Introduction: From Buzzwords to Business Value
It always starts the same way.
A casual message in a team channel.
Someone drops a link to a GenAI demo.
Another chimes in: “Can we do this for support?”
A third wonders if marketing could use it to generate campaigns.
Then someone asks, half-seriously, “What if our product had a copilot too?”
Suddenly, it’s everywhere. GenAI becomes the unofficial agenda in product meetings, hackathons, even 1:1s. Everyone wants in. No one’s sure where to begin.
This isn’t just a tech trend, it’s a wave. And as a product leader, you’re expected to do something about it. But what exactly? Automate a process? Launch a new feature? Replace existing UX with AI-powered magic?
Here’s the truth: AI is not the strategy. Solving real problems is.
But figuring out where AI actually helps and what’s just shiny-object noise is the hard part.
That’s why I built a simple, repeatable framework to help product teams identify, evaluate, and prioritize GenAI use cases that drive real business value. Not just because it’s trendy, but because it’s useful, feasible, and justifiable to your exec team.
In this post, I’ll walk you through:
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The 4-part PAVE framework to score and compare AI opportunities
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A one-page AI Canvas to help you think through a use case from end to end
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A simple ROI model to back up your ideas with data (because yes, your CFO will ask)
Whether you’re leading a B2B SaaS platform, a consumer app, or internal tools for your enterprise, this guide will help you cut through the noise and actually move.
Let’s dive in.
Why AI Needs a Product Mindset
Let’s get one thing out of the way: AI isn’t a magic feature. It’s a tool, one that needs a clear purpose, thoughtful design, and measurable outcomes.
Too often, teams approach AI like it’s a novelty. “Let’s sprinkle in some GPT and see what happens.” But that’s a fast way to burn time, budget, and trust especially with stakeholders watching closely.
What’s missing?
A product mindset.
The same mindset that guides every great product decision:
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Who is this for?
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What problem does it solve?
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How will we know it’s working?
That’s where AI needs to live, not in the R&D corner or the lab, but in the heart of product thinking. Because the most successful AI efforts aren’t just clever, they’re useful. They drive efficiency, improve experience, unlock new capabilities, or create real value for users.
Product leaders are uniquely positioned to make that happen. You know how to balance user needs, tech feasibility, and business priorities. Now it’s time to apply that same discipline to AI.
This isn’t about building an “AI strategy.”
It’s about embedding AI into your product strategy the same way you’d think about mobile, cloud, or APIs.
And like any feature, AI should earn its place.
Common Traps to Avoid
And yet, even with the right mindset, it’s easy to fall into traps. I’ve seen well-meaning teams waste months on AI experiments that go nowhere, not because the tech didn’t work, but because the problem wasn’t worth solving, or the user never asked for it, or worse, the outcome couldn’t be measured.
If you’re just starting to explore AI use cases or trying to rescue ones that stalled, watch out for these common missteps.
1. Starting with the model, not the user
The most common mistake I see: teams begin with the tech. “We have access to GPT-4; what can we do with it?” It feels exciting, but it’s backwards. Start with a user pain. A job to be done. A workflow that’s clearly broken. Then ask: “Could AI make this meaningfully better?”
2. Building for demos, not outcomes
It’s tempting to chase that magical GenAI demo, the kind that gets applause in town halls and investor decks. But what happens after the applause? Does it get used? Does it change anything? If your success metric is “we built it,” you’re thinking like a lab. Instead, define success like a product leader: usage, adoption, efficiency, retention.
3. Ignoring the data reality
AI lives or dies by data. Some use cases seem brilliant on paper until you realize your data is messy, unstructured, or scattered across 17 tools. Before you commit, ask: Do we have the right inputs to make this work reliably?
4. Underestimating change management
AI can be intimidating. It alters workflows, raises concerns, and sometimes triggers resistance from the people it’s meant to help. Don’t assume “smart” equals “adopted.” The best GenAI features come with onboarding, context, opt-outs, and trust built in.
5. Trying to “AI all the things”
Not everything needs AI. Some use cases are better solved with filters, rules, or good UX. Over-AI-ing your product leads to bloat, confusion, and maintenance nightmares. Treat AI as a scalpel, not a sledgehammer.
The takeaway: AI success isn’t about the model. It’s about product thinking.
And that means picking the right use cases, solving the right problems, and doing it in a way that drives clear, measurable value.
So how do you figure out what’s worth doing?
Let’s find out the use cases.
Discover Use Cases
Once you avoid the common traps, the next question is: where do we start?
