How AI Prototyping Is Changing Product Development: Our Scars and Lessons
We moved functional prototypes ahead of parts of the traditional PRD. The speed is real, but so are false signals, rework, and cost. Here is what we now do differently.
We moved functional prototypes ahead of parts of the traditional PRD. The speed is real, but so are false signals, rework, and cost. Here is what we now do differently.
Part 2 turns the ownership problem into a practical framework: what should become memory, how to keep source truth separate, and how to test whether another model can use the same company knowledge.
We spent years trying to stop AI from forgetting the company. Now memory is improving fast, and a new problem appears: where does that accumulated company learning live, and can you keep it when the model changes?
Optimum Partners joined the Technology Association of Georgia’s first //SHIFT: AI Summit for a panel on operationalizing AI across the enterprise. CEO Americas Joe Sticca laid out the questions that decide whether an AI workflow earns a permanent place in the business: ownership, error handling, human oversight, and true cost per run.
Everyone is doing some version of the same exercise right now. We are putting AI into workflows, automating work that used to sit with people, and sooner or later somebody asks the question that decides whether the whole thing keeps moving: is this actually worth it?
One AI agent can carry a surprising amount of work. The problem starts when one request branches into several different jobs. This piece explains the orchestrator worker pattern, clears up agent versus worker, and shows where dividing the work improves the system.
Research on enterprise AI adoption puts the real cause of implementation trouble at 56 to 64% human factors, not technical ones. One single issue, user proficiency, accounts for 38% of that on its own.
We’ve built AI directly into processes running on top of exactly this kind of mess, in both, without tearing out what already works. Ripping out a system that’s run a business for fifteen years, just to
A document doesn’t stay one file once AI touches it. It gets cut into smaller passages, chunks, and each chunk becomes an embedding, a string of numbers capturing what it means, so the system can find it later by meaning, not keyword.
