Agent Strategy

Know which workflows AI should take on first, before you fund the build.

Most teams can name ten places AI might help and can’t rank them. We test candidate workflows against your real data and systems, and tell you which to build, which to defer, and which to drop.

Talk to an AI engineerExplore Advise

Part of

01Advise

Know where to apply AI.

Use-case prioritization

Where this sits in the lifecycle

The problem

AI projects usually fail before the first line of code: someone picks a technology, builds a demo, then goes looking for a workflow to justify it.

  • No shared view of where AI changes the economics of the work
  • Demos that impress in a meeting and connect to nothing
  • Budget going to whatever is newest, not what pays back
  • Cloud bills arriving before anyone has asked what it’s for

How we work

Four steps. One team the whole way.

  1. 01

    Map the work

    Sit with the teams who own the workflows. Find where time, cost, and error actually concentrate, not where the loudest idea is.

  2. 02

    Match capability to work

    For each candidate, decide what today’s AI can genuinely carry: full automation, assistance, or nothing yet.

  3. 03

    Test against your data

    Lightweight prototypes on your real data and systems. If it doesn’t hold up on your data, it doesn’t go on the roadmap.

  4. 04

    Sequence the roadmap

    What to build first, what to defer, and what to drop, ordered so the first build funds the next.

What’s included

What agent strategy covers. In practice.

  • Use-case discovery

    Structured sessions and data review that surface where AI moves an operating metric, not a brainstorm.

  • Feasibility testing

    Quick prototypes against your real data, so “possible” means possible here.

  • Risk assessment

    Data readiness, integration complexity, compliance exposure, and what could go wrong.

  • Cost & return modeling

    Expected build and run costs against the value, so leadership can make a real decision.

  • Sequenced roadmap

    Phased and prioritized, written for the team that has to execute it.

  • Build-ready handoff

    Each first build is scoped well enough for engineering to start the following week.

What you can hold us to

Commitments. Not marketing ranges.

Sprint length from engagements we have run; the rest is what the engagement commits to.

to a roadmap
2–4 wks
to a roadmap
Typical sprint from kickoff to a ranked, sequenced plan
not a sandbox
Your data
not a sandbox
Candidates are tested against your real systems before they’re recommended
with reasons
Ranked
with reasons
Every use case comes back with value, feasibility, and risk written down

Go deeper

More under Advise, and beyond.

Bring us your shortlist. We’ll rank it.

Give us the candidate workflows and access to the real data. We’ll tell you which ones today’s AI can carry, and which to drop.

Talk to an AI engineerSee our work