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AI advisory & products · Built for small and mid-sized business

AI projects don’t fail on the technology.

They fail on readiness — unclear outcomes, unsupported data, undecided governance, unchanged process. All of it exists before a line of code is written. Which means all of it can be fixed before a dollar is spent.

Why this is the problem worth solving

~95%
of enterprise generative AI pilots deliver no measurable P&L impact.MIT NANDA, “The GenAI Divide: State of AI in Business 2025”
>50%
of generative AI projects are abandoned after proof of concept — on data quality, risk controls, cost and unclear value.Gartner (2024 prediction; later observed share)
70%
of successful AI transformation effort goes to people and process — only 10% to algorithms.BCG, the 10-20-70 rule

The OUTCOME Method

Seven gates. Each one has an artifact, and a signature on it.

Six of the seven gates are about people and process, not technology — deliberately, because that is where the evidence says the effort belongs. An initiative does not pass a gate until the artifact exists and its owner has signed it.

Read the full method
  1. O

    Outcome First

    Name the number before you touch a tool.

    Gate artifactSigned outcome statement: metric, baseline, target, date.

  2. U

    Underlying Data

    Confirm the data can support the use case.

    Gate artifactData readiness check with a go / fix-first / no-go conclusion.

  3. T

    Traceable Accountability

    Decide who signs off before launch, not after.

    Gate artifactSign-off map: decision → approver → error owner → fallback.

  4. C

    Clear Scope

    What AI will and won't do, in one sentence.

    Gate artifactA published scope sentence every pilot must fit.

  5. O

    On-the-Ground Input

    Ask the team doing the work what's slowing them down.

    Gate artifactFriction log in the workers' own words, with hours attached.

  6. M

    Make It Specific

    One workflow, not a tool for everyone.

    Gate artifactOne named workflow with an owner, a baseline and success criteria.

  7. E

    Evolve Process + Tool

    Change how work happens, together with the software.

    Gate artifactRedesigned process map plus a 30-60-90 adoption plan.

What makes us different

Most AI consultants are incentivised to say yes. We are incentivised to be right.

01

Fixed scope, every phase

Each phase has a defined end and a named artifact. No open-ended discovery, no retainer that quietly becomes the deliverable.

02

Capability transfer by design

The method, the toolkit and the templates are yours. The measure of a successful engagement is that your second AI project needs less outside help than your first.

03

We will tell you not to use AI

Step one stress-tests the lever. If a process change, a hire, or a $50/month automation moves the number better than a model does, that is the recommendation you get.

04

Built by people who ship

We run the same method on our own products. Every tool in the Products line came out of a real workflow that was measurably leaking time.

Worked example

Four dead pilots, then one that stuck.

A 400-person B2B services firm had launched a chatbot, a sales-email assistant, a meeting summariser and a knowledge copilot in eighteen months. None reached production. The board was asking why the AI line item existed.

Running the method produced one number to move: onboarding cycle time, 12 days, target 6. The unglamorous work — consolidating intake documents out of email and a shared drive — was a third of the project and the actual unlock.

Onboarding cycle time
12 → 5 daystarget was 6
Manual re-keying hours
−65%11 hrs/person/week at baseline
Client volume absorbed
+20%without hiring
Pilots reaching production
1 of 1after 0 of 4

An illustrative composite drawn from typical mid-market engagements. Names and figures are representative, not a specific client.

Next step

Start with the honest version.

A 45-minute call. Tell us the number you are trying to move, and we will tell you whether AI is the right lever — including when it isn't.