How I Built a Complete Product Strategy, Roadmap, and Prototype in One Day (Thanks, AI)
Why AI-powered strategy development is becoming every product leader's secret weapon
Peter Yang's confession hit me like a punch to the gut: "I don't know how to write strategy without AI anymore."
I stared at my screen, realizing he'd articulated something I'd been feeling but couldn't quite name. The old way of spending weeks crafting strategy documents, iterating through endless drafts, coordinating feedback loops—it suddenly felt antiquated.
So I decided to test just how far I could push AI as my strategic partner. The challenge: build a complete end-to-end product analysis for Sierra AI—from market strategy to working prototype—in a single day.
Spoiler alert: It worked. And it's changed how I think about product work forever.
And here’s a gif of robot consultants talking about AI made on Sora. The future really is now…
The Experiment: Strategy to Prototype in 24 Hours
Inspired by Claire Vo (CPO at Color) and Hilary Gridley (Head of Core Product at Whoop) on the How I AI podcast, I designed a three-step AI-native workflow:
AI-Powered Strategy Development → Custom GPT for deep strategic analysis
Strategy-to-Roadmap Translation → Second GPT for execution planning
Roadmap-to-Prototype → Code generation + rapid prototyping tools
The guinea pig? Sierra AI—a company I'd been following but had never worked with. Perfect for testing whether AI could help me understand and strategize for an unfamiliar business.
Step 1: Teaching AI to Think Like a Strategist
Building effective custom GPTs isn't about prompt engineering—it's about knowledge transfer. Here's what I learned:
The Setup Process (Easier Than You Think):
Head to chat.openai.com/create
GPT #1: Focused specifically on company strategy and SWOT analysis
GPT #2: Designed for product strategy and roadmap development (to consume the first GPT's output)
Upload your best previous work as training examples
Give each a strong persona with specific analytical frameworks
The Reality Check: This wasn't one-and-done. I had to iterate multiple times on each GPT, continuously prompting to specify the right output format, structure, and depth of analysis. The first few attempts gave me generic consulting-speak. It took several rounds of refinement—specifying exact section headers, requiring specific types of evidence, demanding concrete metrics—before each GPT consistently delivered results good enough for my purposes.
The Secret Sauce: I treated each AI like a junior strategist who needed clear direction and feedback. I layered in my own industry research, used Claude 4 for quality evaluation, and kept iterating until the outputs felt genuinely insightful rather than templated.
Sierra AI Strategy: Key Insights
The AI-generated strategy revealed Sierra AI's position at a critical inflection point in the conversational AI space. Rather than building another chatbot, they're positioning themselves as the infrastructure layer for enterprise intelligence—essentially becoming the "OS for AI agents."
Key strategic insights emerged:
Market timing advantage: While competitors focus on demos, Sierra can own the enterprise trust and reliability layer
Vertical-first approach: Deep industry expertise in e-commerce, healthcare, and finance creates defensible moats
Developer platform opportunity: Building the "Stripe for AI agents" with SDKs, observability tools, and monetization layers
Step 2: From Company Strategy to Product Execution
This is where the modular approach paid off. Instead of trying to do everything in one massive prompt, I had created that second specialized GPT focused purely on product strategy and roadmap development.
The beauty? The product roadmap GPT was specifically trained to consume company strategy and SWOT analysis as input, then translate those insights into execution-ready product plans. The handoff between GPTs maintained strategic context while producing actionable outputs.
But again—this took work. The first roadmaps were too high-level and generic. I had to keep prompting for more specific timelines, clearer success metrics, and better prioritization logic until it consistently delivered roadmaps I'd actually present to a leadership team.
Sierra AI Product Roadmap: The Agent OS Era
The AI-generated roadmap transformed the strategic insights into six concrete product bets:
1. Agent Observability as a Platform Primitive Build industrial-grade confidence into every conversation through session trace logs, confidence scoring, and hallucination detection. The goal: reduce hallucination rates to <1% while maintaining 80%+ CSAT.
2. Vertical Packs as Market Wedges Prebuilt workflows for e-commerce, healthcare, and finance with compliance built-in. Rather than horizontal plays, dominate specific verticals with deep expertise.
