Is this different from just hiring a Fractional CAIO?

A CAIO drives AI policy and adoption. An AI Implementation engages the CAIO plus the CIO (technology fit) and CISO (governance), with our Foundry build team on call. The combination is what moves AI past a pilot, because most stalls are caused by data, integration, and governance gaps that sit outside the CAIO role.

Do we need to start with the 30-Day AI Foundation?

Not always. If you are starting from zero, the 30-Day AI Foundation is the fastest on-ramp and produces the policy, data scorecard, and first governed pilot. If you already have governance and a use case, we can begin at the Pilot or Embed stage.

What if our data is not ready?

That is the common case, and we plan for it. Most AI projects fail on data, not algorithms. We inventory what you have, fix what is blocking the use case, and only then point models at it. Data readiness is a tracked deliverable, not an assumption.

Will you build custom software, or just configure tools?

Most mid-market AI rides on configured tools (Copilot, ChatGPT Enterprise, domain platforms). When the use case needs a custom integration, pipeline, or model, EdgePoint Foundry builds it under the same roadmap. Strategy stays accountable for outcomes, Foundry stays accountable for the code.

How do you measure whether it worked?

We set a baseline before the pilot and track adoption, hours saved, quality lifts, and workflow change against it. Seat counts are not a success metric. A use case two people actually use beats a platform nobody touches.

// Flagship solution

From AI pilots that stall to AI in production.

AI Implementation is our end-to-end solution for mid-market manufacturers and service companies. It brings three fractional executives and a build team together under one roadmap, so AI gets governed, adopted, and measured, not just demoed.

Schedule a working session See the 30-Day on-ramp

Governance first
Data before models
Adoption measured
Build when needed

The problem

Most mid-market AI never leaves the pilot.

The demo works. Then it stalls. Not because the model is wrong, but because the work around it was never set up: no governance, unready data, no owner, and no plan for the people who have to change how they work. AI Implementation exists to close exactly those gaps.

No guardrails

Acceptable use, data handling, and access rules are missing, so the pilot cannot safely touch real data.

Unready data

The data exists but is scattered, inconsistent, or unlabeled. The model is blamed for a data problem.

No owner

Nobody owns the workflow change, so the tool sits beside the job instead of inside it.

No measurement

Success is a seat count, not hours saved or quality gained, so value is never proven and budget dries up.

How it works

Three executives and a build team, one accountable solution.

Each role owns the part of an AI implementation it is built for. Together they cover the full path from policy to production. This is what you are actually buying.

CIO →

Technology fit

Makes the AI plan operable in the stack you already run. Owns integration, data access, permissions, and the systems work that turns a pilot into something production can support.

CISO →

Governance and control

Sets acceptable use, data handling, vendor review, and access before models touch production data. Risk controls are designed in, not bolted on after an incident.

CAIO →

Use cases and adoption

Prioritizes the work by value and feasibility, shapes the workflow, and drives championing, rollout, and measurement so people actually use the tools.

EdgePoint Foundry joins when configured tools are not enough. Custom integration, data pipeline, or model, built under the same roadmap and accountable for the code while Strategy stays accountable for outcomes.

View Foundry

Delivery

A staged path from pilot to production.

Four stages, sequenced deliberately. Each one has an owner, a deliverable, and a gate before the next begins.

Stage 01
Foundation

Governance, data inventory, and one governed pilot scope. This is the 30-Day AI Foundation when you are starting from zero.

CISO + CAIO
Stage 02
Pilot

One use case, one team, a measurable baseline. Configure tooling, build the workflow, train the pilot users, prove value.

CAIO + CIO
Stage 03
Embed

Wire the working pilot into the systems your team uses every day. Update SOPs, train champions, document what changed and why.

CIO + CAIO
Stage 04
Scale

Expand to adjacent teams and use cases on the same foundation. Monitor accuracy, adoption, and ROI on an operating cadence.

All roles

What you get

Deliverables, not slideware.

Governance: Acceptable Use Policy, data handling rules, vendor review process, and a risk register your CISO can stand behind.
Data readiness: A scorecard of the data your use case depends on, with the gaps fixed before models point at it.
A working pilot: One high-value workflow, live with a real team, with a measured baseline and the prompts, configuration, and SOPs that make it repeatable.
Trained champions: Internal owners who can run and extend the workflow after we step back, plus the change-management rhythm that keeps adoption moving.
A measurement model: Adoption, hours saved, and quality lifts tracked against the baseline, reported on an operating cadence for leadership and the board.
A scale roadmap: The prioritized next use cases, sequenced on the same governed foundation, so expansion is a plan rather than a restart.

Fit

Is this the right solution for you?

A strong fit

A mid-market manufacturer or service company, roughly 50 to 1,000 employees.
AI interest is real but pilots have stalled or never started.
You want AI inside the work (ERP, MES, CRM, ticketing, document review), not a side project.
Leadership wants governance and measurable value, not hype.

Not the right fit yet

You want a single tool license with no change to how work is done.
There is no executive sponsor for the workflow that would change.
The goal is a research lab or a frontier model, not operating value.
You need a one-day demo rather than a governed rollout.

Questions

Common questions.

Is this different from just hiring a Fractional CAIO?: A CAIO drives AI policy and adoption. An AI Implementation engages the CAIO plus the CIO (technology fit) and CISO (governance), with our Foundry build team on call. The combination is what moves AI past a pilot, because most stalls are caused by data, integration, and governance gaps that sit outside the CAIO role.
Do we need to start with the 30-Day AI Foundation?: Not always. If you are starting from zero, the 30-Day AI Foundation is the fastest on-ramp and produces the policy, data scorecard, and first governed pilot. If you already have governance and a use case, we can begin at the Pilot or Embed stage.
What if our data is not ready?: That is the common case, and we plan for it. Most AI projects fail on data, not algorithms. We inventory what you have, fix what is blocking the use case, and only then point models at it. Data readiness is a tracked deliverable, not an assumption.
Will you build custom software, or just configure tools?: Most mid-market AI rides on configured tools (Copilot, ChatGPT Enterprise, domain platforms). When the use case needs a custom integration, pipeline, or model, EdgePoint Foundry builds it under the same roadmap. Strategy stays accountable for outcomes, Foundry stays accountable for the code.
How do you measure whether it worked?: We set a baseline before the pilot and track adoption, hours saved, quality lifts, and workflow change against it. Seat counts are not a success metric. A use case two people actually use beats a platform nobody touches.

// Next step

Put AI into production.

Use a 30-minute working session to review your current AI use, the first use case worth proving, and the governance gaps in the way.

Schedule a working session View all solutions