’s how you can generate a carousel copy for your LinkedIn posts:

Prompt: I want you to re-generate me the previous LinkedIn post but I want you to make it in carousel format. Split the content into multiple bite-sized pieces of information that I can discuss over a few slides.

The generative AI platform separated the informative LinkedIn post into smaller chunks of information that I can use in my slides.

💡 To speed up that process, you can create a template inside a tool like Canva with your branding so you can just input the bite-sized pieces of content into it for faster publishing.

Best Practices For Creating Effective LinkedIn Posts With AI

There are a few best practices to keep in mind when trying to build engaging LinkedIn posts with AI:

#1: Input A Writing Style

You can generate LinkedIn posts that adhere to your writing style and brand guidelines so you can scale your content production without the heavy editing required.

I believe that true content creation scaling comes with inputting a custom writing style so you can replicate your writing across multiple content pieces.

You can add your own or someone else’s writing style by:

• Prompting the writing style of a specific person to a generative AI platform manually, such as ‘’write like Brian Dean’’.
• Prompting the writing style of your brand with words, such as ‘’write in a professional but playful manner.’’
• Customizing the generative AI platform to your needs by using a tool like Team-GPT that lets you add instructions and essentially train your version of ChatGPT.

➡️ You can take it a step further and build a custom LLM model that writes exactly like you, and then bring it to Team-GPT to collaborate with your team.

For example, here are our custom instructions when generating content briefs.

#2: Master The Hook

The ‘’hook’’ in LinkedIn language is the first sentence that you are putting out on your posts.

The goal of the hook is to interest your readers and to stop them from scrolling down to check out your post.

Here’s an example hook from Ben Goodey who intrigues the readers by claiming that even though he’s not very interested in AI technology, he still believes that AI tools can save you time and improve your content.

So potential readers start wondering – what did this person find out about AI that I can use in my workflows?

#3: Enrich With a Personal Story

I believe in personal stories to enable me to truly connect with my audience on LinkedIn.

You can enrich your AI-generated educational LinkedIn posts with a personal story to improve your posts’ engagement.

An example of a personal story that drives engagement is from Jessica R that starts off by saying that she deleted ChatGPT.

The entire post is a story, which is why I doubt she used any AI to write it, but it’s a good example of using stories to improve your engagement because they are relatable.

#4: Proof-Read The Posts

Last but not least, you should always proofread the posts that you are generating with AI technology and I cannot stress that enough.

Even though artificial intelligence can save you time and money from expensive copywriters, you’ll still need to have the final look at the output.

When you’re creating customer-facing content, such as in advertising or on social media, it’s a must to double-check if all the content makes sense.

Best AI Tools To Generate LinkedIn Posts

Here are some of the best generative AI tools to help you generate LinkedIn posts that drive engagement in 2024.

#1: Team-GPT: Customize a Better Version of ChatGPT For LinkedIn

You can use ChatGPT with your marketing team on Team-GPT’s platform and customize it to your needs for LinkedIn

After that, use your version of ChatGPT for LinkedIn and social media tasks, such as:

• Generating LinkedIn posts, captions, and copy for carousels.
• Getting LinkedIn post ideas.
• Generating creatives with DALL-E 3.
• Editing and finalizing posts with Pages and Editing with AI.
• Generating advertising copy for LinkedIn Ads if you decide to go that route.

Here’s why social media marketers use our platform for LinkedIn content generation:

• Use different AI models for generating LinkedIn content: You can use the basic or a customized version of ChatGPT, Claude, Perplexity, and DALL-E 3.
• A shared workspace where your team can collaborate in chats and documents in real-time from a single platform.
• Smart AI-powered editing for improved writing, fixed grammar, and refined text.
• Managing your content by turning any chat into a document or starting a new conversation from a Page.

You can create custom instructions for ChatGPT and other writing tools so that the AI models know how to structure the LinkedIn posts and what tone to use. 

You can add instructions for brand voice, language, and writing style to scale your LinkedIn content production without heavily editing it.

Feature #2: Generate Images With DALL-E 3

You can integrate DALL-E 3 in Team-GPT to generate on-brand image visuals for your LinkedIn posts.

Introducing Dall-E 3 in Team-GPT | Team-GPT

Similar to the way our integrations with ChatGPT and other AI tools work, you can connect DALL-E 3’s API.

It is possible to generate images by inputting your image descriptions and then selecting the quality and size you need.

Pro Tip: If you double-click on the ‘’Prompt’’, our platform will generate an enhanced prompt where the model adds more context to the initial prompt before generating the image.

You can also download and/or re-generate if you are not satisfied with the outcome.

#2: Perplexity

Perplexity is ideal for generating research-backed LinkedIn posts for your brand.

I personally like the AI platform as it has above-average trainability and is good at generating emojis and hashtags for LinkedIn posts.

The advanced AI model integrates advanced search capabilities with generative AI to provide you with research-backed LinkedIn posts.

For example, you can see in the screenshot above how the platform found features about the product I asked the platform to generate a LinkedIn post for.

What’s more, Perplexity is also capable of generating content and imagery since it can access multiple LLMs.

#3: Copy AI

Copy AI is one of the more popular AI content creation tools for social media content creation I came across in my research.

The platform is a good AI for generating social media copy for any marketing channel, but LinkedIn posts are where I felt like the platform could be at its best.

There is a pre-made AI-powered LinkedIn post generator that lets you use different marketing frameworks, such as AIDA, PAS, and BISCUIT.

The platform is ideal for marketing teams looking to scale their LinkedIn content production by inputting their topic and choosing a psychological framework to improve their engagement rate.

There are additional LinkedIn-related templates available from Copy AI, such as their free LinkedIn headline generator.

Generate On-Brand LinkedIn Posts With Team-GPT Alongside Your Team

You can generate on-brand LinkedIn posts alongside your marketing team by building a customized version of ChatGPT inside Team-GPT.

Our enterprise AI platform lets your team generate LinkedIn copy, post ideas, and hashtags by utilizing various AI models like ChatGPT, Claude, and Perplexity.

Apart from that, you can access:

• A comprehensive pre-made prompt library to create efficient workflows.
• Detailed usage analytics to track employee engagement.
• Enterprise-grade security ensures data privacy and the ability to host the platform on your servers.

Sign up for a demo of the platform today!

The post How To Use AI To Generate LinkedIn Posts: Prompts, Best Practices & Tools appeared first on Team-GPT.

I was in that spot not a long time ago – I was running the content operation at Team-GPT and I spent 1-2 hours a week building out SEO-friendly content briefs for our writers.

But then I decided to automate that process with AI while not sacrificing quality – and I’ll show you how you can do it too.

In this guide, I’ll first go over what makes a good content brief for SEO, the best practices for creating effective SEO briefs, and then a step-by-step guide on how to create briefs with AI.

What Makes A Good Content Brief For SEO?

A good content brief for SEO has:

• SEO-friendly heading structure – H2s, H3s, and H4s.
• Semantic keywords for NLP optimization.
• Estimated word count for your writers to aim for.
• Explanation of how to approach each section of the article.
• (Bonus) Insight into how to position your product or service in the context of the article.

Artificial intelligence may not be able to automate all of these steps for you, but it can save you time from 90% of this process.

➡️ This will allow you to focus on the strategic aspect of creating content briefs – the angle of the article for your brand.

How To Create Content Briefs For SEO With AI: Step-By-Step

In this section, I’ll walk you through my process of building out content briefs for our content creation team. 👇

Step #1: Keyword Research

The first step to creating a content brief is getting your keyword research process in place.

This includes the identification of:

• The target keyword – for example ‘’how to do keyword research’’.
• Secondary keywords – for example ‘’common mistakes when doing keyword research’’.
• LSI keywords – for example ‘’SEO software’’.

You can use AI for keyword research to save time and money from expensive SEO tools.

Here’s my prompt: 

I want you to generate 10 secondary keywords and 10 LSI keywords for NLP optimization on the topic of ”how to do keyword research”

I’ll need the secondary keywords when generating the heading structure, and the LSI keywords when trying to optimize for natural language processing (NLP).

Step #2: Generate The Article Outline

The 2nd step in the process is to prompt an AI platform like Team-GPT to generate the article outline with the proper heading structure.

