Think back to how you analysed user feedback two years ago.

For me, running Iconic (my WooCommerce plugin company which I later sold in 2021), it meant opening dozens of browser tabs: GitHub issues, Reddit threads, support tickets, forum posts. I’d spend entire afternoons copying user feedback into spreadsheets, trying to identify patterns that would shape our plugin roadmap.

It was necessary work, but exhausting. And I always wondered: what patterns was I missing in the noise?

Today, in my role as Product Manager at WooCommerce, that same analysis takes 30 minutes for an initial pass. I use ChatGPT not just to analyse feedback I’ve found, but to discover sources I’d potentially miss entirely. It searches public discussions, surfaces relevant threads, then identifies patterns across everything: how a checkout issue on Reddit relates to a GitHub complaint about payment processing, which connects to conversion rate discussions in forums.

The AI holds all that context simultaneously and reveals the hidden structure. Those patterns now drive feature prioritisation with a clarity that manual analysis rarely achieved.

This transformation from information processor to strategic thinker represents one of the biggest shifts in my career.

My AI Toolkit in Action

ChatGPT (o3/4o for images, complex reasoning, and daily tasks) has become my thinking partner across surprising scenarios. Yes, it handles competitive analysis and technical deep dives. But the real magic happens in unexpected places.

When I need to quantify feature requests, ChatGPT crawls through public feedback to find patterns I’d never surface manually. Before tagging stakeholders in complex Linear issues or Slack threads, I can generate context summaries highlighting exactly what input I need. This simple practice transformed our async communication. It allows me to loop people into conversations without expecting them to digest the full interaction before they can contribute.

I use it to explore ecommerce futures, treating it like a strategic advisor who’s read every trend report. When scoping MVPs, it helps me think through edge cases and user flows I might overlook.

On the personal side, it plans trips, designs puzzles, and helps write compelling marketplace descriptions. I frequently use the mobile app when in the supermarket to take a photo of available products on the shelf and ask for recommendations.

My custom OpenAI tool for document generation represents hours of refined prompting. I can input goals, problem statements, and solution outlines, and get back comprehensive PRDs in an exact, consistent format. But it goes beyond templates, suggesting testing approaches, proposing success metrics, and recommending technical considerations. The leap from 3-4 hour documents to 30-minute iterations changes everything. The tool itself was built with AI.

Claude (Sonnet 3.5, now Opus 4) excels where ChatGPT struggles: creating interactive prototypes with genuinely good design sense. I can describe a feature concept or share a reference image and get back a working demo that looks professional. It’s particularly powerful during ideation when you need teams to visualise ideas before design even starts.

Cursor and Bolt complete my technical toolkit. I’ve built custom tools, created advanced prototypes, and explored new coding languages. Together, they’ve made it possible to quickly validate feasibility without waiting for engineering input.

The Compound Effect

A Statista report claims that 72% of companies integrated AI into at least one business function in 2024, up from 55% the year before. In 2025, we can expect that number to grow again. The compound effects extend far beyond individual productivity:

When insights take minutes instead of hours, you can easily understand 10 competitors rather than just 3. When prototyping happens in hours not weeks, you test multiple concepts before committing resources. When past discussions are instantly searchable, every decision builds on previous learnings. When you can instantly reframe information for different audiences, alignment happens naturally.

McKinsey research shows that companies seeing the greatest returns from AI invest as heavily in organisational change and user adoption as they do in technology itself. Successful AI isn’t just about deploying tools; it’s about integrating them thoughtfully into daily workflows and supporting people through the transition.

The real value emerges when human judgment and AI capabilities complement each other, not when one replaces the other.

Beyond Product Specifications

The universality of AI as a problem-solving interface hit me in my garage. My lawnmower needed servicing, and instead of wrestling with faded manuals, I opened ChatGPT’s mobile app, switched to video mode, and got real-time guidance for the oil change and filter replacement.

That moment crystallised something important. This technology isn’t confined to any specific type of work. It’s a universal interface for any complex problem: debugging code, analysing retention metrics, understanding ingredient lists, or diagnosing mechanical issues.

The Reality Check

Let’s be clear: AI hasn’t automated the product manager role. What it has done is remove friction from information processing. The time I used to spend on research and data processing now goes toward building deeper customer empathy, crafting thoughtful strategies, working with team members, and thinking about the future instead of just managing the present.

With recent data showing that 99% of tech-using, adult Americans interact with AI-enabled products weekly, often without even realising it, product managers who don’t embrace these tools risk losing touch, not just with productivity gains, but with the everyday experiences of their users.

Starting Your AI Journey

For those ready to evolve their workflow, start with one pain point. Pick your biggest time sink (user feedback analysis, documentation, competitive research) and dedicate a week to solving it with AI. Don’t try to transform everything at once.

Invest in prompt engineering. The difference between mediocre and exceptional AI output often comes down to how you frame the request. Build templates for common tasks – JSON and XML are great formats for this (tip: the AI can help you write these as well).

Maintain healthy skepticism. AI excels at pattern recognition and synthesis, but always verify critical data points. Share your learnings and help your entire organisation level up.

But also, try using it for personal tasks and projects. Need to look something up and you’re heading over to Google? Try AI instead.

The Path Forward

Generative AI adoption has grown to 65% of organisations worldwide in 2024 according to Statista, representing a 30+ percentage point increase year-over-year. We’re witnessing the fastest technology adoption curve in history.

The question isn’t whether AI will change product management. It already has. The question is whether we’ll use it to amplify what makes us uniquely valuable: connecting technology possibilities with human needs, building bridges between vision and execution, and creating products that genuinely improve lives.

Soon, emphasising that something is “AI-powered” will seem as redundant as advertising a device as “electric-powered.” Just as electricity evolved from novelty to necessity, AI will simply become an expected part of any digital experience. We won’t highlight why a feature is smarter; we’ll simply expect it to be smarter. The transition will be subtle but significant.

The tools will evolve. New capabilities will emerge. But the fundamental shift has already occurred. We now have partners that help us see patterns in chaos, freeing us to focus on what matters most: building products people love.

Once you experience it, there’s no going back.