Original guides by Ratipcan Uysal
Work through the decision, not around it.
Research-grounded frameworks for consequential product and AI decisions. Unlike Reading Paths, these are original arguments and practical decision models, not routes through third-party essays.
Product strategy
Which product bet deserves the next commitment?
Turn strategic ambition into a causal claim, expose the assumption that can break it, and make further commitment wait for evidence.
Read the 12-minute guide
Product roadmaps
Which roadmap items are decisions, and which are commitments?
Keep uncertainty visible without making coordination impossible. Connect Explore, Choose, and Commit states through explicit evidence gates.
Read the 11-minute guide
A three-part decision sequence
Measure the work. Test the cause. Price the result.
AI investment fails when activity, causality, and economic value are collapsed into one optimistic number. Use these guides in order, or enter at the decision blocking you now.
01 / Measure
Are you measuring better work or merely more AI activity?
Replace prompts, output, adoption, and estimated hours with a measurement chain that can distinguish activity from useful throughput.
10 min read →
02 / Test
Did AI cause the improvement you observed?
Design a comparison that can survive selection effects, quality failures, novelty, and the pressure to declare every pilot a success.
15 min read →
03 / Value
Did the improvement create value the organisation can capture?
Translate released capacity into realised benefit, include the full cost of the system, and test whether the investment survives honest assumptions.
11 min read →Put it into practice
Build a workflow you can verify.
Apply the evidence standard to the tools doing the work.