AI Strategy & Adoption
Overview
Who it is for. CEOs, boards, CIOs and transformation leads who already have AI activity in the organisation - licences, pilots, vendor proposals - and no shared view of what it adds up to.
The problem. Adoption is no longer the hard part. In global surveys most organisations now report using AI, yet reported profit impact lags far behind usage. Efforts stall for familiar reasons: many thin pilots, success counted in licences and prompts rather than cycle time or cost, no owner for redesigning the workflow, and data residency or procurement constraints discovered late.
What we do. We inventory use cases and unsanctioned tools, score each workflow for value and feasibility, and make buy-versus-build and stop decisions explicit. We define the operating model: who owns the platform, who owns each use case and how stage gates work. Then we run adoption as a programme, not a webinar: role-based enablement, a champions network, progress measured in cohorts and an acceptable-use policy. AI literacy is part of it: Article 4 of the AI Act requires providers and deployers to take measures that support the AI literacy of their staff.
What you get. A portfolio the board can govern, a 12-18 month roadmap with stage gates tied to business metrics, a 90-day backlog and teams that know how to use the tools they have been given. Our Boardroom Simulator accelerator is the usual starting point.
How we work
At Keter AI, every engagement moves through the same four layers, in the order of our mark: strategy, architecture, delivery, governance. Each layer ends in something you can inspect and keep: a decision, a design, a working system, a set of controls. The depth changes with the service; the order does not.
Strategy
01
We start from the business problem: which decisions and workflows AI should improve, what that is worth and what must be true first.
Architecture
02
We design the system around your constraints: data boundaries, model strategy, integrations, evaluation and where it will run.
Delivery
03
We build with real data and real users, in short increments, until the system passes its evaluation set and is ready for production.
Governance
04
We leave controls that last: named owners, monitoring, an audit trail, documentation and a clear view of AI Act duties.
Deliverables
Each output is written to be used, not filed. Together they give the board a basis for investment decisions and give teams a first backlog they can start on straight away. Scope is sized to the organisation: a focused AI Readiness Assessment takes 2-4 weeks, a full portfolio and adoption programme takes longer.
Technologies we work with
Docker
Containers
Portable, reproducible packaging, so the same AI workload can move between EU cloud, private cloud and on-prem.
Microsoft Azure
Enterprise cloud
Azure AI services for enterprise estates, and Azure Local disconnected operations where systems have to run offline.
GitHub
Code and CI
Repositories, pull-request review and CI pipelines: the place where agent-written changes are tested and approved.
Hugging Face
Open-weight model hub
Hub for open-weight models and datasets. We evaluate, adapt and serve selected models inside private environments.