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AI Strategy & Transformation

MTK helps enterprises turn AI ambition into a funded, governed roadmap, starting with use cases chosen by value and feasibility.

Request an AI readiness assessmentTalk to an AI consultant
Client systems feed a readiness score; ranked use cases become a funded roadmap in three waves, approved by the executive teamClient systems feed a readiness score; ranked use cases become a funded roadmap in three waves, approved by the executive team

The challenge

Why AI programmes stall

  • Pilots without a path to production

    Proofs of concept work in isolation but are never funded, integrated or owned once the demo ends.

    Addressed byAI strategy and value roadmap

  • Use cases chosen before the data is checked

    Priorities are set in workshops, and only later does the team find the data incomplete or untrusted.

    Addressed byAI readiness assessment

  • No owner for AI risk

    Without clear decision rights, legal, IT and business teams slow AI down instead of shaping it.

    Addressed byAI operating model and governance

How it works

From AI ideas to a funded roadmap

Candidate use cases are scored on value and feasibility using real data, then sequenced into funded waves.

Value

Feasibility

Scored on real data

Funded waves

  1. Wave 1
  2. Wave 2
  3. Wave 3

Use cases profiled with MTK Intelligence before they are ranked

What MTK offers

Advisory services for AI transformation

  1. AI strategy and value roadmap

    Use cases ranked by value and feasibility, then sequenced into funded waves with a business case for each.

    OutputA roadmap the executive team can approve, budget and track quarter by quarter.

  2. AI readiness assessment

    Start with · 2 weeks

    A two-week review of data, processes, skills and governance against priority use cases, profiling real data from core systems rather than relying on interviews alone.

    OutputA readiness score, a data quality profile, a gap plan and a ranked list of use cases.

  3. AI operating model and governance

    Ownership, decision rights, risk tiers and a responsible-AI policy, set up so AI can scale safely.

    OutputA starter set covering AI policy, use-case register, risk rules, evaluation standard and incident process.

  4. Process redesign for AI

    Finance, supply chain and customer service workflows redesigned around AI assistance and agents.

    OutputProcess maps that show where AI proposes, where people review and approve, and how results are measured.

  5. AI adoption and enablement

    Training for leaders and teams, usage tracking and change management, so new tools become part of daily work.

    OutputAdoption measured by use case and reported alongside business results.

How MTK delivers

Five stages, each with a defined output

MTK uses its own data and AI platform during strategy work, so recommendations rest on evidence from the client's systems.

Stages

  1. Assess

    Readiness, data quality and priority use cases, scored on real data.

  2. Plan

    Roadmap, business case, target architecture and governance basics.

  3. Pilot

    One use case proven on real data against an agreed KPI.

  4. Scale

    Rollout plan, operating model and enablement for the wider organisation.

  5. Operate

    Value tracking, governance reviews and the next wave of use cases.

  • MTK Intelligence

    Connects to ERP, CRM and files in the client's cloud to profile data quality and test whether priority use cases are feasible.

    Assess · Plan

  • Industry reference models

    Process templates and KPIs from MTK ERP and MTK Energy give each roadmap a tested starting point.

    MTK ERP · MTK Energy

    Plan

Related use cases

Use cases that often come first

View all use cases

  • MTK Intelligence · Advisory

    AI governance starter

    A use-case register, AI policy, evaluation tests and decision log, set up before the first model reaches users.

    AI governance starter

  • MTK Intelligence

    Business data in one model

    ERP, CRM and file data unified, with one definition per KPI shared by dashboards and AI.

    Business data in one model

  • MTK Predictive

    Demand forecasting

    Forecasts by product, channel and region, often chosen as a first pilot because the baseline is easy to measure.

    Demand forecasting

FAQ

Questions about AI strategy engagements

How long does an AI readiness assessment take?
Two weeks for a defined scope, usually one business unit and its priority use cases. The output is a readiness score, a data quality profile, a ranked list of use cases and a recommended next step.
Do we need clean data before we start?
No. The assessment measures data quality on live systems and shows which use cases the current data can support. It also shows what needs fixing first and the effort involved.
What does the client own at the end of an engagement?
The client owns its data, the models trained on that data and the configurations built for it. MTK platforms used during delivery are licensed, on terms agreed at the start.
Does MTK recommend only its own platforms?
No. Recommendations follow the use case and the systems the client already runs. MTK platforms are proposed where they shorten delivery, and they work alongside existing tools rather than replacing them.

Start with an AI readiness assessment

Two weeks, a defined scope and a clear output: where AI creates value in the business and what it will take.

Request an AI readiness assessmentTalk to an AI consultant

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