AI readiness and opportunity assessment
We map your workflows, data and team skills, then identify where AI can save real time or improve quality, and where it should not be used at all.
Service
Bring AI into your company with a plan, not a pile of tools.
Most companies are not short of AI tools. They are short of clarity: which workflows are worth changing, which risks matter, who owns the result, and how anyone will know it worked. AI enablement is the work of answering those questions and then helping your team act on the answers.
How it fits together
Who it is for
- Leadership teams who have been told to 'do something with AI' and want a plan they can defend
- Engineering and product teams adding AI features for the first time
- Operations, finance, legal and support teams who want AI in daily work without creating data or compliance problems
- Companies that ran a few pilots and have nothing in production
Problems we see
- Pilots that impress in a demo and stall before production
- Staff pasting company data into whichever tool is nearest, with no policy
- Tool spend growing while nobody can say what it replaced or improved
- No agreed way to judge whether an AI output is good enough to use
What we do
What is included.
Use-case prioritisation
Each candidate is scored on value, feasibility, data availability and risk. You get a short ranked list with a first project that is small enough to finish.
Governance and acceptable use
Clear rules on data handling, approved tools, human review and accountability, written for the people who will actually follow them. We cover EU AI Act obligations that apply to your use cases.
Architecture and tool selection
Build, buy or configure. We compare models, platforms and vendors against your requirements and recommend the simplest option that meets them.
Pilot to production
We scope, build and ship the first use case with your team, including the evaluation checks that decide when it is ready to go live.
Team training and workshops
Hands-on sessions built around your own tasks and data, so people learn the habits (verification, prompting, escalation) that make AI useful and safe.
How it runs
The process, step by step.
- 01
Discover
Interviews and workflow review to understand how work happens today and where it hurts.
- 02
Prioritise
Rank opportunities, agree the first use case and define what success looks like in numbers.
- 03
Pilot
Build or configure the first use case with a small group of real users.
- 04
Embed
Roll out with training, guardrails and clear ownership for each AI-assisted process.
- 05
Measure
Track adoption, quality and time saved against the baseline, then decide what to do next.
Deliverables
What you walk away with.
- AI opportunity map and ranked use-case list
- Acceptable-use and data-handling policy drafts
- Architecture recommendation with trade-offs written down
- A working pilot, with its evaluation results
- Training materials built on your own workflows
- A measurement plan with a baseline and targets
Questions
AI Enablement: common questions
What does AI enablement mean?
It means helping an organisation adopt AI in a deliberate way: choosing the right use cases, setting rules for safe use, building or buying the right tools, training people, and measuring whether the change worked. It covers the organisational work around AI as well as the technology.
How is this different from buying an AI tool?
A tool is one component. Enablement covers what decides whether the tool helps: which process it changes, what data it can see, who reviews its output, and how you measure the result. Many failed AI rollouts had good tools and no plan for those questions.
Do you recommend specific vendors or models?
No fixed preference. We compare options against your requirements for quality, cost, data residency, and integration effort, and recommend the simplest option that meets them. That may be a vendor product, an open model, or no AI at all for a given task.
How do you handle data privacy and the EU AI Act?
Governance is part of the engagement. We define what data may be used with which tools, set human-review rules, and identify which of your use cases fall under EU AI Act obligations. For formal legal advice we recommend your counsel, and we prepare the technical and process groundwork for them.
How long does an engagement take?
An assessment typically takes a few weeks. A pilot to production usually takes a further one to three months depending on integration needs. We scope this with you before any work starts.
Related
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