The good news is, you probably don’t need to look very far.
Most teams already have a dozen viable AI use cases hiding in plain sight. The key is knowing how to spot them and framing them in a way that gets buy-in from both your team and your leadership.
Here are three reliable ways I’ve seen product teams surface high-value AI opportunities:
1. Look for friction
Start with the messy stuff. The repetitive, manual, error-prone parts of your product or business. These are often great candidates for AI-driven automation or summarization. Think: support agents triaging tickets, users writing similar queries over and over, operations teams stuck in spreadsheets.
Ask your team:
“What’s something we do every day that feels dumb, repetitive, or painful?”
You’ll get gold.
2. Mine the “wish list”
Talk to your sales engineers, your support leads, your PMs. Ask them:
“What do customers keep asking for that we’ve never had the time or resources to build?”
Some of those wishlist items like personalized recommendations, natural language search, or insights from unstructured data are suddenly feasible with GenAI. What was hard or expensive two years ago may now be a weekend prototype.
3. Shadow the user
One of the most underrated discovery tactics: watch people work. Sit in on a live onboarding. Listen to support calls. Observe how users complete a task in your app. You’ll see where they hesitate, where they switch tabs, where they copy/paste. AI thrives in these gaps.
You’re not looking for “cool AI ideas.” You’re looking for real problems that AI might solve better than current solutions.
Once you’ve surfaced a few opportunities, you’ll need a way to evaluate them quickly and clearly.
That’s where the PAVE framework comes in; your go-to tool for deciding which GenAI use cases are actually worth building.
Prioritize with the P.A.V.E. Framework
Once you’ve surfaced a handful of promising AI ideas, the real work begins: figuring out which ones are actually worth building.
Not every use case is created equal. Some solve real pain. Some are better suited for classic software. Some sound exciting but deliver very little impact when they ship. Without a structured way to vet them, it’s easy to get lost or worse, to waste months on shiny demos that don’t move the needle.
That’s why I created the PAVE framework.
It’s a simple, battle-tested way to quickly evaluate GenAI use cases across four dimensions:
P — Pain
How real and acute is the problem you’re solving?
Is this a “nice to have” or a “drop everything and fix it” kind of issue?
The more painful the problem, the more likely your AI solution will drive adoption and excitement. Low-pain problems usually lead to low usage, no matter how clever the tech.
Gut check:
“Would someone actually notice if we solved this tomorrow?”
A — AI Fit
Is AI actually the right tool for this?
Some problems are perfect for GenAI: unstructured text, summarization, personalization, classification, predictions. Others are better solved with good UX, filters, or rules engines.
Gut check:
“Is there something fundamentally fuzzy, pattern-based, or language-driven about the task?”
If it’s clear-cut and deterministic, AI might overcomplicate things instead of helping.
V — Value
What’s the business impact if we get this right?
Value can show up as increased revenue, improved retention, reduced costs, faster workflows you name it. But it needs to be tangible. And how wide is the reach? You’re not looking for abstract benefits like “better vibes.”
Gut check:
“If we solve this, how will it show up on a dashboard the CEO actually cares about?”
E — Effort
How hard will this be to build, integrate, and maintain?
Some GenAI projects sound easy (“just call an API!”) but hide brutal edge cases under the hood like hallucinations, privacy issues, ongoing model tuning. Others can be surprisingly lightweight if you scope smartly.
Gut check:
“Can we ship a first version in 4–8 weeks without needing a standing army?”
When you stack your ideas against Pain, AI Fit, Value, and Effort, patterns emerge fast.
Some ideas light up green across the board: build these first.
Others look exciting but flunk Pain or Value: rethink or park them.
Some seem promising but are massive lifts: consider breaking them down.
PAVE helps you stay disciplined. It keeps you focused on what matters: real users, real problems, real impact—not just building AI for the sake of it.
And once you’ve identified a few PAVE-approved ideas? That’s when the real fun begins: prototyping, testing, learning fast, and scaling what works.
Flesh Out the Idea with the AI Canvas
By now, you’ve surfaced your top GenAI opportunities using the PAVE framework. Now it’s time to zoom in and flesh out the details.
This is where the AI Canvas becomes your best friend.
Think of it as a practical, one-page cheat code to align your cross-functional team from product to engineering to design to execs. It brings structure to what’s often a fuzzy conversation. No more hand-wavy “let’s throw ChatGPT at it” vibes. This is about clarity.
