Insights
Clear thinking for teams putting AI to work.
Field notes for marketing leaders building the strategy, workflows, skills, and guardrails that turn experimentation into capability.
Strategy
01Your AI use-case list is not a strategy
A useful AI portfolio requires choices about value, feasibility, risk, readiness, ownership, and what the organization can realistically absorb.
Read insightOperating model
02AI exposes the workflow you avoided fixing
Automation does not remove process debt. It makes unclear decisions, weak handoffs, and inconsistent standards more visible and more expensive.
Read insightAdoption
03Capacity is an AI adoption problem
Teams cannot learn a new way of working when every hour is already committed and experimentation feels like an additional job.
Read insightEnablement
04Training is not the same as capability
A workshop can create awareness. Capability requires supported practice, feedback, confidence, reinforcement, and a path to independent problem-solving.
Read insightScaling
05Launching an AI tool is not institutionalizing it
A useful build becomes organizational capability only when people can discover it, trust it, use it, improve it, and understand who owns it.
Read insightMeasurement
06Do not measure AI by output volume
More prompts, assets, or tool opens do not prove value. Measure whether the operating system improves the work and the business outcome.
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