Deliver
Deployment, adoption, measurement and operating ownership. The phase most providers treat as someone else’s problem — and the one where value is either realised or quietly lost.
An unadopted capability has negative value
You carry the licence cost, the maintenance and the security surface, and you get none of the benefit. That is worse than not having built it — and it is the most common way an otherwise competent AI project fails.
So adoption is engineered, not hoped for: role-based training, a named internal champion, workflow redesign so the capability sits in the path of the work rather than beside it, and measurement against the baseline agreed during Plan.
Then a deliberate choice about who runs it. Your team takes it with documented handover, or we run it under a managed arrangement. What we will not do is leave that undecided.
What delivery covers
AI Success Services
The QA, cloud, cybersecurity and automation disciplines that decide whether a capability survives contact with production. Each available standalone.
Explore → AI legacy integrationsEnterprise System Integration
Landing AI capability into the ERP, EHR, PMS, CRM and line-of-business systems that actually run your business.
Explore → AdoptionAI Training & Adoption
Change management, role-based training and champion programmes so the capability is used rather than routed around.
Explore → OrchestrationiPaaS & Workflow Automation
Wiring the capability into the wider process so a result triggers the next step instead of sitting in a dashboard.
Explore → RunCloud & DevOps
Deployment pipelines, environments, monitoring and the operational plumbing a capability needs to be supportable.
Explore → OngoingDedicated Engineering Teams
A named squad for the next increment, reusing the platform and governance already paid for.
Explore →How a capability actually goes live
Deployment & cutover
Into production with rollback, monitoring and a named operating owner before go-live, not after.
Adoption & change
Role-based training, an internal champion, and workflow redesign so the capability is in the path of the work.
Measurement
Reporting against the business metric and baseline agreed in Plan — yours, not a model-quality score.
Handover or managed run
Explicitly chosen. Documented handover with acceptance criteria, or we operate it for you.
About delivery and adoption
Do you support the capability after go-live?
If you want us to. Delivery ends with an explicit decision: documented handover to your team, or a managed arrangement where we operate and improve it. Both are priced; neither is assumed.
What does adoption support actually involve?
Role-based training for the people whose work changes, a named internal champion we equip rather than a generic all-hands session, workflow redesign so the capability sits where the work already happens, and a feedback route so production problems reach the engineering team. See AI Training & Adoption.
How do you know whether it worked?
We report against the business metric and baseline agreed during Plan. If the number did not move, we say so and look at why — which is usually adoption or data quality rather than the model.
Get it into production
If you have a capability that has stalled before go-live, that is a conversation worth having.