Service

AI Implementation Strategy

Separate the AI investments that earn their keep from the demos burning your team's afternoons — and roll out the ones that fit.

A person working at a laptop beside a window

Most AI conversations start with the model and end in a Slack channel of half-deployed pilots. We start with the work — where the time is actually leaking, where the judgment calls don't need to be human, and where the brand voice does. Then we cost the options against each other: build, buy, or skip. The deliverable is a short list of pilots with success criteria and a kill date attached, plus the policy and training to put them in your team's hands. Sometimes the right answer is "not yet" — and that still beats the wrong rollout.

Typical deliverables

  • AI use-case audit across operations, marketing, and customer-facing workflows
  • Build-vs-buy-vs-skip scorecards for each candidate (foundation models, vertical SaaS, open-source)
  • Vendor selection and procurement guardrails (data residency, contract terms, exit ramps)
  • Pilot scopes with success metrics and a kill criterion before any rollout
  • Internal-policy drafting: data handling, prompt hygiene, acceptable-use guidelines
  • Training and change management for the teams whose work changes most

Come to the call with the work already started

Answer these first and the consultation opens on your systems and your data, not on background.

AI Implementation Strategy Discovery

Where AI might help, what has been tried, data readiness, and concerns.

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Ready to scope this out?

The 30-minute consultation is where we figure out whether this is the right engagement — and what the first sprint would look like.