OpenAI Product Leader: The 4D Method to Build AI Products That Users Actually Want
An OpenAI product leader's complete playbook to discover real user friction, design invisible AI, plan for failure cases, and go from cool demo to daily habit
Dear subscribers,
Today, I want to share a free deep dive on how to build AI products that users want.
The graveyard is littered with shiny AI products that don't solve real problems or have the rigorous data and evals in place to be truly differentiated.
That’s why I’m excited to invite Miqdad, product leader at OpenAI and ex-Director of AI at Shopify, to share his 4D method to build AI products in this free deep dive:
Discover: Find the boring, high-friction workflows
Design: Make AI invisible and trustworthy
Develop: Build systematically and plan for failure
Deploy: Treat every first-use like a launch
Miqdad also teaches a 6-week live cohort course, the AI PM Certification (the next cohort starts October 11th). I’ve personally taken this course and have referenced the material many times when building AI products.
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The 4D method to build AI products
The world doesn't need another AI chatbot. Instead:
People want "invisible" assistants that save time on their most tedious workflows.
After teaching 2,000+ students and shipping AI features that millions use, I've found that successful AI products follow a specific pattern. I call it the 4D Method: Discover, Design, Develop, Deploy.
Let me walk you through each phase and the tactical steps that make the difference between a cool AI demo and something users rely on daily.
At the end of this post, I’ll also share a link to an AI feature design checklist that you can download to put these steps into practice.
1. Discover: Find the boring, high friction workflows
The best AI products remove friction from core, boring workflows users already have. People don't care that you're using GPT-4 or GPT-99. They care that something annoying just got easier.
a) Find friction points in 5 steps
This is the systematic method I teach every PM to surface real user friction:
Pick one core persona. Consider starting with your highest revenue-generating user segment. Focus beats trying to serve everyone.
Map their user journey. Break it into 3–5 high-level stages like "Discover → Evaluate → Purchase → Use." Keep it simple so you see the forest, not the trees.
List tasks in each stage. Get specific about what your users are actually doing. Document the steps that they need to take to achieve their goals.
Identify friction points. Look for moments where users slow down, get confused, or complain. Where do they copy-paste between tools? What takes longer?
Prioritize high-impact friction points. Focus on friction points that happen frequently, affect many users, and align with business goals. A task that saves 30 minutes for 1,000 daily users beats one that saves 2 hours for 10 monthly users.
b) Validate the friction points using multiple sources
Here are some good sources to find friction points:
Search support tickets. Filter and set up saved searches for phrases like "annoying," "confusing," "takes too long," "this is frustrating," or "why can't I." Look for escalated tickets as they usually signal broken workflows.
Run a short user survey. Ask: "What's the most frustrating part of your daily workflow?" Follow up with "How much time does this waste per week?" Send it to your most active users first as they’ll give you the most detailed answers.
Watch session replays of power users. Look for pause, backtrack, and copy/paste moments. When users stop for more than 10 seconds, that's friction. When they copy data between screens, that's an AI opportunity.
List each JTBD and match with high-friction steps. Break down what users are trying to accomplish, then map where they struggle. For example: "Understanding what happened in a meeting" = digging through 60-minute recordings, reading notes, and more. Each painful step is an AI opportunity.
Talk to your customer success team. They hear complaints all day. Ask: "What do users repeatedly ask for help with?" and "What processes do you walk them through most often?" Those repetitive explanations are AI opportunities.
Above all:
With AI, remember that the magic happens in the mundane.
For example, if your sales reps are complaining about spending 30 minutes after every call just writing summaries, that's where you should start.
Instead of building a chatbot no one asked for, create an AI feature that auto-generates summary emails from transcripts:
Show reps how it saves them 25 minutes per call and reduces burnout. Then adoption becomes a no-brainer.
2. Design: Make the AI invisible and trustworthy
Don't expect busy users to learn new processes or to interact with a chatbot that’s off to the side. Your job is to make things they already do faster.
The best AI features are imperceptible. Users don't think, "I'm using AI now," they just think, "This got easier."
a) Turn AI into a smart shortcut
Gmail's Smart Compose nails this. Hit "Tab" to accept or keep typing to ignore:
How to make your AI product feel like a shortcut:
Tuck AI behind existing buttons. Upgrade what users already click ("Add filter" now offers AI suggestions) instead of adding new options.
Anchor actions to verbs, not nouns. "Summarize notes" feels more enticing and outcome-based than "AI Assistant."
Make AI the default. Start with the AI version of your feature, but make it easy to edit or opt out.
Offer one-click previews. Help users feel in control with quick "Undo" or "Show draft" buttons.
If users need to learn how to use your AI, you've already lost them. Make it feel like a cheat code inside their existing workflow instead of a whole new product to adopt.
b) Build graceful fallbacks with the CAIR equation
Your AI will get it wrong. It'll get confused by edge cases. Rate limits will hit you when you least expect it. If you don't guide users when that happens, they'll stop trusting and using it.
The CAIR equation is my go-to tool for deciding how much to invest in a fallback:
CAIR = Perceived Consequence of Error × Effort to Correct
Use this as your decision matrix for fallback complexity:
3. Develop: Build systematically and plan for failure
Most teams rush from prototype to production without the discipline that makes AI features reliable.
Systematic evaluation isn't optional—it's what separates demos from products people actually use.
a) Build systematically, not randomly
At Shopify, we learned that changing a single word in our prompts meant the difference between 30% and 35% of tests passing. But it wasn't the same 35 tests—different words broke different scenarios.
