"I've been chasing the wrong problem for two years. Like many engineers captivated by AI's potential, I initially saw workflow automation as the solution—imagine an AI system that could draft our proposals and accelerate our entire business development process at Product Creation Studio. The vision was seductive: decades of project patterns synthesized into intelligent automation.

The reality was humbling. When I presented my AI-generated proposals to the team, their resistance was immediate and instructive. 'This suggests deliverables we've never offered,' one engineer observed. The AI was over-generating, producing plenty of contentbut missing the nuanced context that separates viable projects from risky ones.

Granted, there were opportunities for further tuning, but my team was more focused on getting to the relevant truths about the opportunity than they were at fixing the overly verbose agent. What they really wanted wasn't an automated workflow, but access to the right context to make their own decisions rapidly. Teams want to come up to speed themselves before committing their reputation to a delivery promise.

This insight led to Project Nancy—not a workflow automator, but a context orchestrator. Still in development, Nancy applies model-based intelligence to how the project data is stored and retrieved, applying multiple database technologies in unison to increase the relevant insights that are returned. She's not replacing human judgment—she's amplifying it with the right context at the right moment. And that shift from automation to amplification has fundamentally changed how I think about AI's role in product development."

Scott Thielman