The context problem
Most teams approach AI as a prompt engineering challenge. They iterate on inputs, refine instructions, and measure outputs. But this misses something fundamental: AI doesn't operate in isolation. The quality of AI output depends entirely on the context it has access to.
In product discovery, this becomes especially critical. When AI lacks shared semantic context—understanding of your users, your product, your goals—it can only produce generic responses. Useful, perhaps, but not strategic.
Three emerging AI workflows
We're seeing three distinct AI workflows emerge in product discovery:
Context-Rich Discovery AI systems that have deep understanding of user research, product history, and strategic goals. These systems don't just answer questions—they connect insights across domains.
Iterative Refinement Workflows where AI output improves through cycles of feedback, with each iteration building on shared context from previous sessions.
Collaborative Intelligence AI that acts as a strategic partner, maintaining context across conversations and projects, learning from team decisions and outcomes.
Measuring success beyond output
Traditional metrics focus on output quality: Is it accurate? Is it useful? But when AI becomes a strategic partner, success looks different.
Context Retention Does the AI system remember and apply insights from previous sessions?
Strategic Alignment Are AI suggestions aligned with product goals and user needs?
Velocity of Insight How quickly does the team move from question to actionable insight?
The organizations that measure these outcomes—not just output quality—are the ones turning AI from a productivity tool into a competitive advantage.
Building shared semantic context
The shift from prompt engineering to context engineering requires intentional design. It means:
Structured Knowledge Organizing research, decisions, and outcomes in ways that AI systems can understand and reference.
Continuous Learning Systems that improve not just from better prompts, but from accumulated context over time.
Human-AI Collaboration Workflows designed for partnership, where AI amplifies human judgment rather than replacing it.
This isn't about better AI. It's about better systems for thinking together.