Last couple of months on Productify, I have been researching and writing about how Product Managers can start applying AI in their day-to-day work. If you missed the action, here are some links to what was published:
But so far, one thing that was missing was:
What does it mean for Product Leaders (PM Experience > 5 years)?
Product Leaders often operate at a different flight level, dealing with people management, product strategy and variety of product discovery and delivery challenges that impact multiple PMs and teams.
So today, we focus on 10 use cases for Product Leaders specifically using GenAI tools - each use case focuses on one aspect of product leadership challenge and provides how to approach, a list of recommended tools and sample prompts so you can get started immediately.
1️⃣ Market & Competitor Research – AI-powered insights to track trends & rivals instantly.
2️⃣ Prioritization & Roadmaps – Let AI rank features based on impact & effort.
3️⃣ Ideation & Brainstorming – Generate breakthrough product ideas in seconds.
4️⃣ User Research & Interview Prep – AI-crafted questions & role-play for deeper insights.
5️⃣ Customer Feedback Analysis – Turn raw feedback into actionable themes with AI.
6️⃣ Documentation – Instantly generate PRDs, acceptance criteria & release notes.
7️⃣ Meeting Summaries & Stakeholder Updates – AI-generated recaps & action items.
8️⃣ KPI Insights – Ask AI to surface trends & anomalies in product data.
9️⃣ Performance Reviews & Team Feedback – AI-crafted feedback for clarity & impact.
🔟 Hiring & Onboarding – AI-written job descriptions, interview Qs & onboarding plans.
1. Market and Competitor Research (Product Strategy)
Generative AI can rapidly gather and synthesize market data, competitor info, and industry trends. Instead of manually combing through reports and websites, an AI assistant can summarize customer needs, emerging trends, or competitor offerings. This helps product leaders identify opportunities or threats early. For example, AI tools can ingest news, social media, and reviews to highlight market sentiments and gaps. Product leaders can quickly get SWOT analyses or market sizing to inform strategy.
Recommended GenAI Tools:
Free/Premium: ChatGPT or Bing Chat (for asking questions and summarizing web info), Google Bard (free, with web access for up-to-date info).
Highly recommended for up-to-date research date: Perplexity
Enterprise: Crayon (AI competitor intelligence platform, Brandwatch or Sprout Social (AI-powered market research and sentiment analysis, AlphaSense (market intelligence with AI search).
Sample Prompt:
“Our product is a B2B payments SaaS. Give me a summary of the top 5 competitors in this space, including their key features, value propositions, pricing, and any weaknesses or gaps in their offerings.”
Use-case: A Head of Product at a fintech startup used ChatGPT to scan recent fintech reports and competitor websites. The AI generated a table of competitors, each with their core features and differentiators, which helped the PM leader spot a market segment none of the competitors were addressing. This informed a new strategic feature that became a competitive advantage. The AI also summarized social media sentiment about a rival’s new release, alerting the team to a negative feedback trend they could avoid in their own product
2. Prioritization and Roadmapping (Product Strategy)
AI can assist in prioritizing product initiatives by analyzing inputs like user value, effort estimates, and business impact. Product leaders can feed an AI with feature descriptions, customer impact ratings, and development effort, and get a draft prioritization (e.g. using RICE or MoSCoW frameworks).
Generative AI can also draft a high-level product roadmap, sequencing features by quarter or release. It considers market data and even predicts demand by analyzing trends. While the PM makes final calls, the AI saves time by offering an evidence-based starting point
Recommended GenAI Tools:
Free/Premium: ChatGPT (for prioritization reasoning or generating a roadmap outline), Excel with GPT plugins (to score features automatically), Notion AI (to organize and rank ideas in docs).
Enterprise: Productboard’s AI (AI suggestions in roadmapping, Aha! Roadmaps (has AI-powered idea scoring), or Jira Product Discovery (if integrated with AI for ranking). Some organizations also use Azure OpenAI to build internal tools that analyze feature data and suggest priority.
Sample Prompt:
“Here is a list of 10 proposed features for our SaaS product, with rough effort (1-5) and impact (1-5) scores: [feature list]. Using the RICE framework (Reach, Impact, Confidence, Effort), generate a ranked priority list with reasoning.”
Use Case: A VP of Product planning the next quarter’s roadmap used an AI assistant in Productboard. By inputting customer feedback volume and estimated effort for each feature, the AI highlighted which features might deliver the highest ROI. For instance, it suggested that a oft-requested Audit Trail feature should rank above a nice-to-have UI Theme Editor, given compliance importance in fintech.
The PM leader then asked ChatGPT to draft a roadmap timeline: “Q1: Audit Trail (compliance demand), Q2: Role-Based Access improvements, Q3: Theme Editor,” with justification. This matched the team’s instincts and provided a written rationale to share with stakeholders. The AI-driven roadmap was used as a starting point in planning discussions, saving hours of manual sorting and ensuring data-backed decisions.
3. Ideation and Concept Brainstorming (Product Discovery)
Generative AI is like an always-available brainstorming partner. Product leaders can use it to generate new product or feature ideas, or explore solutions to customer pain points. By describing your domain or a problem, the AI can suggest creative approaches or even adjacent industry ideas. For example, asking “How might AI improve our mobile onboarding?” could yield fresh concepts (gamified tutorials, AI chat assistants for setup, etc.). This is especially useful in early discovery when you want a broad idea funnel. ChatGPT can also combine knowledge of emerging tech with user needs to propose innovative features.
Recommended GenAI Tools:
Free/Premium: ChatGPT or Claude (for free-form brainstorming Q&A), Bing Chat (for idea generation with web context), Ideagram AI (text-to-mindmap tools). Even image generators like Midjourney can spark design ideas (e.g. concept art for a new feature’s UI).
Enterprise: Miro AI (brainstorming whiteboard that generates ideas on sticky notes), Coda AI (can suggest product concepts based on doc content), or internal innovation platforms augmented with GPT-4 to suggest ideas from internal data (hackathon ideas, etc.).
Sample Prompts:
“Brainstorm 5 new feature ideas for a personal finance app that help users save money in creative ways.”
“We have a user pain point: ‘manual data entry is tedious’. What product improvements or tools could solve this in our SaaS workflow software?”
Use-case: The product leadership at a SaaS startup held an “AI co-creation” workshop. Each PM entered a key customer problem into ChatGPT (“Customers forget to use our budgeting tool because it’s not engaging”). The AI proposed ideas like gamification with savings challenges, monthly progress reports with personalized tips, and integrating a virtual financial coach.
One prompt even asked for ideas combining emerging tech: “How could we use AI or blockchain to add value to expense tracking?”. The suggestions (e.g. an AI that auto-categorizes expenses and predicts future bills) seeded their quarterly ideation session. Many ideas were discarded, but a couple – such as the AI auto-categorization – were refined by the team and eventually added to the roadmap as innovative differentiators.
4. User Research and Interview Preparation (Product Discovery)
Product managers need to conduct user interviews and surveys to gather insights. GenAI can help at multiple stages: crafting unbiased interview questions, role-playing as a user for practice, and summarizing research findings.
For instance, you can ask AI to generate open-ended questions that avoid leading the interviewee (applying principles from The Mom Test for unbiased questions. You can also simulate a conversation by prompting the AI to “act as a customer” – it will respond with realistic answers, which can help a PM leader refine their interview technique or anticipate different user perspectives. This doesn’t replace real customers, but it’s useful prep. Post-interview, you might feed transcripts to an AI to extract key themes or quotes.