Free resource
A practical guide on what actually works, including workflow architecture patterns, common pitfalls, and how to evaluate any AI implementation before you commit.
Why AI Projects Fail
The three root causes behind weak AI initiatives, plus the warning signs to catch before you spend.
Design the Workflow
A practical model for connecting people, data, tools, models, and review without creating a fragile demo.
Build AI Literacy
What teams need to understand about capability, limits, prompting, verification, and safe everyday use.
Find Real Value
How to choose high-value use cases, control output volume, and avoid scaling low-value AI work faster than people can review it.
Control Token Spend
Match model capability to task value, use smaller models where they work, and keep usage from quietly expanding.
Make Adoption Stick
Use clear ownership, useful incentives, and outcome-based measures so AI becomes part of the workflow.
Run a Better Pilot
A step-by-step framework for scoping, testing, and evaluating an AI workflow before six months disappear.
Ask Better Vendor Questions
A practical checklist for testing architecture, data access, evaluation, security, support, and implementation skill.
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