AI leadership
summaries.
Leadership guides for AI decisions: spend, tools, talent, policy, risk. Opinionated, regularly updated, written for directors and above. Written by someone who builds these systems.
Guides
All guides →The Executive's Mental Model for AI
AI isn't a transformation. It's five operational decisions you already know how to make. The frame that turns vendor pitches, board questions, and budget meetings into 30-second exercises.
Recognizing Leverage
AI leverage in your org looks like an unusually productive employee you didn't hire. Here's how to find the people already doing the work, and why you almost certainly can't name them yet.
Evaluating Spend
What an AI budget should look like when it's working. Seats vs. usage, plan tiers, shadow AI, and the five-minute audit your CFO will accept.
Choosing Tools
How to choose AI tools when leverage is concentrated in 15% of your users. The opinionated framework: who picks, what to fund, when to standardize, and what to do about Microsoft.
Building an AI Policy
A one-page AI policy that gets followed. What to allow, restrict, and track when your team is already using AI and your policy is still in draft.
Driving Adoption
Why half your AI seats are idle and what to do about it. Not a training program. The disposition gap, the workflow gap, and the manager signal that actually moves usage.
Selecting Talent
How to hire people who already have AI leverage, and how to spot them on your current team. The interview questions, signals, and assessments that surface disposition instead of credential.
Measuring Returns
How to measure AI program returns without an ROI framework. Seat-level decisions, workflow signals, and the one paragraph your CFO will accept.
Managing Risk
The four AI failure modes that actually hit enterprises: data leakage, wrong answers, over-reliance, shadow AI. And the operational controls that catch them.
Recent Articles
All articles →AI Marketing Assistants: The Leverage Is in Analysis, Not Copy
Most marketing teams start with AI copywriting and stall there. The workflows that actually compress are analytical — competitive monitoring, campaign reporting, structured content against a spec. The verifier is the conversion rate.
Proactive AI Agents: Your AI Only Works When Someone Opens It
AI tools are reactive by design. Someone opens them, types a prompt, gets output, closes them. The total leverage is bounded by how often that happens. Scheduled and event-driven agents remove the human from the initiation step.
ChatGPT vs. Claude vs. Gemini: What to Actually Buy
The comparison is the wrong question. The right answer is three tiers, not one vendor. Here's what to buy for whom.
How to Measure AI ROI (Without an ROI Framework)
74% of companies can't show AI ROI because the instrument was built for cost savings. Stop measuring returns. Start making quarterly seat decisions.
Verification Is the Bottleneck
AI compresses the work you can cheaply check. Everything else stays stuck. The single best predictor of which workflows yield to AI in your org isn't the model. It's whether you have a verifier.
Defensible AI Spend
Your AI budget is going up. It will keep going up. Stop defending the total and start defending the shape, because that's the only number your CFO will accept.