AI & Data Strategy
Decide what to build before you build it. Assessments, architecture and roadmaps grounded in what your team can actually deliver.
AI and data strategy is the work of assessing readiness, choosing a target architecture and sequencing a roadmap so AI and analytics investments match what your team can actually deliver.
The problem
Boards ask for an AI roadmap. Teams jump to models and demos. Months later the pilot cannot survive messy customers, delayed feeds or the next schema change — because nobody decided what to build on, in what order, or who owns the data path.
Strategy without engineering honesty becomes a slide fiction. We ground assessments in your real systems, skills and constraints.
Who this is for
- Founders and CTOs who need a clear “build / buy / wait” call on data and AI.
- Teams with scattered pilots and no shared architecture.
- Scale-ups preparing a funding or enterprise narrative that depends on credible data foundations.
Outcome
A written architecture and roadmap your team can execute against — with us or without us.
What the engagement involves
- Week 1–2
Assess readiness
Review sources, quality, ownership, tooling and skills. Separate genuine AI opportunities from places where the data path will fail first.
- Week 3–4
Design the target
Propose a target architecture your team can operate — cloud, warehouse or lakehouse patterns, governance baselines and integration boundaries.
- Week 5–6
Roadmap and decisions
Prioritise a sequenced roadmap with decision records, effort bands and clear stop/go criteria so execution does not depend on us staying forever.
What you receive
- An AI and data readiness assessment tied to your actual systems.
- A target architecture document your engineers can challenge and own.
- A prioritised roadmap with sequencing rationale.
- Optional follow-on build or fractional support if you want us to execute the first slices.
Core deliverables
- AI readiness assessment
- Target architecture design
- Prioritised roadmap
What is an AI readiness assessment?
An AI readiness assessment is a structured review of whether your data, platforms, skills and governance can support the AI use cases you want — before you spend on models that will fail on weak foundations.
We look at sources, quality, ownership, latency needs and operational maturity, then say plainly what to fix first.
Why fix data foundations before more AI pilots?
Because most AI project failures are data failures: incomplete inputs, no replayability, no owner. A pilot on a CSV export demos well and dies in production. Foundations make every later AI feature cheaper and safer.
Will you just recommend a long consulting engagement?
No. The strategy deliverable is written so your team can execute with or without us. If we recommend build work, it is scoped and optional — not a hostage outcome of the assessment.
How does this relate to your other services?
Strategy decides what to build. Data engineering platforms and AI governance are how you build it. Fractional architecture is how you keep decisions sharp while your team grows. Many clients start with strategy, then pull one of the other three for execution.
Not sure this is the right fit?
A 30-minute discovery call is enough for us to tell you honestly whether this is what you need.
Book a discovery call