Team-GPT allows you to use your preferred AI model, such as ChatGPT and Claude, and collaborate with your team.

➡️ That will help you invite your writers to view the optimized content briefs when we are finished with them.

Here’s my prompt: 

I want you to take these keywords and build an SEO-optimized heading structure (H2, H3, and H4) and an outline for the topic ”how to do keyword research”. You can use the secondary keywords as part of the headers.

One of the headers should be how our users can do keyword research with Team-GPT, our AI platform that lets you use advanced AI models like ChatGPT and Perplexity.

You can take this a step further and prompt the tool to do competitor research when researching for the content brief.

This will help you cross-reference your heading structure and article outline with what is already ranking well on Google.

Now we’ve got the skeleton of our article but that’s not quite ready yet to hand to our writers.

Step #3: Enrich The Content Brief

The goal is to make it writer-friendly, even if your writers are not experts on the subject.

Here’s my prompt to enrich that heading structure:

I want you to enrich the content brief with a recommended word length and then give explanations on how to approach all of the headers to my writers as if they are new to the topic. That should include the key talking points of each header. I need you to include the target word length for all headers and sub-headers.

Additionally, I want you to include NLP keywords under each header, as well as an example of an NLP-optimized sentence. I also want you to include relevant resources and reading materials for my writers on all complex topics.

The content brief now has:

• Target word length for the article and each section.
• An SEO-optimized heading structure.
• Key talking points under each header, alongside learning resources.
• NLP keywords alongside example NLP-optimized sentences.

Now all that is left for me is to go under the section of ‘’How to Use Team-GPT for Keyword Research’’ and manually insert instructions for my writers.

This has allowed me to scale our team’s article production without having to do all of the keyword research, heading creation, and ‘’key talking points’’ explanations for hours.

(Bonus) Step #4: Feed Your Writing Style To The Algorithm

There are different SEO methodologies stemming from different schools of thought on what are the best SEO writing practices.

If you want to scale your SEO content brief production, you can feed all of that information to your AI platform provider to save time having to explain your SEO writing style to your writers.

For example, at Team-GPT we do not include internal or external links early on in the article.

💡 I’ve found that if I feed all of these ‘’rules’’ into my AI tool, it will duplicate them across all the content briefs for all writers.

For that, I use Team-GPT’s custom instructions to include our content brief writing style rules so they can be duplicated.

Here’s an example output of the beginning of a content brief with our custom rules:

Best Practices For Creating Effective SEO Content Briefs With AI

Now that I’ve spent quite some time generating SEO-optimized content briefs with artificial intelligence, there are a few best practices to keep in mind:

#1: Prompt The AI Tool Multiple Times

When it comes to generating content briefs for writers, the best output does not come from a single prompt.

It’s better to take the process slowly and build a multi-step process of prompting the tools for better output in the end.

I like to first use AI for keyword research purposes to allow myself to figure out what secondary keywords I want to go after, and then prompt the tool to build me an outline with headers.

Finally, when I have decided on the final heading structure, I prompt the AI platform to provide me with the key talking points, target word length, and learning resources for my writers.

Here’s the output if I prompt the tool to do all of the above tasks all at once:

Even though there was a content brief being created, I did not like the level of depth and detail that a content brief should have.

#2: Use Custom Instructions

Custom instructions allow you to spend less time prompting the tool with all of your company’s information regarding writing style and branding.

That enables you to focus on the strategic aspect of the content briefs, instead of having to prompt it all every time.

Here’s how you can do it inside Team-GPT:

• Click on ‘’Custom instructions’’, which sits under ‘’Send’’ when prompting.
• Click on ‘’Create instruction’’. Here you can also browse from your existing ones.
• Choose the type of instruction you want and start writing your pre-made prompt. We need ‘’WritingStyle’’ in this case.

#3: Explain The Key Talking Points Under Each Header

Another core aspect of the content briefs for writers is being able to explain to them what the key talking points should be.

This is why in our content brief creation process we prompt the AI platforms to point out the key talking points under each header.

This is especially important when you are allocating writing tasks to writers who are not experts in the topic and need additional context for each header.

#4: Add Semantic & NLP Keywords

A big part of optimizing an article for SEO is the semantic and NLP keywords that we are using to signal to the search engines what our content is about.

When I was manually creating these content briefs, I’d sift through keyword research tools to dig out secondary keywords to include in my content briefs.

With AI technology, the tools can analyze the SERPs to find what semantic and NLP keywords you need to use to rank for a specific topic.

There are AI tools for SEO like Surfer SEO that let you get that competitor-driven data.

You can then prompt your AI tool to include the secondary and NLP keywords in your content briefs so your writers can know to include them.

I also like prompting the tool to add an NLP-optimized example of a sentence that has included one of the secondary keywords.

#5: Always Have A Final Look

Last but not least, before sending out your content briefs to your writers, it always deserves a final look.

Similar to how you wouldn’t want to blindly trust AI-generated content (no matter how good and on-brand it may be), it’s worth it to have a final look before the send-off.

This is why I have a final read-through of any content briefs generated with the help of AI to make sure that the heading structure makes sense for that topic.

I also make sure that our content briefs have the right positioning of our product.

Best AI Tools To Generate Content Briefs For SEO

Here are the best AI tools that you can use to generate SEO-optimized content briefs based on our experience:

#1: Team-GPT: Customize a Better Version of ChatGPT

Team-GPT lets you use AI models like ChatGPT, Perplexity, and Claude alongside your team and customize it to your needs.

You can use your version of ChatGPT for content brief-related tasks, such as:

• Generating keyword ideas.
• Creating the SEO-optimized article outline with the heading structure.
• Editing and finalizing content brief drafts with Pages and Editing with AI.
• Enriching the article outline with learning resources and key talking points.

You can also speed up your content brief-creating process with our pre-made prompts, use cases, and editable AI Pages.

Here’s why content marketing teams love our platform:

• A shared workspace where your team can collaborate in chats and documents in real-time from a single platform.
• Smart AI-powered editing for improved writing, fixed grammar, and refined text of your content briefs.
• Organized and shared content to ensure quick access to content briefs for all your writers.
• Managing your content by turning any chat into a document or starting a new conversation from a Page.

It is possible to create custom instructions for ChatGPT and other writing tools so that the AI models know how to structure the content brief and what SEO best practices to follow.

You can add instructions for SEO guidelines to scale your content brief production without heavily editing it.

#2: Surfer SEO

Surfer SEO lets you create SEO-optimized content briefs with a suggested heading structure and LSI keywords.

The platform uses competitor data to make recommendations on how you should approach your content to rank for your target keywords.

You can use Surfer SEO to not only generate content briefs but also article content that has a SERP-based tone of voice.

➡️ One of the best aspects of Surfer SEO is that you can generate links for your writers where they can input the finalized article and then get a score from 0-100 on how optimized it is.

#3: Perplexity

Perplexity, similar to Claude and ChatGPT, lets you generate content briefs from prompts.

I’ve found the tool to be particularly useful when you want it to scrap the internet and research your competitors.

The AI platform can also be used for keyword research purposes, which means that you can build competitor-driven content briefs all from the platform.

One of the downsides of Perplexity is that the platform is not very good at following instructions, and when I prompted the platform to provide my writers with the key talking points, it did not go in the right direction.

Generate On-Brand Content Briefs With Team-GPT Alongside Your Team

You can generate on-brand content briefs alongside your team and writers by building a customized version of ChatGPT on Team-GPT.

Our enterprise AI software lets your team generate keyword ideas, article outlines, and entire content briefs by utilizing various AI models like ChatGPT, Claude, and Perplexity.

Apart from that, you can access:

• A comprehensive pre-made prompt library to create efficient workflows.
• Detailed usage analytics to track employee engagement.
• Enterprise-grade security ensures data privacy and the ability to host the platform on your servers.

Sign up for a demo of the platform today!

The post How To Use AI For Content Brief Creation: Prompts, Best Practices & Software appeared first on Team-GPT.

As AI technology has become more mature with time, generative AI tools can now produce more realistic imagery.

In this guide, I’ll walk you through 5 use cases of AI for generating images, a few examples of brands who found unique ways of using the technology for creative purposes, and tools that you can use.

