Product and operating decisions
Product definition, product leadership, architecture, teams, delivery, and operating models.
Writing
Writing on products, data, AI, and technology leadership.
Practical writing on product, data, AI, technology leadership, and consequential operating decisions. The articles draw on Christopher Petrino's enterprise leadership experience and current hands-on product work through Bato Labs.
Start with why AI pilots stallTopics
Themes in the writing.
AI in products and organizations
AI product decisions, AI delivery, evaluation, human judgment, governance, and production operation.
Diligence and executive judgment
Technical diligence, AI claims, vendor dependency, team assessment, executive decisions, and board communication.
Hands-on product creation
App launch, distribution, subscription infrastructure, consumer-product lessons, and using agents in product development.
Articles
Decision frameworks and operating notes.
AI delivery
Why AI pilots stall
Most stalled AI pilots are not blocked by model capability alone. They are blocked because the organization has not defined what must be true for release, who owns the decision, and what evidence will make the workflow trustworthy enough to operate.
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AI release readiness checklist
An AI workflow is release-ready when it has a defined operating context, named owners, realistic evaluation, risk routing, human review where needed, monitoring, support, rollback, and a learning loop.
Read articleAI governance
What should an AI agent be allowed to do?
An AI agent should be allowed to act only where the owner, permissions, boundaries, tools, restricted actions, monitoring, change rules, and shutdown path are clear.
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When should AI work require human approval?
AI work should require human approval when consequence, irreversibility, low confidence, policy judgment, or accountability requires a person with authority to review, approve, escalate, or stop the action.
Read articleAI evaluation
How should an AI workflow be tested before release?
An AI workflow should be tested against the work it is meant to perform, the failures that matter, the policies it must respect, the tools it may use, and the escalation behavior required before release.
Read articleAI measurement
What should you measure after an AI workflow goes live?
After an AI workflow goes live, measurement should show whether real use is producing the intended outcome, where people intervene, which failures occur, what approvals cost, and who owns improvement.
Read articleAI leadership
Fractional CTO vs. AI consultant
Choose fractional leadership when the company needs ongoing technical and AI decision ownership. Choose consulting when the need is bounded advice, diagnosis, or implementation.
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Operating model for enterprise AI
Enterprise AI needs an operating model that connects strategy to release behavior: which workflows matter, who owns them, what evidence is required, and how the system learns from use.
Read articleTechnical diligence
AI technical due diligence checklist
AI diligence should separate what is real, demonstrated, unverified, vendor-dependent, manually assisted, or not yet ready to scale.
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AI startup technical diligence red flags
The most important red flags are not that an AI startup uses third-party models or has imperfect infrastructure. The bigger issue is when claims, evidence, operating behavior, and roadmap assumptions do not match.
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