// Fractional CAIO

AI policy, use cases, and adoption that fit the business.

Governance, data readiness, workflow adoption, and measurement.

Mid-market companies usually need AI embedded into existing work, not a separate AI program. We help govern it, select the right use case, prove it in one workflow, and expand from there.

Schedule a call View Services

Operating model

CIO, CISO, and CAIO working as one system.

AI sticks when business ownership, technology fit, and risk controls move together. We connect CIO, CISO, and CAIO decisions around one roadmap, one workflow backlog, and one change plan.

CIO

Technology fit & integration

Ensures the AI roadmap fits the stack you already run, with the data, permissions, and systems work needed to make it operable.

CISO

Governance & control

Sets the rules for data handling, vendor review, access, and acceptable use so AI can scale without creating avoidable risk.

CAIO

Use cases & adoption

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

Operating principles

Past the AI noise.

As your fractional CAIO, we focus on AI applications that solve actual operating problems. Most mid-market AI spend gets weak when governance, workflow analysis, training, and change management are treated as afterthoughts.

Governance first: Acceptable Use Policy, data handling, and risk controls in place before models touch production data, not bolted on after.
Data before models: Most AI projects fail on data, not algorithms. We inventory what you have, fix what's broken, and only then point models at it.
Adoption over announcement: A use case that two people actually use beats a platform nobody touches. We measure adoption, hours saved, quality lifts, and workflow change, not seat counts.
Embedded, not bolted on: AI lives inside the workflows your team already runs (ERP, MES, CRM, ticketing, document review), not as a separate destination.
Manufacturing-specific: AI solutions tailored to production, distribution, and service delivery, not generic SaaS demos.

Use cases

Where AI can change daily work.

Six application areas we evaluate in context, based on workflow, data quality, ownership, and measurable business value.

Predictive maintenance

Sensor data plus ML to identify failure patterns earlier and reduce unplanned downtime.

Quality control automation

Computer vision systems that detect defects faster and more accurately than manual inspection.

Demand forecasting

Multi-variable analytics that improve forecast accuracy and reduce inventory carry cost.

Process optimization

ML that continuously tunes production parameters to maximize yield and minimize waste.

Customer service AI

Intelligent triage and response systems that handle routine inquiries, freeing staff for high-value work.

Financial analytics

AI-powered modeling for budgeting, pricing, and risk decisions with better leading indicators.

Methodology

A staged approach to AI adoption.

Five phases, sequenced deliberately. Governance, data work, pilot scope, adoption, and measurement have to move together.

Phase 01

Assess

Inventory current AI use (sanctioned and shadow), data sources, workflows, and business priorities. Identify the use cases tied to measurable operating value.
Phase 02

Govern

Acceptable Use Policy, data handling rules, vendor review process, and risk register. Done before any model goes near production data.
Phase 03

Pilot

One use case, one team, a measurable baseline. Configure the tooling, build the prompts and workflows, train the pilot users, and establish the first change-management rhythm.
Phase 04

Embed

Wire the working pilot into the systems your team already uses every day. Update SOPs, train champions, and document what changed and why.
Phase 05

Scale

Expand to adjacent teams and use cases on the same foundation. Monitor accuracy, adoption, and ROI on an operating cadence with CIO/CISO/CAIO review.

On-ramp

From zero to working AI in 30 days.

The 30-Day AI Foundation is a fixed-fee sprint for organizations starting from zero. By day 30 you have an Acceptable Use Policy, a data readiness scorecard, one governed pilot, two trained internal champions, and a 90-day expansion roadmap.

View the 30-Day AI Foundation

Week 1

Govern & assess

AUP, data inventory, risk register, leadership alignment.

Week 2

Literacy & discovery

All-hands AI literacy session and use-case interviews, ranked by ROI × feasibility.

Week 3

Pilot build

Select the top use case, configure tooling, build the workflow with the pilot team.

Week 4

Deploy & measure

Roll out to the pilot team, set a KPI baseline, train champions, hand off the 90-day roadmap.

EdgePoint Foundry

When AI adoption needs custom build.

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

View Foundry

// Next step

Talk through the AI decision.

Use a 30-minute call to review current AI use, governance gaps, data readiness, and the first use case worth testing.

Schedule a call View AI Foundation