TL;DR

• The main benefits of using AI for image generation are the ability to save time, save money, and scale your creative production.
• The most popular use cases of AI in image generation are generating advertising creatives, editing images, generating blog and social media thumbnails, and creating product images.
• An example of a company that used AI for creative purposes is Edelman who utilized the technology to ideate on new products.
• Some of the best AI tools for image generation include Vue AI, Midjourney, and Team-GPT due to their ability to deal with complex requests.

How Is AI Being Used For Image Generation?

Here are a few of the ways that AI is being used for image generation:

• Generating images for commercial purposes, such as marketing campaigns.
• Editing images, such as cropping and resizing.
• Ideating on new product ideas.

The technology utilizes a machine-learning model that scans millions (if not billions) of images and creatives published on the internet.

The way it works is that the technology tries to identify trends between the different images by interpreting the text associated with them and tries to string together what ‘’mouse’’ and ‘’bear’’ mean and what they look like.

➡️ As an algorithm, AI requires a large database to learn from, which is why over time the technology is going to get even better.

What Are The Benefits Of Using AI For Image Generation

The main benefits of generating images with AI include saving time from manual design and editing, saving money from professional work, and scaling creative production.

Let’s go over each one of them👇in more detail. 

#1: Save Time

The biggest benefit of AI technology is its ability to save you time from having to manually design and edit images.

AI’s large database can generate realistic images from short or detailed prompts.

#2: Save Money

The second biggest benefit is generative AI’s ability to save you money from having to:

• Hire a professional designer.
• Pay for premium stock mages.
• Hire a photographer and rent a studio for a photoshoot.

#3: Scale Creative Production

Lastly, the image generation capabilities of AI can be scaled across your entire marketing or product operations.

Assigning a designer 10 tasks might take a week, but with AI, you can generate 10 creatives in minutes.

Additionally, content creation tools like Team-GPT (that’s us) enable your brand to collaborate with your team on image generation together.

You can prompt the platform together, build a prompt library, or access pre-made prompts for image generation.

The ability to save time, and money and generate images at scale are the reasons why people of all professions have adopted the technology.

In fact, there have been more than 15 billion images generated using text-to-image algorithms from 2022 to 2023 alone.

5 Use Cases of AI For Generating Images

Here are the 5 best use cases of AI for image generation from the ones I’ve seen work best in the industry:

#1: Generating Advertising Creatives

The best use case of AI for image generation is its ability to generate marketing collateral for advertising purposes.

Here’s an example from Team-GPT’s DALL-E 3: 

Prompt: An eye-catching marketing graphic for an environmentally conscious shoe brand’s green initiative. The central focus of the piece is an elegantly designed athletic sneaker, noticeably fashioned from repurposed materials, symbolizing energy efficiency and reduction in waste. Remember, this image is to be devoid of any textual elements, purely graphical.

Even though some of the images will have issues with wording, advertisers can prompt the platform multiple times to get it correct.

Alternatively, you can prompt the generative AI tools to not use wording at all (which is what I’m doing).

#2: Editing Images

Apart from its generative use cases, AI is also capable of editing images, such as cropping, resizing, filtering, and enhancing them.

Instead of manually editing your images to perfection, AI can help you do that with a simple click or a prompt.

For example, Samsung’s new flagship phone, the S24 Ultra, lets you edit the images in your phone with AI.

#3: Create Product Images (Save on Photoshoots)

AI is capable of creating product images of AI-generated models using your physical products.

Companies across the world are now able to save on photoshoots (e.g., hiring a model and a photographer) with tools like Vue AI that help your shoppers visualize what a product would look like on a person.

Here’s an example of a brand inserting a bag and then receiving an AI-generated female model holding the bag.

#4: Generating Blog & Social Media Thumbnails

Another use case of AI in image generation is its ability to generate thumbnails for your blog and social media channels.

Instead of using royalty-free stock images (or paying a designer), you can use generative AI tools like Team-GPT and Canva, where you can generate blog banners and creatives for social media posts in a matter of seconds.

Here’s an example from Team-GPT’s DALL-E 3:

Prompt: Generate a blog thumbnail for the title ‘’How to use AI to generate images for blog thumbnails’’. Do not use text on the image. Only on the graphic.

Once again, I’d recommend instructing the platform you’re using to generate only the graphic without using text on the creative to avoid irregular wording.

#5: New Product Ideation

Last but not least, you can use AI tools for new product ideation.

When you have a product idea for a physical product, such as a new flavour, a new bottle, new packaging, or a new clothing line – you can ask AI to help you put your vision into a product concept.

Here’s an example of me using Team-GPT’s DALLE-3 to generate a new concept Coca-Cola bottle for the American market.

Prompt: ‘’Generate me a new variation of Coca-Cola that you believe would be very successful in the USA.’’

➡️ Keep reading to find out how Edelman is ideating on new products with the help of DALL-3 and ChatGPT.

5 Examples of Companies Using AI For Image Generation

Here are a few examples of organizations (creatively) using artificial intelligence for image generation that I was able to find:

#1: BMW

BMW painted their Series 8 car model for an advertising campaign with the help of artificial intelligence.

The car manufacturer partnered with Goodby, Silverstein & Partners to create an advertising campaign in 2021 for their 8 Series Gran Coupe where they projected AI-generated art onto the cars.

#2: Nutella

Nutella used generative AI technology to create unique jar labels.

The chocolate giant created 7 million unique jars of Nutella that have AI-generated labels and promoted them in an advertising campaign.

The results? All of them sold out.

#3: Tommy Hilfiger

Tommy Hilfiger created AI design content in 2023 as a part of the Metaverse Fashion Week.

The goal of the contestants was to generate a designer product in the classic Hilfiger style.

The images that you see above are the winners selected by the brand, which were then made into a digital wearable and available for purchase on the virtual fashion platform DressX.

#4: UnderArmour

UnderArmour started using AI to train the brand’s ‘’visual DNA’’ in an AI tool, feeding their clothing style to the algorithm.

The organization was then able to generate realistic new product and model images, saving money and time from photoshoots.

UnderArmour is then able to use these creatives across its website and marketing channels.

#5: Edelman

Edelman uses AI for product idea generation. 

The brand worked with DALL-3 and ChatGPT to ideate new physical products to expand its product offering.

Instead of replacing their designers, Edelman simply used these AI-generated products as a starting point to conceptualize new products.

What Are The Best AI Tools For Image Generation?

When evaluating which AI tools to include in this list, I looked at their image quality output, trainability, and cost structure.

Our team even published a video not long ago on the topic: 👇

Explore the Pioneers of AI Image-Generation

Here are the best AI for image generation on the market in 2024:

#1: Team-GPT: Generate Images With DALL-E 3 With Custom Instructions

Team-GPT is a collaborative AI platform that lets you access different AI models, such as ChatGPT, Perplexity, and Claude and adopt AI alongside your team.

You can integrate DALL-E 3 in Team-GPT to generate on-brand image visuals, product concepts, and advertising materials.

Introducing Dall-E 3 in Team-GPT | Team-GPT

Similar to the way our integrations with ChatGPT work, you can connect DALL-E 3’s API with our tool.

The way it works is that you can generate images by inputting your image descriptions and then selecting the quality and size you need.

We’ve also added an interesting feature to our platform.

If you double-click on the ‘’Prompt’’, the tool will generate an enhanced prompt where the model adds more context to the initial prompt before generating the image.

It is also possible to download and/or re-generate if you are not satisfied with the outcome.

All of the images that you and your team have generated will then be stored, where they can be downloaded or deleted.

#2: Vue AI

Vue AI enables e-commerce businesses to generate high-quality on-model product photos to save time and money on photoshoots.

The platform lets you showcase your fashion products on models to improve your store’s engagement and conversions.

➡️ The brand also offers an AI-powered virtual dressing room, where your consumers can test different products on your models, and be able to select the model.

I thought of this as a useful feature for brands that want to visualize and style different product outfits on relatable models that look similar to them.

💡 Such automated on-model fashion imagery enables retailers to save up to 75% of photoshoot costs, according to the brand.

#3: Midjourney

Midjourney is one of the most popular AI image generators that has gained popularity with its high-resolution images.

