Data and AI strategy: know where AI will pay off before you invest.
A four-to-eight-week engagement that maps your data, identifies where AI and automation will save time or money, sets the rules for using them safely, and ends in a costed roadmap your board can approve.
Most businesses hold useful data in several places: a CRM, an accounting package, spreadsheets, supplier portals and email. Nobody has a complete map of it, and nobody can say with confidence which parts AI could use. That makes any AI investment a guess.
A data and AI strategy replaces the guess with a plan. We audit every data source for quality, ownership and access, assess where AI or automation would produce a measurable return, write the policies your business needs to use AI safely, and recommend an architecture that fits your budget. The plan is yours to take to any supplier.
What the work includes
Every engagement is scoped and quoted at a fixed price before work starts. These are its parts.
- 01Data auditEvery system and spreadsheet that holds business data, with what it contains, who owns it, how accurate it is and how it connects to the rest.
- 02Opportunity assessmentWhere AI and automation would save time or money, ranked by return and effort, including the cases where they would not.
- 03Data and AI policyShort, plain-English rules on privacy, security, acceptable use and oversight, ready to issue to staff.
- 04Architecture recommendationHow the pieces should fit together: data warehousing, integrations, model and platform choice, matched to your budget and team.
- 05Costed roadmapThe work in sequence, with estimates, so the first step is clear and the later ones are planned.
Who this is for
- Leadership teams planning an AI investment who want advice before committing budget
- Businesses with data spread across several systems and no single view of it
- Organisations that need an AI policy before staff adopt tools on their own
- Finance or operations directors asked to justify a data or AI budget
Discover
Interviews with each department and a technical review of every data source.
Assess
Data quality, gaps, risks and opportunities, scored and ranked.
Recommend
Architecture, policy and a sequenced, costed roadmap.
Present
Reviewed with your leadership team, refined and handed over.
Questions about Data & AI Strategy
How long does a data and AI strategy take?
Four to eight weeks, depending on how many systems and departments are involved. We agree the scope and timeline before starting.
Do we have to build with you afterwards?
No. The roadmap is yours to take to any supplier. Many clients continue with Code Studio because we already know their data, but there is no obligation.
We already use AI tools. Is this still useful?
Yes. Unmanaged AI use, with company data in personal accounts, is where most of the risk sits. A strategy brings it under control without stopping people working.
What does it cost?
It is a fixed-price engagement, quoted after a short scoping call. The price depends on the number of systems and departments.
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