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AI leadership readiness check

AI leadership readiness check

Twelve short scenarios from the situations leaders actually face. It is not an exam and not a certification: it is a free ten-minute read on where you are strong and which parts of leading AI would repay the most attention. Nothing is stored unless you ask us to email your result.

1 of 12 · Sizing an AI use case
Your team proposes an AI use case and asks for budget. What do you ask for first?
A demo of the tool working on a sampleThe number it has to move, and the number that would make you stopA list of competitors already using itThe vendor's case studies
2 of 12 · Reading the AI landscape
A vendor pitches an 'AI agent'. What tells you what you are actually buying?
The priceWhether it is a rented engine, a product built on one, or an agent that acts, and what each costs by month 13The logos on their customer pageWhether it uses the newest model
3 of 12 · Working with AI output
An AI drafts a client-facing summary. How should the work be set up?
Trust it if the model is good enoughBan AI for anything client-facingRun the task through AI, then review the output against the source before it leaves the buildingHave a second AI check the first
4 of 12 · Guardrails and shadow AI
You learn staff are pasting client data into public chatbots. What is the first move?
Block all AI tools on the networkSend a warning emailWrite the one-page policy that says what data goes in which tools, with a named owner and a human checkWait for the vendor to add controls
5 of 12 · Leading AI projects
You are about to greenlight an AI pilot. What must be defined before it starts?
The tool and the vendorThe number that means it worked and the number that means you stop, with a dateThe size of the teamThe press release
6 of 12 · The AI roadmap
You own the org-wide AI roadmap. How should the bets be structured?
One big transformation programAs many pilots as possible to see what sticksNow, Next and Later bets, each with a kill number that stops itWhatever the board asks for that quarter
7 of 12 · The AI operating model
You have budget to build an AI capability. Who do you hire first?
Machine-learning engineersThe judgment roles that decide what is worth building and whether to trust the output, before any engineerA large data-science teamAn external agency to run it all
8 of 12 · Governance at scale
How should an executive treat the EU AI Act and company-scale governance?
As a legal problem to hand to counselAs a reason to slow all AI downAs a filing calendar: which of your uses fall into which risk tier, and which dates apply, with a clock on each partAs something to deal with when a regulator asks
9 of 12 · Data and decision infrastructure
The board wants a date for an AI capability that depends on your data. What comes first?
Commit to a date and startScore the readiness of the dataset the plan depends on, and the gap to close, before giving any dateBuy more dataHire a data scientist and wait
10 of 12 · Driving adoption
Your pilots succeed but nothing scales. What is the likely cause?
The tools are not good enough yetOne of a small set of specific rollout causes is in the way, and the fix is to name and remove that cause, not to run another pilotStaff are resistant to changeYou need a bigger budget
11 of 12 · Generative AI and agents
You are about to deploy an AI agent that takes actions. What do you set first?
The fastest possible rolloutA failure budget: how often it may be wrong, and what happens when it is, before it shipsFull autonomy to prove the valueA press announcement
12 of 12 · Vendor strategy
You are signing a major AI vendor. What do you settle on signing day?
The lowest priceThe three clauses a vendor concedes on signing day, and the exit you test before you need itThe longest possible contract for stabilityWhatever the vendor's standard terms say

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Free AI leadership readiness check for executives (10 minutes) | Chief AI Leader