The AI tool has above-average customization capabilities, as it lets you select the artistic style, color tone, and level of realism.

This is the reason why the platform allegedly has over 16 million users and over 40 million monthly active users.

Here’s an image I was able to generate when I prompted Midjourney to create an advertising creative for Coca-Cola without text:

The platform starts from as little as $8/month on their Basic plan and lets you get access to around ~200 generated images per month.

You’ll also get access to the tool’s member gallery and the ability to top up your credits to generate more images.

#4: Jasper Art

Jasper Art, a part of Jasper AI, lets you generate and edit professional-looking images for marketing purposes.

The AI tool stands out by allowing you to train the AI platform on your brand’s guidelines to ensure that your creatives are on-brand.

The platform offers a comprehensive range of features, such as:

• Customizable AI templates to help you get started.
• A content filter for sensitive material.
• The ability to remove text from imagery, remove background, and ‘’reimagine’’ images by changing their concept.

#5: Canva

Canva offers a free AI image generator that lets you generate high-resolution images from prompts.

Also known as Magic Media, the tool can generate images in different image formats, such as watercolor, realistic, and retro designs.

Users can modify colors, be able to tap into diverse art styles, and then use Canva’s editing capabilities to further customize the images.

➡️ I’ve noticed that this can be a good option for social media marketers because Canva has a direct publishing option where you can generate images and then publish them to your social media with your copy.

Next Steps: Generate Quality Images With AI Alongside Your Team on Team-GPT

You can generate quality images with DALL-3 on Team-GPT and feed the platform your custom instructions about your brand.

Our enterprise AI software lets you collaborate with your team members instead of everyone in your team working out of different platforms.

Apart from that, you can access:

• A comprehensive pre-made prompt library to create efficient workflows.
• Detailed usage analytics to track employee engagement.
• Enterprise-grade security ensures data privacy and the ability to host the platform on your servers.

Sign up for a demo of the platform today!

The post Using AI For Generating Images: 5 Use Cases, Examples & Tools appeared first on Team-GPT.

But how is AI being used in knowledge management exactly and what are its best use cases?

In this article, I’ll cover everything that you need to know about how AI can help you make better use of your organizational knowledge and resources.

TL;DR

• The biggest benefits of using AI for knowledge management include easier knowledge sharing, a personalized user experience, and being able to save time.
• Some of the best use cases of AI for knowledge management include setting up an internal search and retrieval system, content creation with a Custom LLM model, and issue monitoring.
• Some of the best AI tools for knowledge management on the market include Team-GPT, Shelf and Document360 due to their trainability.

How Is AI Being Used In Knowledge Management?

The way AI is being used in knowledge management is by enhancing the efficiency of information retrieval and personalization of responses by analyzing large amounts of unstructured data.

The technology is being integrated into your existing knowledge management systems to help you retrieve specific information whenever you need it and generate on-brand text.

From enhanced internal search capabilities to automated content tagging, brands have been training custom AI models and knowledge bases with their internal data.

That enables you to manage your organization’s knowledge and to automate the retrieval process within the knowledge base.

➡️ The technology uses natural language processing and machine learning algorithms to help you access relevant information from your organizational data records.

What Are The Benefits Of Using AI For Knowledge Management

The core benefits of using artificial intelligence for knowledge management include easier knowledge sharing with your team, providing a personalized information retrieval experience, and the ability to save time.

Let’s go over each one of them👇in a bit more detail. 

#1: Easier Knowledge Sharing

Artificial intelligence facilitates knowledge sharing amongst your team for easier information access.

This enables you to build a centralized knowledge repository for all of your organizational departments so no one in your company has to work in silos.

💡 For example, if you build a custom LLM model and bring it in over at Team-GPT (our tool), your company’s employees will be able to use your company’s AI model, be it for content creation or a research assistant.

#2: Personalized User Experience

As you build out your AI-powered knowledge base, the technology will be able to provide each one of your workers with personalized answers.

As the technology has access to your internal network, it can quickly find the relevant information and show it to your team members.

From having a static internal database, your internal knowledge systems will become quick-to-respond and interactive by unifying different sources of information, improving productivity.

#3: Saving Time

Last but not least, by tapping into AI-powered knowledge sharing, your team will be able to save time.

Instead of having to manually find the data that your team is looking for, they can retrieve the insights they are looking for with an AI-powered internal search.

💡 Alternatively, your team can save time from having to prompt your generative AI model information about your target audience and competitors since the custom AI model will already have access to that information.

6 Use Cases of AI For Knowledge Management

Here are the 6 best use cases of AI for knowledge management from some of the companies I’ve seen do:

#1: Internal Search & Retrieval

The best use case of AI for knowledge management is being able to build an internal search and retrieval system for your organization.

➡️ Instead of your team members having to dig deep into your information systems to find the information they are looking for, they can simply prompt AI to find it for them.

The technology uses natural language processing (NLP) to understand user queries to deliver relevant results.

For example, in sales, there are tools like Einstein AI that help sales development representatives with their questions about deals or your organization and provide them with instant answers.

#2: Content Creation (Custom-GPT)

You can feed your company’s information, branding, and writing style to a custom LLM model to create on-brand content at scale.

With AI collaboration tools like Team-GPT (that’s us), your team can bring a custom AI model and then host it on your premises so you can collaborate with your team on content creation.

Be it for sales collateral or marketing materials, such as articles and advertising copy, your company can fully build and own an AI model that can produce on-brand content.

It is possible to store all of your prompts and enable your newer team members to access them whenever they need them.

#3: Data Analysis

Just like it’s possible to train an AI model to search and retrieve information for you and generate content, an AI model can also be trained to analyze data to identify areas needing improvement.

The way it works is that AI is capable of analyzing large data sets of usage patterns within your knowledge management systems to find gaps in the content.

I’ve found this useful for organizations to fill the gaps with supportive content sooner before it becomes a problem.

➡️ For example, you might find out that your sales team is specifically searching for information regarding a specific feature on your SaaS product since prospective clients are requesting it, but they are struggling to find relevant information.

#4: Knowledge Base Maintenance

Artificial intelligence can further optimize your internal knowledge base by flagging outdated content or underperforming internal information for review.

As 94% of company files have at least one inaccuracy, enterprises can no longer afford to leave their knowledge base with errors.

Your team will then be able to review if the information needs to be outdated and ensure that all data is up-to-date and to standard for your end-users.

➡️ Such automation will also help your team focus on more strategic knowledge management activities, instead of sifting through hundreds of technical content to find if they are all relevant and up-to-date.

#5: Content Summarization

We already mentioned that AI is capable of pointing your members to the correct page where they can answer their questions, but the technology is also able to summarize that information.

As workers are busy, many of them do not have the time to read through heavy technical articles.

This is why one of the best use cases of AI in knowledge management has been their ability to summarize the contents of your documents and information.

#6: Content Tagging & Classification

Lastly, AI algorithms are capable of automatically tagging and classifying your organization’s information and content based on their context, relevance to your organization, and importance.

This not only helps you simplify your organization of information but also helps you improve searchability for your team members (especially newer employees).

For example, Document360 has an automated tag generation that lets you generate tags with AI for every technical article or resource you produce without having to manually add them.

This functionality can be used for internal information management purposes, as you can help different organizational departments quickly find what they are looking for via tags.

What Are The Best AI Tools For Knowledge Management?

When evaluating which platforms to add to this list, I looked at their internal search capabilities, customization options, and cost structure.

Here are the best tools to manage your organization’s internal data:

#1: Team-GPT

Team-GPT is a collaborative AI platform that lets you create a prompt library with advanced prompts and custom instructions, and bring a custom AI model to the platform.

Our AI platform allows you to collaborate with your team using platforms like Claude, ChatGPT, and Perplexity.

You can boost your team’s collaboration with our real-time generative AI collaboration and organizational features.

Team-GPT gives your team an interactive workspace in which you can collaborate in chats, prompts, and threads in real-time.

Master AI alongside your team with our shared learning path feature, allowing you to share insights and support each other.

💡 You can organize your information in folders and sub-folders within the team chats to find the information you need at any time.

It is also possible to see the interactions that their teams are having with the AI and join them.

Customize a Better Version of ChatGPT

You can use ChatGPT with your team on Team-GPT’s platform and customize it to your needs.

