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AI & Automation - Consulting

An AI strategy you can take to a board, including the parts where the answer is no.

AI consulting here means deciding what is worth doing. We assess candidate use cases against your data, your risk appetite and a metric you already track, then produce a sequenced roadmap with governance attached - so investment goes to the two or three cases that will actually hold up in production.

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At a glance

Who this is for
Executive teams deciding whether, where and how much to invest in AI.
Output
Scored use-case register, readiness assessment, governance framework and a costed roadmap.
Approach
Outcome-led and platform-fit driven. The recommendation follows the assessment.

The real questions

What an AI strategy has to answer.

Not 'which model' - these six. Getting them wrong is what turns an AI programme into an expensive pilot graveyard.

  • Where would AI actually change a number?

    Not where it is impressive - where it shortens a cycle, removes a cost or lifts a conversion rate you already measure.

  • Is our data good enough yet?

    AI applied to inconsistent, duplicated or undocumented data produces confident nonsense. Readiness is assessed before anything is scoped.

  • What must never be automated here?

    Decisions with legal, safety, privacy or reputational weight need a person accountable. Defining that boundary is part of the strategy.

  • Build, buy or wait?

    Often the honest answer is that a platform feature will ship this year and building now would be waste. We will say so.

  • Who owns AI once it is live?

    Monitoring, evaluation, escalation and review need an owner and a budget, or quality drifts without anyone noticing.

  • What are the obligations we carry?

    Privacy, record keeping, disclosure to customers, and where data is processed. These get documented as constraints on the roadmap.

Engagement

How an AI strategy engagement runs.

Structured, time-boxed and evidence-led. You should be able to decline every recommendation and still have something useful.

  1. 01

    Opportunity scan

    Workshops across functions to gather candidate use cases from the people doing the work, not just from leadership.

  2. 02

    Data and systems readiness

    Assessment of data quality, structure, access and ownership for each candidate. Most fail here, and that is useful to know early.

  3. 03

    Value and feasibility scoring

    Each use case scored on measurable value, technical feasibility, data readiness, risk and change effort - using your numbers.

  4. 04

    Risk and governance framework

    Acceptable use, human oversight points, data handling rules, vendor constraints and review cadence, written for your business.

  5. 05

    Roadmap and sequencing

    A prioritised plan with a small number of proving cases first, and clear criteria for what would justify going further.

  6. 06

    Business case and decision

    Costs, assumptions, dependencies and expected effect on the metric each case targets - enough to approve or decline with confidence.

Feasibility

Where AI earns its place, and where it does not.

The single most common finding is that a well-defined rule would be cheaper, faster and more reliable than a model.

Good AI candidates

Where it tends to pay

  • Unstructured input that a person currently reads and retypes
  • High-volume classification, extraction or summarisation
  • Drafting that a human reviews before it is used
  • Search across scattered internal knowledge
  • Tasks where an occasional error is cheap to catch and correct

Poor AI candidates

Where rules or process win

  • Structured data with clear rules - automate it conventionally
  • Decisions requiring guaranteed accuracy every time
  • Processes nobody has defined yet
  • Anything where an error is expensive and hard to detect
  • Use cases with no measurable baseline to improve

Our position

How we keep the advice honest.

  1. 01Platform fit before platform preference

    The assessment decides the tooling. We do not claim vendor accreditation we have not earned, and we do not publish partner badges.

  2. 02Willing to recommend no

    A meaningful share of AI enquiries are better answered with process redesign or conventional automation. That advice is part of the engagement.

  3. 03No borrowed statistics

    We do not quote industry productivity figures as if they were your expected outcome. Value is estimated from your own baseline.

Continue exploring

Decide where AI belongs before you buy anything.

A scoping conversation will tell you whether an AI strategy engagement is warranted, or whether two automations would serve you better.

Book a ConsultationSee AI use cases