AI Operations
Run AI as a managed function.
The control gap
Most companies have AI. Few have someone running it.
32%
of 145 enterprises surveyed name the lack of a single owner or accountable team for AI as their biggest barrier, the most-cited answer.
Source: VentureBeat Pulse Research, The control gap (July 2026)
42%
of IT professionals say accountability for AI decisions is actually clear, even though 85% report having AI policies or oversight in place.
47%
of organizations report AI spending above what they planned.
Source: Futurum Group (September 2026)
What we do
Six parts of running AI well.
- 01
Focus and priorities
Which AI initiatives are worth doing, in what order, and which to stop. A ranked portfolio instead of scattered pilots, with the work redesigned before it is automated. - 02
Data readiness
Which data AI can reach, whether it is clean, permissioned and defined, and what to fix first. Most AI problems start as data ownership problems. - 03
Architecture and tooling
How the pieces fit together, and repeatable patterns your teams reuse as building blocks. Build, buy, keep or retire decisions on tools, and implementation through vetted delivery partners or your own team when it is time to build. - 04
Ownership and controls
One intake for AI requests, an approval step before anything goes live (a named owner, a security review and a test of how it performs), and clear rules on which models and tools are approved. AI governance, sized to your business, so teams can move quickly and safely. - 05
Running AI day to day
Admin and access for each AI tool, seats and licenses, usage limits, vendor renewals and support, run by your IT or business technology team like any other system. And adoption: business teams using AI in their daily work. - 06
Oversight and reporting
One regular view for leadership and the board: what AI is running, who owns it, what it costs, where it replaces hiring and where it does not, and whether it is paying off, with controls your finance team can follow. Payoff is reported in four separate lines: spending removed, capacity gained, quality, and risk reduced.
Architecture
How the pieces fit together.
Every company already runs infrastructure, data, integrations and applications. AI adds new layers on top: shared data definitions that models can use, a layer where models are accessed, checked and tested, and agents that increasingly do work applications used to do. Security, monitoring, policy and cost run across all of it.
We map the tools you actually run onto this picture. Gaps, duplicates and the next decisions become obvious, and each decision is sized to where you are: a handful of agents needs different answers than hundreds.
Then we turn what works into repeatable patterns: standard ways to connect data, call models, add guardrails and hand work to agents. Your teams build new use cases from these blocks instead of starting over each time.
Architecture work is led with senior enterprise architects who have spent more than 20 years at enterprise software companies.
Agents and AI-assisted work
New: work that applications and people used to do by hand
Model access and checks
New: which models people can use, with guardrails and testing
Data definitions
Newly critical: the definitions and metrics people and models both rely on
What you already run
Infrastructure, data, integrations and applications
Security · monitoring · policy · cost
Operations
Part of how your technology team already runs.
Running AI is operations work as much as strategy. We set it up inside your IT or business technology team, using the service, security and vendor processes they already run, so AI becomes part of how the team works instead of a separate function on the side.
- Admin, access and permissions for each AI tool
- Seats, licenses and usage limits by role
- Usage and spend tracked alongside other software
- Vendor contracts and renewals in the same process as the rest of your stack
- Support and change management when models and tools update
How we work
Set it up, run it, hand it over.
- 1.
Set it up
An owner, an intake, the rules, and a reference architecture for your company.
- 2.
Run it
With your team, holding the seat or alongside your leaders, until it works without us in the room.
- 3.
Hand it over
To a named internal owner, with the runbook, roles and policies to hire against.
In practice
Cybersecurity software company, $300M+ ARR
After a restructuring, the business technology organization needed leadership, and AI was spreading across teams and tools faster than anyone could account for it.
Read the full story- One intake for AI, with an approval step before anything goes live: a named owner, a security review and a test of how it performs
- AI administration and spend under one accountable function, with usage limits set by role
- Chose a narrower license for a workplace tool so company data would not leave through a vendor's search connector
Already have a CIO or AI lead?
Good. We work with them and under their direction, adding senior capacity on business operations, business applications and the data AI depends on. They stay in charge of the platforms, business teams own their workflows and how they adopt AI, and finance owns how value is measured.
One owner, not two.
How we engage.
Scoped engagement
A defined time, scope and fee, from working out where to start through to the result. We own the result. Your team owns the decisions and the systems, and we sign off on the work together.
Fractional or interim leadership
Ongoing, typically 20 to 25 hours a week. We hold the seat or work alongside your leaders, and plan the transition from the start.
The other side: Business Operations.
AI only pays off on top of operations that work. We help set up, scale and streamline those too.
Let’s talk about where you are.
Start with a 30-minute conversation. You will leave with a view of where we would begin, and whether we are the right fit.