After that, you can use your version of ChatGPT for tasks, such as:

• Creating SEO-optimized article content.
• Writing social media posts, advertising copy and email campaigns.
• Editing and finalizing technical materials with Pages and Edit with AI.
• Producing sales collateral and case studies.

Here’s why enterprises love our platform:

• A shared workspace: Collaborate in chats and documents in real-time from a single platform with your team.
• Smart AI-powered editing for improved writing, fixed grammar, and refined text.
• Organized and shared content, ensuring quick access to chats and pages for all your team members.
• Managing your content by turning any chat into a document or starting a new conversation from a Page.

Enterprise-Grade Security

Team-GPT is built with enterprise-grade security, privacy, and compliance.

You can create your custom version of Team-GPT but deploy it on your hosting infrastructure. 

You will hold all your data, whether on-premise or in a private cloud while using all your preferred AI models. 

The software is deployed on your servers and lets you effortlessly onboard marketing employees into the platform.

With Team-GPT Enterprise, you can: 

• Retain complete control and flexibility over your data.
• Apply security filters and retain complete control of the platform with admin rights and report access.
• Prioritize privacy and protection, as Team-GPT is committed to all GDPR compliance standards and holds SOC II and ISO 27001 certifications.

Team-GPT Enterprise

#2: Shelf

Shelf lets you implement an all-in-one solution that was built to streamline knowledge management by integrating AI features, such as:

• Intelligent search that helps your team members quickly find the information they are looking for.
• Automatic identification of obsolete knowledge, as the platform is constantly monitoring your content to identify gaps and opportunities.
• A content generator (GenAI Content Copilot) that lets you improve your knowledge quality.

Instead of building complex AI systems from scratch, the platform lets you quickly organize and optimise your existing knowledge base.

💡 What stood out to me about the platform is how scalable it is, unlike some of its competitors on the market, which is why it has been used by companies such as Hello Fresh and Glovo.

#3: Guru

Guru lets you set up an AI-powered knowledge base for your sales team. 

The platform offers an enterprise AI search, an intranet, and a copilot to give your team quick access to sales collateral.

Here are some of Guru’s top features:

• Quick answers about your company’s products, or insights about deals.
• Knowledge base maintenance: The platform lets you identify outdated sales collateral that needs to be updated.
• Personalized content recommendations for each deal that can be inserted into a conversation with a customer.
• AI-powered content assistant that is capable of creating, translating, and summarizing content for your sales team.

#4: Tettra

Tettra lets you create an AI-powered knowledge base that helps your team members get instant answers from your company docs.

Your team can also get summaries from your Slack threads that they can later save to the platform.

The knowledge-sharing software has been built to make new employee onboarding quicker by showing new members relevant information.

With Tettra, you can create a knowledge base quickly with either new or existing content using their built-in editor or Google Docs and Notion files.

The platform has an integration with Slack where your employees can ask questions and get instant answers from the Tettra bot on Slack.

Interestingly, if the chatbot is not capable of finding the correct answer, the tool will request information from the right person in the company.

#5: Document360

Document360 is a multi-functional AI-powered knowledge base software.

The platform lets you create, share, and manage your knowledge base, internal documentation, APIs, SOPs, and user manuals.

The platform offers separate-built features for the four different categories:

• Knowledge base: Reduce your organizational support tickets by giving easy access to your employees to relevant self-help content.
• Software documentation: Create and manage technical documents across your organization (technical articles) or outside of your organization (release notes).
• User manuals: Replace your old static PDF manuals with dynamic, searchable and multi-lingual user manuals that help you keep your content up-to-date and easy to find.
• Standard operating procedures (SOPs): Centralize and standardize your SOPs for organizational processes, such as quality assurance, quality control, employee training, and compliance.

The platform uses AI-powered search to help your team find the right policy, procedure, case study, feature, or guide that you’ve got in your database.

Next Steps: Adopt AI Alongside Your Team on Team-GPT

Team-GPT lets you create a prompt library with advanced prompts and custom instructions, as well as the ability to bring a custom AI model to the platform.

Our enterprise AI software lets your team utilize various AI models, such as ChatGPT, DALL-E 3, Claude, and Perplexity.

Apart from that, you can access:

• A comprehensive pre-made prompt library to create efficient workflows.
• Detailed usage analytics to track employee engagement.
• Enterprise-grade security ensures data privacy and the ability to host the platform on your servers.

Sign up for a demo of the platform today!

The post Using AI For Knowledge Management: 6 Use Cases, Benefits & Tools appeared first on Team-GPT.

In this guide, I’ll share the tested and tried tips, tricks, and best practices for efficiently using AI for decision-making, helping you unlock your business’s full potential.

As a bonus, I’ll recommend some of the best AI tools that can enhance your decision-making process, giving you a great starting point for your AI journey.

Let’s go!

TL;DR

• AI improves decision-making speed and accuracy by analyzing large data sets quickly, minimizing manual tasks, and enhancing real-time adaptability.
• AI can also recognize trends and patterns, enabling you to create data-driven strategies for product development, pricing, and market expansion.
• You can also use AI to accurately predicts demand, optimize stock levels, and mitigate supply chain disruptions.
• It can be leveraged for personalization, such as delivering targeted recommendations, customized promotions, and dynamic content to improve customer engagement and loyalty.
• Popular AI Tools include Team-GPT, Insight7, Athenic AI, InstantPersonas, and Quantive, which assist with tasks like data analysis, customer persona development, and strategic planning.

How Is AI Used To Improve Decision-Making?

Did you know that over 40% of CEOs use AI to inform their decision-making process?

However, if you’re still having doubts about how an algorithm could help you, a human, make optimal decisions, here’s a quick breakdown of how it works.

It all starts with AI’s key trait—its ability to analyze large sets of unstandardized data in seconds, identify patterns, extract insights, and make data-driven predictions.

What’s more, AI is much more precise than humans when analyzing data and is not prone to emotional and other biases, reducing the chance of error and ensuring consistent data accuracy and quality.

As a result, stakeholders are provided with deep, actionable insights and accurate, reliable, real-time information, enabling them to make informed decisions, ultimately leading to better business outcomes.

This makes AI a godsend in many business areas, especially those requiring careful analysis of vast amounts of data, such as risk management, financial services, sales and demand forecasting, and more.

The best way to understand how AI can enhance decision-making is through a practical example.

Take a rapidly growing retail business that, due to its size, faces several challenges, such as efficiently handling inventory management, smoothly expanding to new markets, and providing good customer experiences at scale.

Implementing AI can help tackle all these challenges, enabling the business to:

• Optimize its inventory, as the AI will analyze historical and real-time data and predict trends and demand.
• Manage its supply chain more efficiently, thanks to AI providing reliable data on potential disruptions and bottlenecks.
• Get a deep insight into market conditions, customer demand, and competitors’ strategies, allowing it to set competitive prices and design optimized sales and marketing strategies.

What Are The Benefits Of Using AI To Enhance Decision-Making?

When considering using AI to improve your decision-making, you should first understand what you can expect from implementing AI.

So, let’s look at some of the key benefits of using AI for decision-making.

1. Improved efficiency 

If there’s one thing you can expect from implementing AI in your decision-making processes, it’s a significant efficiency boost.

AI can analyze large amounts of data in seconds, helping businesses make decisions in real-time. 

This speed is precious in fast-paced industries like e-commerce, where rapid adjustments in pricing, inventory, or marketing strategies can drive better results.

But that’s not all. AI can also automate repetitive decision-making tasks, such as customer service queries, invoice processing, and basic data analysis, making these routine operations more efficient.

As a result, your team will have more time to focus on strategic work, i.e., decision-making, leveraging the data and insights AI provided.

2. Better accuracy

Being able to make decisions faster would do you little good if those decisions were based on incorrect insights or biases.

Fortunately, AI can process vast datasets with 99% accuracy, improving the quality of insights you get and allowing you to make better-informed decisions.

This is especially useful when it comes to areas that depend on several highly complex factors, such as supply chain management, where you have to take into account:

1. Logistics.
2. Inventory.
3. Sales.
4. Potential disruptions.

AI can easily integrate and analyze data from these multiple sources, providing comprehensive insights that make it easier to make decisions.

