Service 08
AI strategy and operating model
We help leadership pick the AI work that pays off, decide what to build or buy, and set up the operating model that keeps AI systems owned and measured.
In one paragraph
An AI operating model says who owns each AI system, how it is measured, how models and prompts change, and how incidents are handled. It lets a company run AI systems without outside help.
Deliverables
What you get
- 01 A ranked list of use cases, each with a business case, a risk rating and a first step
- 02 A platform decision for build, buy or vendor platform, with an analysis of the lock-in
- 03 An operating model that says who owns agents, evals, incidents and model changes
- 04 A data and security readiness review
- 05 A training plan for the people who will build, run and review each system
- 06 An ownership and exit plan for each system we build
Where it fits
Typical use cases
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First AI program
A short list of projects with real numbers behind them, instead of a long list of ideas.
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Stalled pilots
A review of pilots that worked in a demo but never reached production, with a decision for each one to fix, stop or scale.
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Platform choice
A side-by-side test of vendor platforms on your own use case, with costs and an exit path for each.
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Operating model
Clear owners, review cycles and incident rules for every AI system, written so your teams can run them without us.
How we work
Start from the work
We start with the work your teams do every day: the queues, the documents, the hand-offs and the rework. For each candidate, we measure the volume, the cost and the error rate today. Then we estimate what share an AI system could take on, and at what risk.
The result is a short, ranked list with a business case for each item and a clear first step.
Build, buy or platform
Every major model vendor and cloud now offers an agent platform. Many software vendors now sell agents in their own products. Some of these are the right choice. Others lock your data and your process into one vendor. We compare the options on your own use cases and write down the exit cost of each.
Leave the team stronger
Each engagement ends with your team running the system. We pair with your engineers from the first week, train the operators and reviewers, and hand over the code, the evals and the runbooks in your own repositories.
FAQ
Questions about strategy
Do we need a strategy before we build anything?
No. A short assessment and one system in production often teach more than a long strategy project. We usually do both together. The first build gives the strategy real numbers.
Should we buy a platform or build our own?
Usually both. Buy where the work is common, such as office tasks or common support flows. Build where the work is specific to your business or your data. We test both on your own cases before you commit.
What does an exit plan mean?
From the first week, we write down what your team needs to run each system without us, such as skills, access, documentation and on-call. We work through that list during the project and check it at handover.
Related
Often combined with
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Service 04
Claude and OpenAI team enablement
We roll out Claude, ChatGPT, Claude Code and Codex to your teams, then teach each team to use them in its daily work, with admin setup, data rules and a library of tested workflows.
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Service 07
Evals, security and AI governance
We measure whether your AI systems work, test how they fail and document them for auditors, with evals, red-team tests, tracing and compliance mapping.
Tell us which process you want to hand to an agent
A 30-minute call with an engineer. We will tell you whether an AI system is the right tool for it, and what it would take to run it in production.