3. Developer-Centric Infrastructure SDKs, CLIs, and telemetry hooks that make Sierra the "Stripe for AI agents"—easy to integrate, powerful to scale.
4. Self-Optimizing Agents A/B testing and CSAT optimization built into the platform, so agents improve themselves over time.
5. Feedback Intelligence Engine AI-powered systems that collect customer signals and automatically surface insights to product teams.
6. Agent OS Vision The long-term play: orchestrate agents across tools and workflows, becoming the operating system for enterprise intelligence.
What Made This Roadmap Strong:
Specific success metrics: Hallucination rates, CSAT thresholds, developer NPS
Clear prioritization logic: Infrastructure first, then vertical expansion, then platform play
Measurable outcomes: Each bet tied to concrete KPIs rather than vanity metrics
Step 3: Making It Real
Here's where things got fun. I took the roadmap's key features and asked both ChatGPT and Claude to help me figure out exactly what to type into Lovable to build a compelling prototype.
This wasn't just "generate some code"—I needed specific guidance on how to structure the prompts for Lovable's AI-powered development platform. Both ChatGPT and Claude helped me craft the right technical specifications, user stories, and feature descriptions that would translate my strategic vision into actual working components.
The Result: A functional demo that brings the strategy to life. Not just slides and documents, but an interactive experience that shows what the vision could actually look like—transforming strategy into prototype in hours rather than weeks.
What Actually Happened (The Honest Version)
The Good:
Speed was incredible—what typically takes weeks happened in hours
Quality was surprisingly high with proper iteration
The end-to-end flow from strategy to prototype felt seamless
I learned more about Sierra AI in one day than weeks of traditional research
The Challenging:
Custom GPTs still need constant prompting discipline—they drift from format and lose persona
Claude 4 was better for evaluation and rubric-based scoring than ChatGPT
Hit usage caps on both platforms (probably a good problem to have)
Multi-tool workflows work but feel fragmented
The Missing Piece: Real internal data. This is the biggest limitation of the entire approach. No amount of AI sophistication can replace actual user research, growth metrics, customer feedback, and internal performance data. My analysis was necessarily market-reasoning heavy because I was working from public information and industry patterns.
Without access to Sierra's actual customer conversations, churn data, feature usage analytics, and user feedback, even the most sophisticated AI strategy work remains somewhat theoretical. The best product decisions come from understanding real user pain points, not just market positioning.
Why This Matters Beyond Cool Tech Demos
This isn't just about building faster—it's about thinking differently.
Traditional strategy development is often slow, consensus-driven, and risk-averse. AI enables a more experimental, iterative approach. You can test multiple strategic directions, validate assumptions quickly, and prototype solutions before committing significant resources.
For job seekers, this creates a new category of differentiation. Instead of just talking about what you'd do, you can show up with:
Deep strategic analysis of the company
Specific roadmap recommendations
Working prototypes of your ideas
The new interview strategy: Don't just apply—show what you'd build.
Try It Yourself
I'm sharing the full toolkit because I believe this approach could transform how product people work:
The Bottom Line
We're at an inflection point where AI can genuinely augment strategic thinking—not replace it, but accelerate and enhance it in ways that felt impossible six months ago.
I still believe in human judgment, customer empathy, and market intuition. But I also believe in leveraging every tool available to think faster, test ideas more rapidly, and build better products.
The old way isn't wrong. It's just slower.
Want the detailed breakdown of my GPT prompts and evaluation rubrics? Hit reply and let me know—I'm considering a follow-up deep-dive for subscribers who want to build their own AI strategy toolkit.
What's your experience with AI-powered product work? Have you found tools that have genuinely changed your workflow? I'd love to hear about your experiments in the comments.
Keywords: #ProductStrategy #AI #CustomGPT #ProductManagement #Innovation #StrategyDevelopment #AITools #Productivity #Sierra #TechStrategy #ProductDevelopment #FutureOfWork
Legal Disclaimer: Views expressed are my own and do not reflect those of current or past employers.