Moreover, when analyzing large amounts of data, AI minimizes the chance of error and removes bias. 

Namely, humans can easily get lost in mountains of data they need to process or make unobjective decisions based on personal preferences and prejudice.

AI removes all this from the formula, improving the overall reliability of the output you get.

This accuracy is critical in fields like finance, healthcare, etc., where even small mistakes can have enormous consequences.

3. Increased agility and adaptability

When you consistently receive accurate, real-time insights into relevant information such as market trends, demand, etc., you’ll be able to adjust to any potential disruption before anyone else.

And that’s exactly what AI allows businesses to do—respond quickly to market changes by continuously monitoring relevant data and adjusting recommendations, helping executives make optimal decisions that set them up for success. 

This agility is especially valuable in dynamic industries where consumer behavior, regulations, or competitors’ actions may shift rapidly.

4. More informed strategic planning 

AI identifies patterns and trends that human analysis might miss, helping you make informed decisions in areas that require careful strategic planning, such as market expansion, product development, and competitive positioning.

Namely, AI can analyze all kinds of relevant data, including market conditions, competitors’ strategies, customer reviews, and more.

As a result, you’ll be able to create data-driven strategies, including:

• Marketing and sales approaches tailored to customers’ needs, pain points, and expectations.
• Setting realistic prices.
• Product development, ensuring that its features and capabilities are going to resonate with your customers

5. Lets you make decisions proactively

Making decisions in real-time is challenging enough, but it gets even worse when you need to make them proactively.

For instance, stocking your e-commerce inventory months ahead can quickly become a nightmare if you don’t know what factors to consider when making the decision.

AI can help by using predictive analytics to forecast future trends based on historical data, allowing you to make accurate decisions in advance.

This way, you’ll be able to adjust your inventory levels and stock products that will be in high demand, as shown in the example above.

Moreover, AI supports scenario planning by analyzing the potential outcomes of various choices, helping leaders evaluate the best options based on predicted consequences. 

This capability is especially useful in fields like product launches or investments that can have many possible outcomes depending on specific factors.

Pro tip: You can use the Fork Chat feature in Team-GPT to test various scenarios without losing or messing up the original chat.

This way, you and your team can explore various potential outcomes simultaneously, gaining a 360-degree view of the entire situation and all possible scenarios.

Top 7 Use Cases Of AI-Powered Decision-Making

Although you can apply AI in decision-making in many different areas, some are especially suitable for it, including the following:

1. Providing personalized customer experiences

AI can help sales and marketing stakeholders deliver highly personalized experiences for every customer on scale.

Instead of manually processing data such as web search history, purchase behavior, and relevant preferences—which takes time and opens the door to many potential errors and oversights—you can leave it to AI.

AI models will gather and analyze relevant customer data and deliver:

• Personalized product recommendations, ensuring that each customer is presented with the set of products that’s most likely to spark their interest.
• Dynamic content personalization, including on-site elements like banners, promotions, offers, etc.
• Customized discounts and promotions tailored to each customer’s sensitivity to price changes, purchase history, and engagement with promotional content.

Benefits of this use case include more conversions, increased customer loyalty, higher retention rates, and optimized marketing spend.

Moreover, these insights can be used for higher-level decision-making, such as developing products or expanding to new markets.

2. Risk assessment and management

Using AI for risk management can improve the efficiency of your risk mitigation systems by up to 40%, a figure well worth considering.

AI models can continuously monitor vital business processes, ranging from financial transactions to online orders, and detect anomalies indicating potential criminal activity.

Additionally, AI can be trained to score risk based on predefined factors you input, allowing you to assess risk levels from a customer, supplier, partner, or any other third party.

As a result, risk and compliance teams can focus on what really matters, like screening and checking only the relevant risks identified by AI instead of dispersing their attention over vast amounts of data.

This also enables you to provide smoother customer experiences, as the entire onboarding and risk screening process will be automated, making it much faster and less intrusive, especially for financial institutions.

3. Inventory optimization

Managing inventory is far from easy, as any small mistake or miscalculation can increase storage costs, lead to over- and understocking, and result in many missed opportunities due to product availability.

AI models can predict demand based on sales trends, seasonality, and external factors like weather conditions or the economic situation, helping retail and similar businesses make optimal decisions regarding their inventory.

As a result, these businesses will be more agile and capable of meeting their customers’ needs even in the toughest market conditions.

4. Talent management and recruitment

AI can also be hugely helpful to HR.

If you’ve ever been involved in the hiring process, you know what a pain it can be, both for the employer and the candidates.

Assessing each candidate fairly and giving everyone an equal chance during the hiring process is no easy task.

AI can help with that by screening resumes, assessing candidate qualifications, and predicting potential performance based on past hiring data and job requirements. This can save you tons of time and ensure that no quality candidate falls through the cracks.

This way, the chances of finding candidates who are the best fit for both the role and your company culture significantly improve, leading to a 30% increase in efficiency and a 50% reduction in cost-per-hire in some cases.

5. Dynamic pricing

Being able to quickly adjust your prices to current market conditions, supply chain disruptions, increases or decreases in demand, etc., is often the difference between staying competitive and going under in the fast-paced digital landscape.

AI can help you adjust pricing in real-time, leveraging data such as demand, competitor prices, customer behavior, and other external factors.

This allows decision-makers to easily set competitive prices for their products and ensure an optimal profit margin—a goal that would be tough to achieve without careful data analysis.

6. Ad optimization

If you’re a sales or marketing professional, you’re probably already well aware of the many benefits and potential use cases of AI in your sector.

Optimizing decision-making is yet another possible application in this industry, as it can analyze customer data to determine which ads to show to which audiences.

AI models predict everything from the most effective messaging and timing to the best channel to reach each customer segment.

The results?

Increased ROI, better engagement, and a reported 35% increase in conversion rates in some instances.

7. Document analysis

Did you know that AI can also help you analyze any type of documentation?

Instead of reading through text-heavy files such as contracts, legal documentation, etc., you can save time and reduce the chance of error by letting AI do it for you.

For instance, you can feed any document into an AI platform like Team-GPT and prompt it to summarize it, extract vital insights, identify critical points, recognize sentiment, or flag potential risks.

This way, you can get all the information you need for any document or similar content in seconds instead of hours and leverage it to make optimal decisions.

Examples of Companies Using AI For Decision-Making

Today, about half of all businesses report adopting AI in at least one area, often including decision-making.

Here are a few examples of successful companies that leverage AI to enhance decision-making in different business operations.

1. Financial House

Financial House is a global financial services provider that guarantees smooth, quick, and safe transactions to its clients.

However, the digital era introduced a number of new risks, making ensuring safety in financial services challenging.

This is why Financial House adopted an AI-driven risk management solution that enables it to streamline the entire compliance process, continuously monitor for risks, and be notified in real time when a threat occurs.

As a result, the company experienced a significant operational efficiency boost, improved employee and customer experiences, and safety across levels.

2. Amazon

Amazon is known for its wide use of AI to optimize a number of business processes.

For instance, it uses AI to analyze images and videos to improve product listings and recommendations, instead of relying on manual effort, which would take more time and wouldn’t be as accurate.

It also leverages AI to increase supply chain efficiency by forecasting demand, optimizing inventory levels, and improving routing orders.

3. Meta

Meta is another company that uses AI to deliver optimal, highly personalized customer experiences.

If you’ve been wondering how your feed is so tailored to your interests and preferences, it’s AI you can thank.

AI-driven algorithms recommend stories, accounts to follow, and reels to watch based on a number of relevant factors, such as:

• Previous interaction with content (watches, likes, shares).
• Demographics.
• Location, etc.

Best AI Tools To Improve Decision-Making

And now, I’ll quickly break down some of the best tools you can rely on to help you harness AI for decision-making.

1. Team-GPT

First up is Team-GPT – the platform I regularly use alongside my team and thousands of satisfied business users from various industries.

Team-GPT is a model-agnostic AI platform that lets your team work together on AI chats, using them for everything from ideation and content creation & editing to analyzing data, extracting vital insights, and more.

