Field notes · Topic
Strategy & leadership.
Most AI programs succeed or stall on decisions made before anyone builds: which work to change, who owns it and what to leave alone. These notes help leadership teams make those decisions on evidence, one workflow at a time.
13 notes
Notes on this topic
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Choose the workflow before the model.
Most stalled AI projects didn’t fail on technology. They failed because nobody picked a specific piece of work to change. Here’s how we choose — and what we measure before writing any code.
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Questions leadership teams ask before their first AI project.
Where do we start? What will it cost to run? What happens to our people? Who’s accountable when it’s wrong? Our answers to the questions we hear most.
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An AI glossary for business teams, in plain words.
The terms you hear in every vendor meeting, each defined in two plain sentences with the one question it should make you ask. Grouped by the decision each term affects, not by the alphabet.
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Build, buy or wait: how to decide for each AI workflow.
Decide per workflow, not per company. Buy what every company needs, build where the workflow is how you compete or where you must control data, prompts and evaluation, and wait when there is no owner or no reachable data.
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When not to use AI: signs a rule, a form or a report will do better.
Many AI requests are better solved by a rule, a lookup, a redesigned form or a report. Where the answer must be exact, the volume is tiny or nobody owns the outcome, a model adds cost and risk without adding value.
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How to prioritize AI use cases when the list has thirty ideas.
Score only what you can evidence: volume, time per case, cost of errors, reachable data and a named owner. Keep value and feasibility on separate axes, because one weighted score hides the trade-off leadership actually has to make.
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Is your company ready for AI? Ask one workflow at a time.
Company-wide maturity scores rarely predict success. Readiness belongs to a workflow: reachable data, a named owner, a baseline you can measure, an error you can tolerate and people with time to take part.
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Productivity assistants or custom AI systems: what is each one for?
General assistants make individuals faster at drafting, summarizing and searching. Custom systems change a workflow, with access to its data, approval points and measurement against a baseline. You will probably need both, bought and built for different jobs.
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What belongs in an AI roadmap, and what should stay out of it.
A useful AI roadmap is a sequence of workflows, each with an owner, a baseline and a go/no-go gate, arranged as now, next and later rather than by calendar quarter. Technology appears only where a workflow needs it.
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Generative AI or machine learning: which does your problem need?
Forecasting, scoring and anomaly detection on structured data are usually classic machine learning. Reading, writing and reasoning over documents and conversations is where language models help. Many good systems use both, each for the part it does well.
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What an AI center of excellence should own, and what it shouldn’t.
A small central team should own shared foundations, standards, evaluation practice and vendor contracts. Workflows and their results belong to business owners. A center of excellence that approves every idea turns into a queue.
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What should a board ask about AI? Questions for directors.
Boards don’t need model details. They need a register of AI systems in production with named owners, the decisions each one can influence, how quality and cost are measured, and what happened in the last incident.
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AI in a group of companies: what to share and what to leave local.
In a group, share the foundations: model access, security patterns, evaluation practice, contracts and training. Let each company choose and own its workflows. A group-wide platform mandate before any workflow works usually slows everyone down.