What AI systems need from product managers
Traditional PM frameworks miss AI feature requirements, producing poorly specified features, misaligned success metrics, and avoidable production failures.
How to measure AI feature success
AI features fail in ways standard product metrics miss: users can be unhappy without errors rising. Measuring AI success needs different instrumentation.
How AI changes the economics of software development
AI coding tools compress parts of the development cycle, but not the expensive parts. Knowing where real costs live changes how to read productivity claims.
What good AI governance looks like
AI governance is not mainly compliance. It is the decisions and accountability behind AI outcomes you can stand behind. Most organizations have less of it.
How to make the business case for AI investment
Most AI investment proposals fail because they are framed around capability, not outcome. How to build a case that finance and leadership will approve.
The AI vendor landscape is consolidating: what it means for buyers
Credible AI infrastructure vendors are shrinking in number. For enterprise buyers, that changes procurement in ways most evaluation frameworks miss.
How to structure an AI team
There is no single correct AI team structure, only ones that fit specific contexts and ones that create predictable failure modes. How to tell the difference.
What to ask before buying an AI platform
AI platform evaluations focus on benchmarks and feature checklists. The questions that predict whether a platform will work in production are different.
What separates AI teams that ship from teams that stay in pilot
Most AI pilots succeed. Most AI products don't. The gap isn't technical skill, it's the organizational decisions teams avoid making before the pilot ends.
Why AI systems drift without contracts
AI systems rarely fail loudly. They drift because the assumptions behind inputs, outputs, and behavior are never made explicit enough to enforce.
The observability debt in AI systems
AI observability debt compounds faster than technical debt. Failures are probabilistic and latent, and retrofitting costs more than building it early.
How to set AI goals that actually measure progress
Most AI goals are too vague to measure or too narrow to matter. Here are the three metric types that separate progress from busywork.
Financial services AI: four constraints that reshape the architecture
Generic AI patterns break in financial services. Four constraints (audit, residency, adversarial input, risk asymmetry) reshape architecture from day one.
What Business Processes Can Be Automated with AI in 2026
AI automation works best on high-volume, repeatable processes. Here is a scoring framework across seven business domains to find where to start.
How to Choose an AI Platform or Partner: A Practical Evaluation Guide
Most AI vendors have never shipped to production. Here are exact questions, red flags, and criteria that separate firms that build from ones that only advise.
AI Implementation Costs in 2026: What Companies Actually Spend
A weekend AI prototype cost $47. The production version cost $180,000. Here is what companies actually spend, and where they overspend.
How AI Is Reshaping Professional Services
AI beats consultants at research, analysis, and drafts. It cannot replace organizational trust, novel problem-solving, or accountability.
AI for Small Business: When It Makes Sense (And When It Doesn't)
A small business owner asked if AI was worth it for his company. The answer depends on your data, your process, and your budget, not your size.