What sets Team-GPT apart from other AI-driven tools is the following:

• You can easily switch between AI models within one chat, enabling you to use the model best suited for your current needs every time.
• Everyone on your team can work in the chat, giving prompts, leaving comments, reactions, etc.

• Chat Forking allows you to test various scenarios to get a complete view of a problem, enabling you to make informed decisions every time.
• Prompt library with customizable pre-made prompts for common use cases that help you save time (you can also create and save custom prompts tailored to your organization).
• Flexible AI toolkit that can be fully customized to fit your organization’s needs and objectives.
• Build expert personas for certain areas and use them to get expert advice, suggestions, and insights into a topic.

• Options to save and organize chats in folders and subfolders for easier navigation.

As such, Team-GPT is best suited for teams of all sizes looking to adopt AI smoothly and make the most of its unbridled potential.

2. Insight7

Insight7 is a nifty tool for HR, sales, and marketing people who need to quickly extract insights and key points from conversations.

Insight7 leverages AI to analyze documents, audio, and video files and can:

• Provide accurate transcriptions.
• Extract themes and actionable insights across multiple files in just one click.

• Generate detailed reports and visual maps that help you track the entire conversation and easily pinpoint key questions, pain points, churn risks, behavior insights, and more.

3. InstantPersonas

InstantPersonas uses AI to help businesses handle a vital process – creating their Ideal Customer Persona.

Without a good understanding of who you’re selling to and their needs, expectations, and problems, you cannot expect to create efficient and targeted sales and marketing strategies.

InstantPersonas helps you take care of that by creating your ICP in seconds based only on the description of your product or services.

The platform also provides:

• A detailed description of your ICP, including the kind of content they consume, the channels they are most likely to use, social media platforms they’re most active on, etc.

• See how your website would perform with your ICP.
• Create SEO-optimized content tailored to your ICP.

4. Quantive 

Quantive is a comprehensive strategy management platform that leverages AI to help you plan, execute, and adapt to market changes in real time.

As such, it can be useful to managers and other executives who want to improve their decision-making and strategic planning with AI.

Some of its key capabilities include:

• Pulling actionable insights from relevant internal and external documentation and data, helping you make data-driven decisions.
• AI-driven recommendations help you set realistic goals and define the best ways of achieving them.
• Executive dashboards enable you to monitor the progress, contributions, and tasks of the entire team and each individual team member, as well as the overall project advancement.

5. Athenic AI

Athenic AI lets you make the most of unstructured data sets without being a data scientist.

All it takes is prompting the engine in natural language to analyze data in a certain way or pull specific insights, and it will deliver.

Its advantages include:

• High customization that lets you tailor its LLMs to your specific needs and data types.
• Generates charts and other visualizations in seconds based on natural language prompts.

• Explainability, meaning that the platform will explain how it answered your questions and what led to the output you got.

Next Steps: Improve Your Decision-Making with Team-GPT

And now, all that’s left is to start implementing AI in your decision-making processes.

If you’re a team of 2 to 20,000 that needs to speed up AI adoption and ensure that every critical team member is onboard for optimal results, Team-GPT is the surest way to go.

Thanks to its versatility, you can use Team-GPT to optimize a number of business processes, ranging from decision-making in every area imaginable to creating content, analyzing data, improving sales and marketing strategies, and more.

Sounds too good to be true?

Book a demo today and see firsthand how Team-GPT can help you unlock your business’s full potential.

The post Using AI For Decision-Making: 7 Use Cases, Examples & Software appeared first on Team-GPT.

Thanks to its ability to analyze vast data sets and identify patterns, AI can be a sort of a super-accurate crystal ball that enables predicting future market trends and anticipating customer behavior, giving you a strong competitive advantage.

In this article, I’ll teach you how to use AI for predicting in a way that will let you get the most out of it, based on my years of experience as a serial entrepreneur and AI expert.

Let’s start by explaining what predictive AI is.

TL;DR

• Predictive AI analyzes large data sets to forecast trends, anticipate customer behavior, and gain a competitive edge.
• It enhances decision-making, operational efficiency, and customer experience, enabling data-driven strategies that optimize outcomes.
• Major benefits include cost reduction, better resource allocation, proactive adjustments to demand and supply chain, and improved customer retention.
• Top predictive AI applications in retail include demand forecasting, consumer behavior prediction, churn detection, fraud detection, and supply chain optimization.
• Companies like Starbucks, Netflix, Amazon, and Macy’s use predictive AI to personalize experiences, prevent disruptions, and improve inventory management, demonstrating its broad impact across industries.

What Is Predictive AI?

Simply put, predictive AI refers to the use of AI and ML algorithms to forecast future outcomes or behaviors based on historical data. 

Namely, predictive AI models can make informed predictions about potential future events by analyzing patterns, trends, and relationships in past data – something AI excels at in general.

These predictions help organizations anticipate customer needs, optimize operations, improve marketing strategies, and reduce risks.

Here’s a closer look at how predictive AI works step-by-step:

1. Data collection – Large datasets are gathered from various sources, such as customer behaviors, historical sales, market trends, social media activity, and other business-relevant sources.
2. Data processing – Data is cleaned, organized, and transformed for accurate analysis.
3. Model training – Machine learning models are trained on historical data to identify patterns and correlations unique to your business and industry.
4. Generating predictions – Once trained, models use this and new data to create predictions, adjusting their accuracy over time through continuous learning and feedback.

As a result, you’ll get a well-oiled mechanism capable of generating more leads, getting you more conversions, and boosting your overall operational efficiency.

Which gets us to the next question:

What Are The Benefits Of Using AI To Make Predictions?

Creating an extensive list of all the benefits of using predictive AI would be impossible, as there are so many of them that I could probably write an entire article about them alone.

Here, I’ll share some of the main advantages of leveraging AI for making predictions, which I and some Team-GPT users personally experienced:

1. Enhanced decision-making

One of the biggest perks of AI is that it can identify patterns that are often too complex for traditional methods to detect.

This way, instead of relying on your gut instinct and hoping for the best, you’ll be able to anticipate market shifts, customer needs, and potential risks, helping you make data-driven, strategic choices.

As a result, you’ll be able to:

• Expand to new markets more efficiently.
• Successfully up-sell and cross-sell.
• Speed up and enhance the product development process, as you’ll know how to optimize it for your customers’ needs, pain points, and much more.

2. Increased operational efficiency

Predictive AI can optimize various processes, ranging from supply chain management to demand forecasting and more.

For instance, predictive AI allows you to predict potential supply chain disruptions in certain parts, allowing you to adjust on time, find new suppliers, or choose better delivery routes.

When it comes to demand forecasting, predictive AI can analyze historical sales data, seasonality, and market trends to predict demand with high accuracy. 

This way, you’ll be able to stock optimal inventory levels, reducing both overstock and stockouts, which can lead to wasted resources or missed sales opportunities.

By predicting future demand and potential disruptions, AI allows you to proactively adjust to all those shifts, enabling you to reduce costs and redundancies across levels.

3. Cost reduction

As I pointed out above, predictive AI models allow organizations to save on costs by optimizing resource allocation and reducing inefficiencies.

However, there are other ways AI can help you reduce the overall operational costs, such as:

• It can identify potential equipment failures before they occur, minimizing downtime and repair costs. For example, a mining company used AI-driven solutions to predict maintenance needs, reducing production downtime by up to 30%.  
• It can help hospitality businesses predict busy periods, enabling them to schedule stuff optimally and adjust their hiring frequency accordingly.

4. Improved customer experience at scale

Predictive AI also allows you to deliver optimal customer experiences across levels.

With it, you’ll be able to anticipate customer needs, personalize interactions, and create a smoother and more engaging journey for each customer. 

This way, you’ll boost customer satisfaction, loyalty, and engagement – and at scale, as AI enables you to handle swarms of customers in seconds.

5. Significant competitive advantage

Finally, leveraging predictive AI gives you a solid competitive advantage, especially compared to businesses not yet using it.

With predictive AI at your side, you’ll get a more agile business optimized for success, as you’ll be able to:

• Predict trends before the competition and adjust your entire business strategy accordingly.
• Adjust to sudden market changes or disruptions much faster than the competition.
• Identify potential threats in areas like cybersecurity, compliance, and supply chain management, allowing you to prevent them.

