Enterprise AI Delivery
A pilot shows that something can work. Production needs much more: pipelines that run without manual effort, integration with the tools people already use, a security review, monitoring and support, and someone in the business accountable for the result. We design for production from the first week.
Enterprise AI
Why leadership teams bring this to us
When the production questions wait until after the pilot, the pilot ends and the questions remain. Ownership is unclear, the data was hand-prepared, the cost of running it for every user was never estimated, and the initiative stalls at the demo.
What the service covers
Solution design
Use cases designed for your data, systems and users, with the evaluation approach agreed before the build.
Architecture and platform choice
A vendor-neutral choice of models and platforms, and a reference architecture for running them securely.
Delivery through stage gates
Discover, prove, harden, operate and scale, with a gate at each step that forces the ownership, data and cost questions early.
Delivery assurance
Independent review of your team's or your vendor's work at each gate, when you build with someone else.
Adoption
Training, workflow changes and the measures that show whether people are using what was built.
How we deliver it
- 01
Discover
Frame the problem, the users and the value at stake. Gate: a business owner is named and a risk tier assigned.
- 02
Prove
Test feasibility on real data with a small group of users. Gate: quality and risk thresholds are met.
- 03
Harden
Engineer for production: data pipelines, integration, security and evaluation. Gate: security review passed and support model agreed.
- 04
Operate and scale
Release with monitoring and support, then extend once value is confirmed against the business case.
What you should expect to change
Fewer stalled pilots
Initiatives either earn their place in operations or stop early, while stopping is still cheap.
Owned systems
Every AI system in production has a business owner and a support model.
Known running costs
The cost to run for every user is estimated before scale, not discovered after.
Measured value
Results are tracked against the baseline set at the start.
What you receive, and where it is used
Deliverables
- Solution design and evaluation plan
- Reference architecture and platform recommendation
- Gate reviews with go or stop decisions
- A production release, or an assurance report at each gate
- Adoption plan and usage measures
Typical situations
- Taking a stalled customer-service pilot through to production
- Independent assurance over a vendor's AI delivery
- Choosing a model platform for a regulated environment
Talk to us about Enterprise AI Delivery
Send a short description of the decision or program in front of you. A senior advisor from this practice will reply within two business days.