teiō
“Of everything AI could do here, which three things pay for the rest?”

A forward-deployed AI strategist, a day a week, who finds the AI projects and defines them.

An operator from a consulting background, in your business about a day a week for a quarter at a time. They find where AI changes a goal you already have, and define each project to the point where one of our teams or yours can start it.

What we build

Your leadershipWhat we do

A forward-deployed AI strategist, in your business about a day a week, for a quarter at a time.

Mondayleadership meeting
Weeks 1 to 4interviews, 14 people
Weeks 5 to 9scoring and definitions
Weeks 10 to 12roadmap and handover

What comes back at the end of a quarter.

A ranked list of defined projects, each with its return, its effort and the team that should build it, and a recommendation for the first ones to fund.

A million-dollar roadmap for a mid-market manufacturer.

If you had $1M for AI this year, what would each of you fund?
Half a day with the CEO, CFO, COO, VP Sales and head of CS · 8 candidates · estimated annual return, effort 1 to 5
roadmap $1.9M / yr est.
CandidateOwnerEffortEst. return / yrPoints
Shipped this month
CompletedIn progress
1
V. Rao
Data lead
0 / 47 tasks
2
P. Mathew
Agentic software lead
0 / 36 tasks
3
S. Shetye
Data lead
0 / 29 tasks
4
C. Rajpal
AI strategist
0 / 18 tasks
5
R. Deliwala
Agentic software
0 / 21 tasks
6
A. Khan
People and ops
0 / 6 tasks

Our own AI strategist runs the same play on us.

We use the same method to rank teiō's internal projects and decide what to build next. The board below shows what shipped, so the roadmap stays honest.

Between the company goal and a project someone can start, there is usually nobody.

What stalls is the step from 'we should do something' to a project someone can start. A day a week is enough to be in the room when decisions are made; a quarter is long enough to find and define the projects, and short enough to decide again.

Weekly, in the business

They interview the people who do the work, not just the people who manage it.

Governance lands on data

In every mid-market company we have worked with, AI governance ended up under the data leader. We plan for that.

Estimates with the arithmetic

Opportunity estimates show their inputs and the arithmetic, so your finance team can check them.

Case studies

The other three practices

Own the AI that runs your company.

Built inside your company and yours to keep. Thirty minutes with a practice lead.

Book a first call