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AI Strategy and Solutions

From AI ambition to a portfolio of use cases that reach production and pay back.

Most organisations do not lack AI ideas; they lack a way to choose, fund and industrialise the few that matter. We help leadership teams build an AI portfolio with clear value cases, then design and deliver the solutions that make it real.

The work connects strategy to engineering: every use case on the roadmap has an owner, a measurable outcome, a data readiness check and a path to production.

What is included

Service offerings

01.1

AI strategy and roadmap

Ambition, priorities and a sequenced roadmap tied to business goals.

  • AI ambition and principles
  • Prioritised use-case portfolio
  • 12–24 month roadmap

01.2

Use-case discovery and value cases

Find, score and size opportunities with business owners.

  • Use-case long list and scoring
  • Value cases for top candidates
  • Data readiness check

01.3

AI operating model

Roles, funding, governance and the hand-offs between business, data and IT.

  • Operating model design
  • Decision rights and stage gates
  • Capability plan

01.4

Generative AI and LLM solutions

Design and build assistants, retrieval and agent solutions on the right models.

  • Solution architecture
  • Working pilot
  • Evaluation results

01.5

Model and vendor selection

Compare models and platforms on quality, cost, language and residency.

  • Evaluation framework
  • Benchmark on your data
  • Recommendation

When to call us

Signs you need this

  • Many AI pilots, few in production.
  • The board asks for an AI strategy and a number.
  • Business units are buying AI tools independently.
  • Unclear which model, platform or partner to commit to.

Approach

How we work

  1. 01Frame

    Agree the business goals, constraints and how value will be measured.

  2. 02Discover

    Generate and score use cases with the people who own the outcomes.

  3. 03Decide

    Build value cases and choose a small portfolio to fund.

  4. 04Deliver

    Pilot in production conditions, measure, then scale or stop.

Standards and frameworks

Aligned with recognised practice

  • NIST AI Risk Management Framework
  • ISO/IEC 42001 (AI management systems)

Solutions

Where this service is applied

Applied AI

Enterprise AI Assistants

Secure assistants that know your business and act within your rules.

View solution Applied AI

Knowledge and Retrieval Systems

Make the organisation's knowledge findable, citable and usable by AI.

View solution Decision intelligence

Predictive Analytics

Forecasts and risk scores that are trusted enough to act on.

View solution Applied AI

Intelligent Workflow Automation

Automate the work around decisions, and keep people in charge of the decisions.

View solution Decision intelligence

AI-Powered Reporting

Reports that explain themselves, built on numbers everyone trusts.

View solution

Technology

Platforms we work with

Vendor-neutral: examples of platforms commonly used in this service. Names identify technologies only and do not imply a partnership.

  • OpenAI
  • Anthropic
  • Google Gemini
  • Azure OpenAI
  • Amazon Bedrock
  • Meta Llama
  • Mistral AI
  • Jais
See the full technology ecosystem →

Insights

Further reading

Why AI Pilots Rarely Reach the P&L

Most organizations no longer have an adoption problem. They have a conversion problem: pilots that prove a model works, but never prove the business changed.

Read →

An AI Roadmap That Survives Reality

An annual AI plan that names models, vendors and unit prices is out of date before its first quarterly review. Commit to the capabilities that last, hold the fast-moving choices as options, and write down what would make you change course.

Read →

Selecting AI Use Cases That Pay Off

Most AI portfolios are built from brainstormed ideas ranked on a value-feasibility grid. Better portfolios start from the work itself, and from one question most grids never ask: how cheaply can the output be checked?

Read →

Designing the AI Operating Model

Every enterprise has the same argument about whether AI should be centralized or spread across the business. The better question is which decisions should be made where, and how that should change as the organization matures.

Read →

Talk to us about AI Strategy and Solutions

Engage this service on its own or as part of a solution. We will agree scope, outcomes and the right team with you.

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Data Strategy and Governance

Treat data as a managed asset, with owners, standards and measurable quality.

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