Evidence gates

Start with the source

AI can turn incomplete information into a fluent recommendation. Before that recommendation shapes a roadmap, I want to trace the consequential claims back to the research notes, technical documentation, or customer evidence behind them. An inference should remain visible as an inference.

Ask what changes for the customer

A capability needs a place in an actual workflow. Who would use it? Which task would change? What evidence connects the proposed change to a customer need? Those questions require discovery and judgment, even when AI helps organize the material.

Make constraints part of the comparison

My GeoAI assessment compared 15 capabilities against customer value, technical readiness, and operational constraints. Using the same criteria made the options easier to compare. It also kept a promising demonstration from becoming the entire argument for product investment.

A useful assessment explains where a constraint changes the recommendation. That gives the team something specific to investigate.

Name the next decision

A prioritized opportunity can support further investigation without implying that a prototype exists or a release is ready. The GeoAI work produced an assessment and a prioritized view of the top opportunities. Research and evaluation were the scope.

Before sharing a recommendation, I check that the language matches the evidence and makes the next decision clear. A human remains accountable for the priorities and the words that reach a customer.