Combine this with all the benefits I listed above, and it becomes crystal clear that predictive AI is something you should definitely incorporate into your system.

Top 5 Use Cases Of Predictive AI

Now, let’s look at some of predictive AI’s most common use cases, where its capabilities shine the brightest.

1. Demand forecasting

As I briefly mentioned above, when discussing the key benefits of predictive AI, demand forecasting is one of its essential use cases.

Namely, predictive AI analyzes a wide range of relevant data, including historical sales data, seasonality, and market trends, leveraging it to accurately forecast future demand. 

Since AI can uncover patterns where humans cannot, it can detect relevant correlations within massive datasets, enabling a deeper understanding of demand drivers. 

For instance, it can reveal how factors like customer sentiment, social media activity, or shifts in economic conditions affect demand, allowing you to adjust your business accordingly.

Moreover, predictive AI’s ability to process large volumes of data and learn from new information continuously results in more accurate forecasts, helping you allocate your resources more efficiently and making you much more adaptable to sudden changes in market conditions.

As a result, you’ll be able to optimize inventory levels, reduce stockouts and overstock situations, and ensure you meet customer demand on time and without waste.

2. Predicting consumer behavior 

Another critical use case of predictive AI is predicting consumer behavior – an essential factor in your business success.

By understanding your prospective customers’ likely behaviors—such as purchase intentions, browsing patterns, pain points, or brand loyalty—you can tailor your offering, marketing, and overall business strategy to them, providing them with an unmatched customer experience.

It works similarly to demand forecasting in some aspects: AI collects and analyzes historical and real-time consumer data to identify patterns and trends that may indicate future actions.

Once you’re able to predict consumer behavior, you can leverage it for a number of things, including:

• Providing personalized recommendations based on customers’ past purchases, browsing history, and demographic data.

• Delivering dynamic personalization of app or website content based on each user’s preferences, needs, and prior interactions, making their experiences more intuitive and satisfactory.
• Identifying buyer intent by analyzing the frequency of website visits, time spent on specific pages, and engagement with ads or email content, allowing you to focus on high-intent leads from the get-go.
• Driving cross-selling and upselling opportunities.
• Dynamic pricing optimization which allows you to adjust your pricing based on relevant factors like competitor pricing and customer purchasing behavior, helping you set highly competitive prices.

3. Predicting customer churn

Predictive AI models detect behaviors that are indicators of very specific consumer behavior, such as customer churn.

These indicators could include anything from reduced engagement and long periods of inactivity to negative feedback in customer service interactions. 

In addition to helping you identify these signals early, AI can help you get a better understanding of the key drivers for churn, allowing you to take proactive steps – such as reaching out with tailored offers, discounts, and more – in order to retain them.

4. Detecting fraud and other security risks and breaches

Another common use case for predictive AI – especially in highly regulated sectors or those handling sensitive data, such as the medical industry, finance, etc. – is risk management.

Because predictive AI excels at identifying suspicious activities in real-time and adapting to new fraud techniques, it’s perfect for enhancing the overall cybersecurity of any business.

For example, predictive AI can continuously monitor data for anomalies based on predefined rules, patterns, and previous interactions, like unexpected login locations, large transactions, or rapid account changes.

The best part of leveraging AI for cybersecurity is that it learns from past criminal activity it detected or was introduced to, which enables it to quickly adjust to evolving types of crime and flag them in time.

As a result, you’ll be able to detect potential threats before they wreak havoc on your business.

5. Supply chain optimization

It’s never been as difficult to manage supply chains as today, as they’ve become much larger and more complex.

Enter AI to save the day again.

Feeding historical and real-time data regarding your suppliers, common transfer routes, and other relevant information into your predictive AI model of choice enables you to accurately predict potential supply chain disruptions and bottlenecks.

This way, you’ll be able to adjust in time by switching to different suppliers or finding better delivery routes, helping you save valuable resources otherwise wasted.

Predictive AI Statistics

And now, let’s look at what the numbers have to say regarding the rising use of predictive AI.

1. Predictive AI is one of the fastest-growing AI markets

As of 2023, the global predictive AI market was valued at approximately $14.9 billion. 

Moreover, the market is expected to keep growing over the next 10 years, reaching a staggering $108 billion by 2033. 

The industries that lead in market share are sales & marketing, risk management, and financial forecasting.

2. AI can reduce forecasting errors by half

A McKinsey study showed that using AI can reduce forecasting errors by 50% and lost sales due to inventory shortages by up to 65%. 

This figure perfectly illustrates how predictive AI can help you reduce costs and overall product waste. Accurately forecasting demand and sales enables you to maintain optimal inventory levels and allocate resources strategically.

3. Predictive AI helps you create robust supply chains resilient to disruptions

A case study published by IBM illustrates how the technology giant applied some of its AI-powered supply chain tools to its own operations with excellent results.

The experiment resulted in $160 million in savings and a 100% order fulfillment rate even during the peak of the COVID-19 pandemic. 

4. AI-driven predictive analytics can boost your revenue

According to the research firm Aberdeen, companies identifying customer needs and expectations using predictive AI can increase their organic yearly revenue by an average of 21%.

Once you compare that to an average of 12% without predictive analytics, it’s clear why so many businesses are in a rush to start implementing it.

5. More than half of marketing leaders are already using or plan to use predictive AI

Thanks to its ability to predict market trends and demand, 53% of marketing leaders use or intend to trial predictive AI.

The main reason listed is a deeper and more accurate analysis of customer insights, which will enable them to create better, tailored marketing strategies.

Examples of Companies Using AI For Predicting

Here’s a list of several top companies already successfully leveraging predictive AI to boost ROI and customer satisfaction.

1. Starbucks

First up is Starbucks – a company known for its customer-centricity, which is one of the main reasons for its years-long success.

Starbucks has started using predictive AI some years ago to be able to create better-personalized product recommendations.

Its AI model collects and analyzes purchasing behavior, such as what a consumer usually buys, when, how much they spend on average, and more.

As a result, the engine spews personalized recommendations based on what you’re most likely to enjoy at a given moment.

2. Netflix

Netflix is also a well-known user of predictive AI, although many users are unaware of it.

All the personalized watching recommendations you get are made possible precisely by predictive AI.

Netflix carefully analyzes the shows and movies you’ve watched, pinpointing the genre and type of program you most prefer. It then uses this information to deliver highly personalized suggestions bound to delight even the most demanding users.

3. Amazon

Amazon is yet another company that uses predictive AI to create up-sell and cross-sell opportunities by forecasting demand and providing personalized product recommendations.

Based on your previous purchases, searches, or the products you’re currently eyeing, Amazon’s AI model will recommend additional products you’re likely to be interested in.

The result?

15x the improvement in ROI and customer engagement than ever before.

4. Kraft Heinz

Kraft Heinz, a multinational food company, uses predictive AI to predict and avoid potential supply chain disruptions.

As a company with several complex supply chains, being able to predict potential bottlenecks, ingredient unavailability, and similar issues is critical for maintaining a steady manufacturing and distribution chain.

What’s more, Kraft Heinz doesn’t use predictive AI just to predict disruptions—it relies on AI to automatically make alternative decisions and switch to a different route or supplier in case the original choice is problematic.

5. Macy’s

Finally, Macy’s uses predictive AI and ML models to improve inventory management and enable more accurate demand forecasting.

Since as many as 50% of Macy’s annual products are new, predicting what customers will want six or twelve months from now is little more than guesswork.

Predictive AI helped Macy’s take a turn for the better, enabling the retail giant to make better-informed decisions and stock only products bound to sell.

Next Steps: Leverage Predictive AI with the Help of Team-GPT

If you’re wondering how to start incorporating predictive AI into your organization, look no further than Team-GPT.

Team-GPT is an AI platform designed for teams of all sizes that need:

• A collaborative AI space that enables teamwork on any AI project.
• A model-agnostic solution that allows for using any AI model within the same project.
• Military-grade safety.
• A flexible AI toolkit that can be tailored to fit any industry and specific use case, including predictive analytics and much more.

Book a demo today to learn how Team-GPT can drive AI adoption across your organization and help you harness predictive AI’s full potential.

The post Using AI For Predicting: 5 Use Cases, Examples & Statistics appeared first on Team-GPT.