How AI Can Be Your Market Research Assistant

Staying ahead of the competition is a constant battle. As a product manager, you need to know not just what your competitors are doing today, but what they're planning for tomorrow. The problem is, manual market research is a full-time job in itself. It involves sifting through press releases, reading competitor blogs, monitoring social media, and analyzing product updates—all while your own product needs your attention.

What if you could outsource this exhaustive research to a tireless, intelligent assistant?

This post is part of a series: Innovate Your Way to Product Management Success with AI. In this series, we explore how AI can help you conquer the most tedious parts of your job as a Product Manager or Project Manager, so you can focus on what truly matters. Click here to see all the posts in the series

This is where AI becomes a game-changer. Instead of being a passive consumer of endless data, AI allows you to become a strategic leader, getting a comprehensive market overview in a fraction of the time.

The Manual Grind of Competitive Analysis

The traditional approach to competitive intelligence is a time-consuming and often reactive process. You're constantly playing catch-up, relying on a mix of Google Alerts and manual check-ins. This method is slow, prone to missing key signals, and doesn't easily allow you to see the bigger picture.

You might be able to create a simple competitive matrix, but keeping it up-to-date is a monumental effort. Every time a competitor launches a new feature, you have to go back and manually update your analysis. This isn't strategic work—it's data entry.

AI: Your Personal Market Research Team

AI-powered tools and Large Language Models (LLMs) can act as your personal market research team, working 24/7 to gather, synthesize, and report on competitive and market data.

• Automated Data Harvesting: AI can crawl the web for information on your competitors—from product pages and feature updates to news articles and pricing changes. It can monitor social media for shifts in customer sentiment and even analyze job postings to predict their strategic hires.
• Synthesized Summaries: The real power of AI isn't just in gathering data, but in making sense of it. Instead of reading through a dozen press releases, an AI can provide a concise summary of a competitor's Q3 strategy, highlighting their key focus areas and recent product launches.
• Trend Identification: AI can analyze vast amounts of data from industry reports and news to identify emerging market trends and shifts in consumer behavior. This helps you spot opportunities and threats that might have been hidden in the noise.
• Dynamic Competitive Matrix: You can use an AI to generate and maintain a feature-by-feature comparison of your product against key competitors. When a competitor launches something new, the AI can flag the change and update the matrix, giving you a live, accurate view of the landscape.

A Real-World Scenario

Imagine you're launching a new feature in a crowded market. You need to know what your top three competitors are offering in that same area, what their pricing looks like, and what customers are saying about their solutions.

Instead of spending days manually researching each company, you could give an AI a simple prompt: "Analyze our top three competitors—Company A, B, and C. Provide a summary of their features related to [your new feature], their pricing tiers, and a sentiment analysis of recent customer reviews for those features."

Within minutes, you'd have a comprehensive report that gives you a clear picture of the competitive landscape, allowing you to make a more informed decision about your own feature's positioning and pricing.

By offloading the tedious work of data collection and analysis to AI, you're not just saving time—you're elevating your role. You can spend more time thinking about strategy, identifying new market opportunities, and leading your team, instead of getting lost in a sea of search results. 

How AI Summarizes Feedback Like a Pro

As a product manager, you're the voice of the customer. But what happens when that voice is a deafening roar of hundreds, even thousands, of support tickets, survey responses, and app reviews? You get stuck in a tedious loop of manual data analysis—a process that's slow, prone to bias, and, frankly, keeps you from doing the strategic work that really matters.

What if there was a better way?

This post is part of a series: Innovate Your Way to Product Management Success with AI. In this series, we explore how AI can help you conquer the most tedious parts of your job as a Product Manager or Project Manager, so you can focus on what truly matters. Click here to see all the posts in the series

This is where AI, specifically Large Language Models (LLMs), becomes your new best friend. Instead of spending hours reading every single piece of feedback, you can let an AI do the heavy lifting, giving you actionable insights in minutes.

The Manual Method: A Slog of Spreadsheets

Let’s be honest. The traditional way of analyzing feedback is a grind. You're exporting data to a spreadsheet, manually tagging comments with categories like "bug," "feature request," or "UX issue," and then trying to find patterns. It's not just a time sink; it’s also easy to miss subtle trends or get bogged down in individual, loud-but-uncommon complaints. You might find a few key themes, but you're likely missing the full picture.

The AI Advantage: From Noise to Clarity

With an LLM, you can feed a mountain of unstructured data into a single tool and get a concise summary in return. The AI doesn’t just count keywords; it understands context.

• Summarizing at Scale: Instead of reading through 500 feedbacks, an AI can process them all and tell you, for example, that "40% of users are complaining about a bug in the new checkout flow" and "25% are requesting a dark mode feature."
• Identifying Sentiment and Pain Points: An LLM can perform sentiment analysis to tell you not just what people are saying, but how they feel. This helps you quickly gauge if a recent feature launch was a success or a flop. It can also pinpoint common pain points that you may have overlooked.
• Surfacing Feature Requests: By analyzing user feedback, the AI can group similar requests together, helping you identify the most-demanded features. This allows you to prioritize your roadmap based on what your users actually want, not just what a few vocal critics are yelling about.

