Solution · Transformation
AI Value Proof
Prove what AI is worth — in numbers a board accepts.
A deliberately small, decision-grade proof. We pick the highest-leverage use case, build it end to end on your stack, and measure it in operational and financial terms — so the next step is a decision, not a debate.
At a glance
- Practice
- Transformation
- Engagement
- Focused proof
- Commitment
- 3–8 weeks
- Best suited for
- Before you defend the AI spend — or bet the roadmap on a new approach — unproven.
Is this you?
The board wants an AI ROI number, and I can't defend the spend.
That sentence is the problem this engagement exists to solve — one problem, solved end to end, not a program that promises everything.
How it usually shows up
- Usage dashboards up, delivery metrics flat
- An AI line item facing scrutiny at the next budget cycle
- A new process or technology you won't bet the roadmap on unproven
The engagement
What we do.
AI Value Proof is for the leader who has to defend the AI spend — or wants evidence before betting the roadmap on a new process or technology. We pick a small set of high-leverage use cases, define their value in operational and financial terms, and baseline where you are today.
Then we build one surface end to end against real work, instrumented from day one. Not a demo: a working reference in your repo, with the machine-readable context behind it, that your engineers can extend.
You finish with the evidence, a board-ready ROI model, and an honest go, kill, or reshape recommendation. “Kill it” is credible from us because we actually say it.
The shift
“It feels faster” doesn't survive a CFO. We instrument one real initiative and hand you the number — even when the number says stop.
What’s included
What you keep.
Named deliverables, not a statement of intent. Every one is built with your team so it stays useful after we leave.
- Nº 01
Use-case selection and baseline
The handful of use cases with the best ratio of win to effort, with today's cost and cycle time measured before anything changes.
- Nº 02
End-to-end working proof
One surface built all the way through on your stack, so the reference is real and extendable, not a slide.
- Nº 03
Value instrumentation
Operational and financial measurement wired in from day one, so the result is a number, not an impression.
- Nº 04
Go / kill / reshape call
An honest, evidence-backed recommendation on what to scale, what to stop, and what to change first.
The team model
Your engineers join our team.
This is the mechanism behind “the capability stays” — not a handoff meeting at the end, but how the team is shaped from day one. Every engagement runs on it.
- Nº 01
Day one
A senior-led Sigao team arrives with the process and the platform. Your engineers don't get displaced — they get invited in.
- Nº 02
In flight
Your engineers join our team — not the other way around. One Engagement Lead owns scope and quality, and everything is built in the open on your stack.
- Nº 03
After handoff
Because your people co-built the work, they keep running it. The specs, patterns, and operating rhythm stay. The lift outlasts the engagement.
The approach
How we run it.
- Nº 01
Frame
Pick the use case, define value in board terms, and baseline today.
- Nº 02
Build
Ship one surface end to end against real work, in the open with your team.
- Nº 03
Measure
Gather the operational and financial evidence as the work runs.
- Nº 04
Decide
Hand back the reference, the ROI model, and a clear go, kill, or reshape call.
What changes
The shape of the outcome.
- Output
- A number, not a feeling
- Reference
- Working code in your repo
- Risk
- Bounded and reversible
Related solutions
Often paired with.
- Transformation
Every team is reinventing AI on its own, and none of it reaches production.
AI Platform Enablement
One shared AI-native foundation, built with your teams.
~4 monthsHow it works - TransformationFlagship
We don't want another pilot — we want AI to change how the whole org ships.
AI-Native Transformation
Get your whole SDLC from A to AI.
Multi-quarterHow it works - Development
We need the new product line — but every engineer we have is consumed running the current one.
New Product Builds
A greenfield product, AI-native from the first commit.
Scoped to the buildHow it works
Insights
Our thinking on this work.
Perspective·9 minWhat is your chatbot doing to you?
AI has become the most patient, affirming, available presence in a lot of people's lives. I use it constantly, and the research on what it's doing to us is arriving faster than the honest conversation about it.
Chris Sims
Operating model·3 minThe hidden cost of AI tool sprawl
Most engineering orgs have more AI tools than they think. Here's how to find them and what to do next.
Luke Colburn
Values·4 minOutcomes over output: a working definition
Roughly 80% of shipped features are rarely or never used. All of them counted as output. Here's how we measure what mattered instead, with numbers a CFO will sign.
Chris Sims
Values·4 minTransparency builds trust, including the hard parts
People bury bad news even when they gain nothing from silence; psychologists call it the MUM effect. Beating it takes structure, not courage. Bad news leads our weekly report.
McCaul Baggett
Free · AI Value Calculator · 3 minutes
Get a first number before you scope the proof.
Model the capacity your team unlocks by maturing AI across the lifecycle — your portfolio, your usage levels, your team's cost basis. Translated into hours, engineer-equivalents, and dollars.
What you get
- i.Projected capacity gain across your whole portfolio
- ii.The dollar value on your team size and cost basis
- iii.Phase-by-phase breakdown, plus the caveats that keep it honest
Prefer to talk first
Or put real edges around AI Value Proof.
A conversation to understand the work, the constraints, and the shape this engagement should take for your team. If it’s not the right fit, we’ll say so.