AI strategy consulting

AI strategy consulting that starts with the people who have to use it.

A twelve-week engagement that turns AI ambition into a ranked roadmap, a governance model, and working systems your team owns. Emotional intelligence tells us what will break; applied AI gives us the tools to build it right the first time.

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The engagement

Four pillars of an AI strategy that survives contact with the team.

Strategy is only useful if it ends in something running. Each pillar produces an artefact your team can operate without us.

Pillar / 01

Readiness & adoption diagnostic

Before a single tool is chosen we baseline the metrics that decide whether AI pays off — cost-per-hire, cycle time, capacity, and the emotional temperature of the team that has to live with the change.

Pillar / 02

Use-case prioritisation

A ranked portfolio of AI use cases scored on business value, data readiness, and human resistance. We kill the science projects early and fund the two or three that move a number.

Pillar / 03

Operating model & governance

Who owns the prompt library, who reviews model output, where a human must stay in the loop, and how decisions get defended later. Governance sized for your company, not a Fortune 50 template.

Pillar / 04

Enablement & handoff

Coaching sprints, documentation your team actually keeps, and an adoption plan tied to the baseline we set in week one. The engagement ends with an installed capability, not a dependency.

Why EI-led

The differentiator is the human layer.

  • —Technical-only consultants ship the workflow and leave the fear that kills adoption untouched.
  • —Emotional intelligence tells us which team will quietly refuse — before you spend the budget.
  • —Every recommendation is scored against the human cost of the change, not just the licence cost.
  • —Thirty years of scaling teams across healthcare, real estate, federal contracting, corporate, and small business informs the roadmap.

FAQ

Common questions

What does AI strategy consulting actually deliver?

A prioritised roadmap of AI use cases, a governance and operating model, and an adoption plan tied to baseline business metrics — plus the built workflows and enablement to put the first use cases into production.

How is this different from a technical AI implementation?

Most AI rollouts stall on the human layer, not the technical one. We pair applied AI build work with emotional-intelligence-driven team design, so the systems we install are the ones your team keeps using after handoff.

How long does an engagement take?

The standard arc is twelve weeks: two weeks to diagnose, roughly seven to build and enable, and the final weeks to embed and hand off. Solo operators and founders can run a shorter, lower-touch sprint.

Which industries do you work with?

Healthcare, real estate, federal contracting, corporate teams, and small businesses — five sectors of hands-on experience distilled into one operating model.

Start with a diagnostic — we baseline the numbers before we recommend anything.

Book a diagnostic