Think of it as having a tireless research assistant who can read and comprehend thousands of pages of text instantly.

A Real-World Example

Imagine your product just launched a new feature. In the following week, your support team gets hundreds of tickets. You could spend days going through them one by one. Or, you could take all that data, feed it into an AI tool, and ask it, "What are the three most common problems users are reporting about the new feature?"

Within seconds, you get a clear, concise answer. This not only saves you countless hours but also ensures that your response is swift and data-driven. You can then go into a sprint planning meeting with a clear agenda, armed with hard evidence of what needs to be fixed.

The goal of AI isn't to replace your strategic mind; it's to free it. By automating the drudgery of data analysis, you can spend more time thinking about product vision, talking to your customers, and leading your team—not wrestling with spreadsheets.

From Meeting Minutes to Action Items: Automating Follow-Up with AI

Meetings are the lifeblood of product management. They're where ideas are debated, decisions are made, and strategies are set. But what happens when the meeting ends? The conversation often gets lost in a sea of scribbled notes and forgotten action items.

You might as well have not met at all.

This post is part of a series: Innovate Your Way to Product Management Success with AI. In this series, we explore how AI can help you conquer the most tedious parts of your job as a Product Manager or Project Manager, so you can focus on what truly matters. Click here to see all the posts in the series

This post isn’t about making meetings shorter (though AI can help with that, too). It's about ensuring every valuable insight and decision is captured, organized, and acted upon without you having to be the sole keeper of the team's collective memory. AI is transforming this process by moving beyond simple transcription and providing true, intelligent automation.

The Manual Struggle of Post-Meeting Chaos

After a crucial meeting with engineering and design, you’re left with a jumble of notes. You have to manually sift through them to identify who is responsible for what, what the next steps are, and which decisions were finalized. This manual process is:

• Time-Consuming: You spend precious time writing and summarizing instead of leading the product.
• Prone to Error: It's easy to misinterpret a note or forget a key detail, leading to misunderstandings and rework.
• Inefficient: The time between a decision being made and an action item being assigned can cause unnecessary delays.

AI: Your New Intelligent Note-Taker

With an AI-powered note-taking and transcription tool, the post-meeting scramble becomes a thing of the past. These tools don't just transcribe audio; they understand the content and extract the most critical information for you.

• Automatic Summarization: The AI can generate a concise summary of the entire meeting, highlighting the main topics and key decisions.
• Action Item Identification: It can automatically listen for phrases like "let's follow up on..." or "John, can you take a look at..." and create a list of action items, assigning them to the correct person.
• Timeline and Task Management Integration: Many tools can integrate with project management software, automatically creating tasks and setting deadlines in a system like Jira or Asana, saving you from manual entry.

This automation ensures that what happens in the meeting room actually translates into progress, turning decisions into actionable steps with minimal effort.

More Than Just Meetings: AI in Your Project/Product Management Systems

The power of AI's automated monitoring and summarization extends far beyond the meeting room. Think about the complex systems you manage that are crucial to your workflow.

• Briefing Management Systems (BMS): As a product manager, you're constantly involved in briefings—for internal stakeholders, sales teams, or executives. A BMS helps you organize, schedule, and track these. Instead of manually preparing a "briefing book" with slides and data, AI can automatically pull relevant information from different sources (project dashboards, customer feedback summaries, market reports) to create a concise, up-to-date briefing document. It can also identify key questions asked during the briefing and help you generate a list of follow-up tasks to ensure alignment across the organization.
• RAID (Risks, Assumptions, Issues, Dependencies): A RAID log is a critical tool for identifying potential roadblocks. Manually tracking and updating this can be tedious. AI can automate much of this process by:
• Predicting Risks: By analyzing project data and historical trends, AI can flag potential risks before they materialize.
• Surfacing Assumptions: AI can analyze meeting transcripts and documentation to identify unspoken assumptions that need to be validated.
• Identifying Issues: It can monitor customer support channels and bug trackers to flag new issues that require immediate attention.
• Mapping Dependencies: It can analyze project plans and team communications to map out and highlight critical dependencies between tasks and teams.

These are just two examples of how AI can automate monitoring and decision-making for even the most complex systems, turning a flood of raw data into proactive, intelligent action.

By leveraging AI, you can move from a reactive state of constantly putting out fires to a proactive one, where potential problems are flagged and addressed before they even become an issue.

The End of Writer's Block: Let AI Draft Your PRDs and User Stories (Yes, Really!)

The cursor blinks. An empty page stares back at you.

We've all been there. Staring at a blank document, tasked with writing a detailed Product Requirements Document (PRD) or a series of user stories, and feeling that familiar dread. It’s a crucial part of the job, but it can be a significant drain on your time and creative energy.