Without systematic tracking, we would never have known if our changes were making the differences we thought they were making.
Here’s what you can take from this:
Create test scenarios before you start. Run every prompt variation against this same set so you can actually compare results, not just gut feelings.
Version control every prompt change. Use notebooks or tools that let you test all variations simultaneously. Track what breaks when you optimize for something else.
Plan for months of post-launch iteration. Budget time and resources for extended learning periods. AI features need more refinement than traditional features based on real user patterns.
As you're developing, pressure-test with a lightweight AI PRD that answers:
What task are we automating?
How painful is that task today?
What does success look like? (e.g., 50% repeat usage)
What are our most significant risks? (e.g., hallucinations, bad UX, zero trust)
b) Plan for failure
Don't stop at the ideal flow. Write out your fail states, too.
What happens when the AI can't generate anything at all? How does the UI recover? Who owns the fallback experience?
With AI, you earn trust in failure moments.
Design clear guardrails, quick recovery, and user control into your V1:
Add graceful fallbacks. "Sorry, I couldn't generate that. Want to try again?”
Bake in feedback loops. A simple "Was this helpful?" with options like "Too generic" or "Wrong info" helps fine-tune prompts.
Log every rejection. Weekly AI bug triage with design and data teams prevents issues from escalating.
4. Deploy: Treat every first-use like a launch
First impressions with AI are brutal. If users don't "get it" in 30 seconds, they won't try again. Worse, they'll stop bothering with anything labeled "AI."
Your first-use flow needs as much love as designing the feature itself.
So treat every AI feature launch like a product launch—because it is one. What would you do to ensure early users get value instantly?
a) Run internal stress tests
Put teammates who didn't build the feature in front of it. Watch where they stumble, and fix those moments.
This sounds simple, but it's incredibly revealing. The people who built your AI feature know exactly how it's supposed to work. Fresh eyes will find every confusing interaction, unclear instruction, and moment of hesitation.
Here's how to run it:
Pick the right testers. Choose colleagues who match your target users but weren't involved in building the feature. If you're targeting sales reps, find actual sales people in your company.
Give minimal context. Don't explain how it works or what to expect. Just give them the scenario: "You need to create a product description for this item."
Watch, don't help. Resist the urge to jump in when they struggle. Note exactly where they pause, backtrack, or look confused.
Document everything. Create a Notion doc with GIF demos, key use cases, and common questions. Your support team will thank you later.
Form a beta squad. Include people from support, sales, and customer success—they hear user pain points first and can collect qualitative feedback.
Fix before you ship. These aren't nice-to-haves. If your internal testers struggle, your real users will abandon the feature entirely.
b) Onboard with instant value
Forget tutorials. Users don't want to learn, they want to accomplish something. So your AI must deliver the "aha" moment immediately.
Instead of explaining capabilities, demonstrate them:
Provide clear scaffolding. Add suggested prompts, "Try this" buttons, and mini-examples to help users take that first confident step.
Set expectations up front. Show what the AI can and can't do using copy or visuals. Don't make people guess.
Design for fast wins. Help users complete a fundamental task in the first 30 seconds. Then reinforce the results with feedback like "🎉 Created in half the time!"
Make success obvious. When the AI delivers value, make sure users notice. Highlight time saved, quality improvements, or tasks eliminated.
Partner with Customer Success. Run a 15-minute "AI kickoff" session with your top users. They become early advocates and provide real-world feedback before broader rollout.
Even a simple "beta" copy at launch can reset expectations. For example, the green label next to Spotify's AI Playlist signals, "This is new. It might mess up. Tell us how it goes."
c) Measure what actually matters
I think of clicks like curiosity: someone was interested enough to try the feature. Repeat use tells you they found value.
Don't fool yourself with engagement metrics. Adoption happens when your AI actually makes people's jobs easier. Everything else is noise.
Before you build anything, get your team to agree on what success looks like. Then build your metrics around those answers:
How to implement this:
Write success metrics into your PRD before engineering starts. This gives your eng and data teams something measurable to track post-launch.
Track real impact, not just engagement. Focus on task completion, time saved, satisfaction, not clicks and hovers.
Use these metrics to refine your UX and prompt design. If people delete every AI output, you have a UX or model problem.
Your first-use experience is your adoption strategy.
Test onboarding like you test features and keep rollouts slow. I've seen teams rush AI features to everyone and spend months rebuilding trust.
Start small, get it right, then scale.
Your downloadable AI feature design checklist
The actual test of any AI feature is regular use. If yours disappeared tomorrow, would anyone notice? Most teams focus on making their AI impressive. The teams that succeed focus on making it indispensable.
Everything we've covered boils down to the 4D Method and the core frameworks you can use immediately. I've put together a one-page checklist that captures the essential tools from this playbook.
Print it out, share it with your team, or keep it handy for your next AI feature:
Inside, you'll find:
The 5-step friction-finding process to identify high-impact AI opportunities
The CAIR equation decision matrix for building the right level of fallback
First-use stress test questions to ensure your AI delivers value in 30 seconds
Start with actual problems. Make the solution feel invisible. Plan for when it breaks. And measure what actually matters.
Do that, and you'll build AI that people rely on, not just try.
If you enjoyed Miqdad’s deep dive, be sure to check out his 6-week live cohort course, the AI PM Certification (next cohort starts October 11th).
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Neat article! Love the sections on validating friction points and building graceful fallbacks.