Battle-tested approaches from building AI products at Best Buy ($1B+), C.H. Robinson ($150M), and Olo ($3.6B IPO). These aren't theories—they're the exact frameworks I used.
Complete implementation guides with case studies, timelines, and prioritization frameworks.
From pilot purgatory to $1B+ revenue. The exact approach I used at Best Buy to build a personalization platform in 90 days.
How I built the industry's first logistics LLM in 2022—18 months before ChatGPT. The Signal Hub architecture and profit center playbook.
The data architecture that survives due diligence. Built for Olo's $3.6B NYSE debut. For Series C+ startups building for exit.
The strategic frameworks I developed from building AI products at Fortune 500s and startups.
Why your AI strategy is optimizing for the wrong bottleneck. Execution isn't scarce anymore—Anthropic shipped a full product in 10 days with 4 people. The bottleneck has moved to clarity, ambition, and distribution.
Includes the 8 Friction Defaults diagnostic and role-specific action plans for executives, managers, and ICs.
Why "digital labor" is the wrong way to measure AI agents. Klarna saved $60M replacing 700 people, then had to rehire. The Agent Yield Framework measures what agents actually generate: revenue acceleration, not headcount reduction.
Includes 3 agent archetypes, the yield formula, 4 measurement metrics, and a deployment diagnostic.
Break Even → Break Through → Break Away. How to prioritize AI projects by strategic impact.
90% of data teams are cost centers. The 4-level maturity model to become "untouchable."
The salary gap isn't SQL skills—it's two words. The 4-level analytics maturity model.
OpenAI and Anthropic just bet billions on healthcare AI. A strategic analysis of the January 2026 healthcare AI race.
Why 85% of AI projects fail for the same reasons Big Data projects failed. Includes the 10-20-70 principle.
Only 10% of enterprise AI initiatives reach production. The 3 pillars that separate success from pilot purgatory.
$3.2M build. 90 days. $1B+ revenue. The framework for high-velocity enterprise development.
From 2 people to 300+. The leadership evolution framework.
Data Strategy6 tests to ensure your dashboards drive decisions.
RetailWinners vs. losers in retail AI divergence.
VerticalBest Buy: $1B+ platform. Target: 100M+ personalization.
VerticalPre-ChatGPT GenAI pioneer. C.H. Robinson: $150M.
The articles teach the concepts free. The implementation guides give you the worksheets, templates, and week-by-week roadmaps to put them to work — from $29.
Practical lessons on AI, data teams, prompts, and product leadership — drawn from hands-on work at Best Buy, C.H. Robinson, Olo, Target, and Shipwell.
The best data analysts don't start with SQL—they start in the field.
The real problem when building a data team isn't hiring—it's what you hire for.
Hard-won lessons from building data orgs from scratch to 300+ people across five companies.
What separates a prompt that gets garbage from one that gets gold.
A practical framework for writing prompts that actually get useful results from LLMs.
Why most AI pilots are stuck, what's actually working in 2026, and how to escape pilot purgatory.
How product management changes in an AI-first world—and what PMs need to unlearn.
You don't need a data science team to benefit from AI.
How project managers can use AI to run tighter projects and catch risks earlier.
Personalization finally works—because of LLMs. Here's what's changed.
If your AI initiatives are taking forever to show results, it's not the technology.
Practical ways data analysts can use AI today to multiply their output.
Most companies say they're data-driven. Most aren't. Here's the honest test.
Data lakes don't create value. People create value.