This post is part of a series: Innovate Your Way to Product Management Success with AI. In this series, we explore how AI can help you conquer the most tedious parts of your job as a Product Manager or Project Manager, so you can focus on what truly matters. Click here to see all the posts in the series

What if your most time-consuming writing tasks could get a head start?

Enter AI. Large Language Models (LLMs) are not here to replace you as the strategic brain behind the product, but to act as a powerful co-writer who can get the first draft done in minutes. This frees you up to focus on the nuances, the strategy, and the details that make the difference between a good product and a great one.

The Pain of Manual Documentation

Creating a PRD from scratch is a multi-step process. You have to outline the problem, define the goals, list the user stories, and detail the functional requirements. This process is essential, but it takes hours, often days, of focused effort.

The same goes for user stories. You need to craft each one to be clear, concise, and actionable for your engineering team. Writing them all can feel like a repetitive chore, even when the underlying feature is exciting.

This isn't just a time sink; it’s a creative blocker. The energy you spend on the mechanics of writing is energy you could be spending on customer interviews, competitor analysis, or team alignment.

The AI-Powered Solution

An LLM can take a few bullet points and turn them into a structured, well-written document. You provide the high-level strategy, and the AI fills in the rest, creating a solid foundation for you to build on.

• Drafting PRDs in Minutes: Give an AI a simple prompt, like "Create a PRD for a new feature that allows users to create custom profiles." Add a few key details like target users, business goals, and core features. The LLM can then generate a comprehensive first draft, complete with sections for the problem statement, success metrics, and a list of potential user stories.
• Generating User Stories at Scale: For a new feature, you might need dozens of user stories. Instead of writing each one manually, you can ask an AI to "Generate 10 user stories for a mobile banking app's new savings goal feature." It will provide a set of stories like, "As a user, I want to set a specific savings goal so I can track my progress," and "As a user, I want to get a notification when I'm close to reaching my goal so I stay motivated."
• Improving Clarity and Consistency: LLMs are great at maintaining a consistent tone and structure. They can help you refine your wording to ensure your documentation is easy for your engineering, design, and marketing teams to understand. You can simply say, "Rewrite this section to be more concise and clear for a non-technical audience."

By offloading the initial drafting to an AI, you're not just saving time. You're making your documentation process more efficient, allowing you to focus on the strategic depth that only a human can provide. Your role shifts from being a document creator to a strategic editor, refining and perfecting the work to align perfectly with your product vision.

Stay tuned for our next post, where we’ll talk about how AI can help you conquer the chaos of meeting notes and follow-ups.

Is Blogging Still Relevant in 2023?

I'm writing this in February 2023 and my take is: Yes, I think it's still relevant.

I did a quick search on Google and I found that there's a lot of people think the same way. You can read about it here on Google. I agree with most of them, people still read blogs, blogs allow us to dig deeper into a subject, blog can provide more detailed information, and so on.

I just started this blog, not long ago. I haven't put anything much on it. I haven't even decided my niche yet. Probably for now, I'd just throw anything here. I had some blogs a while ago which I abandoned.

And, English is not my primary language, so please excuse my crappy writings.

With the rise of rich social media, many people decide to turn to video. Then, why on earth am I starting a blog? Why not videos?

Well, first, I feel publishing videos would be a lot of work. I can record videos, make animations, or compile some stock videos. I know how to edit videos. I wasn't bad on this thing at all. 

But, I think, it still will have to start with writing. I may have to write the scripts for my videos first. I may have to plan my contents and then work on it. So, I thought, if I have to write anyway, I better start publishing it immediately. How? Blog it.

Then, will I make videos, when? My simple answer is, when I'm ready. Because it entails a lot of work.

But, then I question myself, do I really need to turn to videos? Well, I don't think so. I may going to stick to writing. I may stay blogging.

I believe, I won't need to make my own videos. With the progress of AI today, I believe that this thing would soon be able to help me. I believe AI will be able take care of it, soon. Check out Bard and ChatGPT and see their progress. Or, have a google search on AI Video Generators, there are quite a number of them.

We know today there are tools that will read out things for you. Be it books, news, websites, anything with text on it, we already have tools to read em for you.

I believe that soon, AI can also read and visualize streams of text on your screen. AI can automatically transform my blog, into videos.

And I also believe that AI may also be able to present unique visualizations to each audience. This will bring back the joy of reading, where we would try to visualize what we read, in our mind. Each person may have different visualizations in their heads about the same thing that they read.

With that in my mind, I strongly feel that blog is still relevant. It will continue to grow, evolve, and get even better. I think, many would even come back to blogging. For audience that prefer video, they can watch AI generated videos which will produce a unique visualizations that suit the audience. Others who prefer reading, can keep on doing what they like to do, reading.

That's just my opinion. I can be wrong. May be I'm just too lazy to make videos. Let's just wait and see.

Please share your thoughts in the comment section. 

GBU.