When generative AI first entered into the mainstream (late ‘22), product teams everywhere sprinted to embed it, often without stopping to ask if their roadmap was aligned with real user behavior or grounded in the practical realities of enterprise adoption. This is the third story in my series on GenAI product strategy and focuses on how integrate GenAI into your long term roadmaps. (p1: features, p2, team)
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Bottom line: We all need to do better.
Generative AI isn’t just another feature set—it’s a new interface paradigm, a new user expectation engine, and a new enterprise revenue center. It redefines how we interact with data, make decisions, and design product experiences. But without a tightly aligned strategy rooted in behavioral insight and organizational readiness, most GenAI investments risk becoming expensive detours.
As someone who has spent decades turning early-stage AI initiatives into scalable, defensible products—whether training LLMs to model supply chain risk or embedding conversational AI into regulated workflows—I’ve seen what works and what falls apart.
Let’s break down what GenAI roadmapping should really look like in 2025.
Step 1: Follow the Behavior, Not the Hype
The most overlooked input in GenAI roadmapping is not model performance or latency—it’s behavior. Users don’t really want to “interact with AI”; they want to get things done faster, smarter, and more intuitively. What we’re seeing in the wild supports this: usage patterns are shifting toward co-pilots and agents, not chatbots. Users want actionability with optionality.
Users are experimenting with prompts. Product teams are iterating midstream. Users expect AI to summarize, recommend, and personalize. Users are building mental models based on consumer experiences (ChatGPT, Claude, Gemini), and they’ll abandon your app the moment it feels dumb or less helpful.
Capturing this means looking beyond MAUs and NPS. It means analyzing prompt flows, abandonment points, decision friction, and trust gaps - basically look out for what your platform is NOT answering. If your roadmap isn’t anchored in that telemetry, it’s already drifting.
Step 2: Build Within Your Enterprise Lines
It’s easy to dream about “what’s possible with GenAI.” It’s harder to live inside the box of what’s permitted, performant, and payable.
Enterprises, especially in regulated sectors, are not rolling out wild-west AI. They’re setting boundaries around:
Data exposure: “Does this touch PII?” triggers audits—not applause.
Model selection: Self-hosted vs. API vs. fine-tuned? Each has radically different tradeoffs in control and cost.
Inference economics: LLMs aren’t cheap. Your pricing model had better account for that $0.003/token bill. (future article on the economics of GAI)
Security controls: Prompt injection isn’t just theoretical—it’s already in the OWASP Top 10.
A successful roadmap is constraint-aware by design. It doesn’t just ask, can we ship this? It asks, can we ship this responsibly, repeatedly, and in a way that supports IT, risk, and finance teams too?
Step 3: Rethink the Roadmap as a Living Signal Loop
The traditional product roadmap—a static sequence of quarterly feature drops—is officially dead in GenAI. In its place should be a continuous signal loop that looks something like the below (I can’t underline how important this is):
Proof-of-Concept – Can we build it?
Behavioral Validation – Do users want it this way?
Enterprise Hardening – Can we support it at scale, securely?
Outcome Optimization – Is it moving the needle?
Your roadmap should behave like an LLM prompt: adaptive, responsive, and guided by feedback. I’ve found success in mapping GenAI initiatives onto a maturity model—moving from simple augmentation to full agentic orchestration, but only after gating each stage through usage data and constraint testing.
Step 4: Define Success Beyond Clicks
If your KPIs for GenAI adoption start and end with engagement, you’re missing the plot.
The real value in GenAI comes from:
Time saved per task
Reduction in operational errors
Decrease in human review cycles
Confidence-weighted suggestions that improve over time
Lower support tickets or training costs due to intuitive UIs
At one SaaS platform I advised, we introduced a GenAI-powered triage layer. Users didn’t just use it—they trusted it. And the metric that mattered most? A 40% drop in time-to-mitigation across high-severity risk events. That’s ROI you can bring to the boardroom.
TLDR: Be Humble, Be Iterative, Be Ready to Pivot
Product teams too often fall in love with the GenAI “moment.” But this isn’t a one-and-done moment—it’s a motion. A shift in how users think, how enterprises adopt, and how product leaders must respond.
If you’re not pairing every big-bang LLM release with real-time behavioral insight, adaptive guardrails, and constraint-sensitive metrics, you’re just chasing demos.
Instead, build for evolution. Build for trust. And above all, build for actual users—because their expectations are changing faster than your quarterly plan ever could.
Inspiration and Attribution
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AI or Die, Ravi Gupta, Anthropic
McKinsey & Co. (2023). The State of AI in 2023
BCG (2024). The GenAI Product Playbook
Andreessen Horowitz (2023). GenAI UX Patterns
Gartner (2024). Strategic Tech Trends: Democratized AI
Harvard Business Review (2023). KPIs for AI