Here’s what each box in the canvas actually means, and why it matters:
1. Problem / Opportunity (1–5)
Start here, always. What’s the real user pain, inefficiency, or business opportunity we’re going after? Get specific. If this box is vague, nothing else will save you. You’re not building with AI, you’re solving a problem using AI.
2. Target Users / Stakeholders
Who actually benefits? Internal teams? End-users? Specific roles (like underwriters, recruiters, claim adjusters)? This helps you stay focused on who the feature is for—and who’ll care enough to use it.
3. Proposed GenAI Solution
What does the AI actually do? Summarize, classify, generate, rank, recommend? Be concrete. “AI magic” doesn’t fly here. Describe the core functionality you’re imagining.
4. Desired Outcomes / Success Metrics
How will you know if this works? Think adoption, efficiency, experience. CSAT, NPS, time saved, call deflection, increased conversion whatever’s relevant. Bonus: define what “bad” or “neutral” would look like too.
5. AI Fit Assessment (1–5)
Why does this problem need AI at all? Maybe the task is unstructured, repetitive, data-heavy, or language-based. If the solution doesn’t require intelligence or interpretation, a simple rule-based system might be better. This is your litmus test.
6. Technical Feasibility
Can you even build this? What models or toolkits might you use? Does your team have the data? Is the data good enough? Data is key to any feature. The team needs to dig deeper to get a handle on the data.
7. Risks & Constraints
What could go wrong? Hallucinations? Bias? Privacy breaches? Latency? Accuracy concerns? Trust issues? GenAI brings real power but also real risk. Calling these out early shows maturity and earns trust with leadership.
8. Level of Effort (1–5)
How hard is this to implement? Estimate dev lift, integration complexity, tuning needs, and UI changes. A 1 might be a simple prompt over existing data. A 5 might mean RAG, custom UI, human-in-the-loop validation, and more.
9. Business Value (1–5)
How big is the upside if this works? Think cost savings, revenue growth, competitive advantage, or experience boost. A 5 is a clear game-changer. A 1 might just be a nice-to-have that won’t move the needle.
10. PAVE Score Summary
Bring it home. Use the PAVE framework to summarize the opportunity’s strength:
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Pain (Is it a real, felt user pain?)
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AI Fit (Is this a natural fit for GenAI?)
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Value (Will it impact the business?)
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Effort (Reversed! Lower effort is better.)
Total PAVE Score = Pain + AI Fit + Value + (6 – Effort)
You’ll never make perfect trade-offs, but this math helps you spot obvious wins and avoid shiny distractions. If a use case has a low PAVE score… shelve it and move on.
The magic of the AI Canvas isn’t just in how it helps you think, it’s in how it helps your team think together. Use it in planning meetings, roadmap reviews, AI strategy docs. Make it a living artifact.
And pro tip: The canvases get filled out in dozens, fast. The trick is to quickly zero in on the few that matter.
Here’s the AI Canvas template: Canvas link and a sample Canvas: Clinical Documentation Canvas
Estimate ROI
Now that your GenAI idea is structured and scoped, it’s time to answer the one question every exec will (rightfully) ask:
“What’s the ROI?”
Because let’s be honest, cool doesn’t equal valuable. And just because GenAI is the hottest thing in tech doesn’t mean it earns a seat at your roadmap. You need to show the math.
But the good news? You don’t need an MBA or a finance team to get directional clarity. Here’s a lightweight ROI formula I’ve used with product and engineering teams to quickly sanity-check GenAI initiatives:
ROI = (Revenue Uplift + Cost Savings + Time/Productivity Gains) – (Build + Run Costs)
That’s it.
Let’s break it down, with examples:
Revenue Uplift
Could this feature drive more revenue? Examples:
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Increasing conversion rates through better product recommendations
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Upselling users through personalized insights
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Retaining customers longer with smarter support
Even a 1% lift in a high-volume flow can move real numbers.
Cost Savings
Could this reduce spending somewhere? Some ways GenAI can save you money:
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Automating support tickets (deflecting human interactions)
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Accelerating claims processing or document review
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Replacing outsourced data entry or QA
This is often the most immediate win, especially for operational teams.
Time / Productivity Gains
This is the most common and hardest to quantify. Try to translate saved hours into dollars or redeployable capacity. Example:
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GenAI writing meeting summaries = 10 hours saved per week × $100/hr × 50 weeks = $50,000/year
It’s not just about time saved, it’s about freeing up people to do higher-leverage work.
