Frameworks for thinking about AI as operational capacity.
The four foundational pieces: workflow gap, deployment trap, SaaS replacement scorecard, and infrastructure decision.
Read the framework series.
'We Don't Change Your Process' — What That Actually Means
When a managed AI service says it won't change your process, here is precisely what stays the same, what honestly changes, and five questions to vet any vendor making the claim.
Why Your Team Doesn't Need AI Training to Benefit from AI
AI training makes sense when your team will operate AI tools. With managed AI operations they never do — the work comes back review-ready, and review runs on judgment your team already has.
The Forward-Deployed Model Was Built for the Enterprise: Why the Mid-Market Gets Priced Out
The forward-deployed model works, but its documented wins are all enterprise-scale. This breaks down the cost floor that prices the mid-market out, what fills the gap instead, and what a smaller buyer should actually evaluate.
Services Dressed Up as Software: How to Tell Real AI Embedding From Theater
A buyer's due-diligence guide for embedded AI engagements: the seven questions that separate real forward-deployed engineering from 'services dressed up as software,' the exit test for vendor lock-in, and the red flags analysts name.
The Forward-Deployed Model: Why AI Companies Are Embedding Engineers Inside Your Business
The forward deployed engineer — a role Palantir invented to embed engineers in customer workflows — became the standard go-to-market motion for AI in 2026. Here's where it came from, why it's everywhere, and the tradeoffs its own champions concede.
AI at Work: The Gap Between What Companies Deploy and What Employees Use
Enterprise AI deployments are producing shelf software while employees use personal AI accounts that leak company data into uncontrolled channels. The missing layer isn't better AI — it's workflow design.
The Workflow Gap: Why AI Implementations Stall Before They Start
Why most enterprise AI projects fail despite better, cheaper models. The bottleneck isn't technology. It's the design work that turns AI capabilities into functioning business workflows.
Local LLM vs Cloud API: The Infrastructure Decision That Determines Your AI Costs Forever
A practitioner-grade framework for choosing between local LLM inference and cloud APIs. Includes real cost data, compliance requirements by industry, hardware recommendations, and the hybrid architecture that most mid-market companies actually need.
The SaaS Replacement Scorecard: What to Replace, Augment, or Keep
A practitioner-grade 5-axis scorecard for evaluating which SaaS tools to replace with AI, augment, or keep. Includes real tool scoring examples, cost data, and a step-by-step audit process for mid-market companies.