Frameworks & Blueprints
RSS FeedBattle-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.
Industry-Specific AI Playbooks
Complete implementation guides with case studies, timelines, and prioritization frameworks.
Retail AI Blueprint
From pilot purgatory to $1B+ revenue. The exact approach I used at Best Buy to build a personalization platform in 90 days.
Logistics AI Blueprint
How I built the industry's first logistics LLM in 2022—18 months before ChatGPT. The Signal Hub architecture and profit center playbook.
IPO-Ready AI Blueprint
The data architecture that survives due diligence. Built for Olo's $3.6B NYSE debut. For Series C+ startups building for exit.
Original IP for AI Transformation
The strategic frameworks I developed from building AI products at Fortune 500s and startups.
The Velocity Gap Framework
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.
The Agent Yield Framework
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.
B1/B2/B3 Innovation Framework
Break Even → Break Through → Break Away. How to prioritize AI projects by strategic impact.
The Profit Center Framework
90% of data teams are cost centers. The 4-level maturity model to become "untouchable."
The "So What?" Framework
The salary gap isn't SQL skills—it's two words. The 4-level analytics maturity model.
Healthcare AI Strategy 2026: Why This Time Is Different
OpenAI and Anthropic just bet billions on healthcare AI. A strategic analysis of the January 2026 healthcare AI race.
The Semantic Mirror: Why Enterprise AI Follows the Big Data Playbook
Why 85% of AI projects fail for the same reasons Big Data projects failed. Includes the 10-20-70 principle.
Why 90% of AI Projects Fail
Only 10% of enterprise AI initiatives reach production. The 3 pillars that separate success from pilot purgatory.
Build vs. Buy: How We Outperformed a $30M Vendor Quote
$3.2M build. 90 days. $1B+ revenue. The framework for high-velocity enterprise development.
The 4 Phases of Leadership Scaling
From 2 people to 300+. The leadership evolution framework.
Data StrategyBeyond the Dashboard: 6-Point Audit
6 tests to ensure your dashboards drive decisions.
RetailRetail Transformation 2026
Winners vs. losers in retail AI divergence.
VerticalAI for Retail & Ecommerce
Best Buy: $1B+ platform. Target: 100M+ personalization.
VerticalAI for Logistics & Distribution
Pre-ChatGPT GenAI pioneer. C.H. Robinson: $150M.
Ready to Apply These Frameworks?
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.
Fresh From Edward
Working notes on leading with AI, prompting skill, who should own AI, and the philosophy behind data strategy.
Improve Your Leadership Style with AI
I analyzed my own chats with Claude to see how I work. The result was a working-style review I did not expect, and a way to spend fewer tokens.
The Three C's of AI
Creative thinking, critical thinking and clear communication: three skills we learned in elementary school that decide how good your AI output is.
Who Really Owns AI in Your Company?
Most people assume the CIO or CTO. I am not seeing that as the natural home for AI anymore.
Philosophy and Data Context
There is a layer that often gets forgotten when assessing a data strategy: the philosophy layer.
Newest Research and Checklists
Current data on AI pilots and scale, a plain definition of the Velocity Gap, and a scored checklist for your own organization.
The State of Enterprise AI, Q4 2026: What the Latest Data Says About Pilots, Scale and Payoff
Most companies use AI. Few report a meaningful financial return. Here is what the newest research says separates the two groups, and where it does not.
What Is the Velocity Gap? Definition, the 8 Friction Defaults, and What It Is Not
A plain-language definition of the Velocity Gap Framework, the eight habits behind it, and how it differs from other uses of the phrase.
AI Pilot to Production Checklist: 8 Questions to Answer Before You Scale
One question for each of the eight Friction Defaults, a scoring scale, and three extra questions drawn from current research.
Latest Articles
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.
Data Teams
The Value of In-Field Experience for Data Analysts
The best data analysts don't start with SQL—they start in the field.
The Solve When Building a Data Team
The real problem when building a data team isn't hiring—it's what you hire for.
Lessons Learned from Building Data Teams
Hard-won lessons from building data orgs from scratch to 300+ people across five companies.
Prompt Engineering
Good Prompts vs Bad Prompts
What separates a prompt that gets garbage from one that gets gold.
How to Write Great Prompts
A practical framework for writing prompts that actually get useful results from LLMs.
AI Leadership
AI Strategy
The State of AI Pilots
Why most AI pilots are stuck, what's actually working in 2026, and how to escape pilot purgatory.
Product Management and AI
How product management changes in an AI-first world—and what PMs need to unlearn.
AI for Small Businesses
You don't need a data science team to benefit from AI.
AI for Project Managers
How project managers can use AI to run tighter projects and catch risks earlier.
Personalization in 2026
Personalization finally works—because of LLMs. Here's what's changed.
Why AI Results Are Slow
If your AI initiatives are taking forever to show results, it's not the technology.
Data & Analytics
How Data Analysts Can Use AI
Practical ways data analysts can use AI today to multiply their output.
Are You Really Data Driven?
Most companies say they're data-driven. Most aren't. Here's the honest test.
Data Lakes Do Nothing
Data lakes don't create value. People create value.