Build Costs
How much will it cost to build the MVP? Factor in:
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Engineering/design time
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Prompt tuning / evaluation work
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Internal coordination costs
Run Costs
This includes things like:
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API/model costs (OpenAI, Claude, etc.)
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Monitoring/logging infrastructure
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Periodic prompt tuning or re-training
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Human-in-the-loop workflows (if any)
Multiply the per-call model cost by expected volume. It adds up quickly especially with image or multi-modal models.
Optional but Powerful: Payback Period
If you really want to impress your CFO, add this:
Payback Period = Build + Run Costs ÷ Monthly Savings or Revenue Lift
If your initiative pays for itself in under 6 months, you’re in great shape. If it takes 2 years… maybe rethink.
Start Small, Measure, and Scale
By now, you’ve got a solid GenAI use case. You’ve scoped the opportunity, modeled the ROI, and probably started sketching out the build in your head. The temptation at this point? To go big. To rally the whole team, build a robust v1, and “launch AI” at your company.
Resist that urge.
The companies doing GenAI well aren’t the ones boiling the ocean. They’re the ones that start with something narrow, useful, and measurable and use those wins to earn the right to go further.
Here’s a simple playbook I’ve seen work across teams and industries:
Start with 1–2 quick wins
Look for use cases that are low-effort, low-risk, and high-visibility. The kind of things that take a few weeks to ship but make people say, “Oh wow, this is actually useful.”
Some good candidates:
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Auto-generating summaries of support chats
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Categorizing user feedback or reviews
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Writing onboarding copy with a human-in-the-loop
Don’t worry if it’s not flashy. The goal here is to show value, not wow with tech.
Pick 1 strategic bet
While quick wins buy you credibility, a bigger, more strategic use case can buy you leverage. This could be something tied to revenue growth, cost reduction, or core product differentiation but still scoped small enough to build a v1 in a few months, not quarters.
You don’t need to bet the farm. You just need to start the learning loop.
Ship, measure, learn
Set clear metrics (remember the GenAI Canvas?) and make sure you’re set up to track them. The key question to ask: Did this change behavior or outcomes in a meaningful way?
That could mean higher engagement, fewer tickets, faster workflows, or better CSAT. Don’t just look at usage, look at impact.
And share what you learn. Create a short Loom, show a before/after, or drop a one-pager in Slack. GenAI can be intimidating to non-technical teams. Your job is to make it feel real, useful, and safe.
Build trust incrementally
Trust is the real currency in any AI rollout. Trust from your team that you’re not chasing hype. Trust from leadership that this won’t blow up in their face. Trust from users that what you’re building won’t hallucinate its way into chaos.
You earn that trust by:
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Being transparent about what the AI can and can’t do
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Having humans in the loop early on
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Designing for reversibility; start with opt-in, not forced automation
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Fixing what breaks, quickly
Turn wins into momentum
Once you have a few small wins under your belt, things start to change. People start Slack DMing you with their own GenAI ideas. Execs bring it up in all-hands. Engineers start prototyping things on their own.
That’s your cue to scale. You’ve proven value, built internal momentum, and established a track record of delivering. Now, you can start thinking about deeper integrations, dedicated teams, or platform investments.
But you got there not by launching AI across the company but by shipping one helpful, boring, high-leverage thing at a time.
Conclusion: Your Role as a Product Leader
If you take just one thing away from this, let it be this:
AI isn’t a research problem anymore. It’s a product opportunity.
We’re past the phase of speculative excitement and into the phase where real companies are building real products, shipping them fast, and capturing real value. And who’s best positioned to lead that work?
You are.
Not the data scientist. Not the AI strategist. You, the product leader who knows your customers, knows your systems, and knows how to ship.
You don’t need a PhD in machine learning to start. What you need is the same muscle you’ve always used: identifying problems worth solving, testing solutions quickly, and shipping value. GenAI just adds a new set of tools to your toolbox. A powerful new set, sure. But still just tools.
The real differentiator isn’t the model you pick. It’s your judgment. Your taste. Your ability to find that small but magical use case that everyone else missed.
The companies that win in this next wave of AI won’t be the ones that threw the most money at it. They’ll be the ones where someone like you rolled up their sleeves, picked a problem that mattered, and made something useful.
So don’t wait for a mandate. Don’t wait for a tiger team or a roadmap.
Pick one use case. Fill out the canvas. Score it with PAVE. Build something small. Measure the impact. Share it with your team.
And just like that, you’re no longer watching the GenAI wave.
You’re riding it.