AI, Data Intelligence & Machine Learning
Waiting is expensive. Rushing is worse. We do neither.
Sitting it out is no longer a neutral decision.
Competitors are automating back-office work, shortening service cycles and making faster decisions with better data. Customers increasingly assume it. Boards are asking what the plan is.
So the pressure to act is real — and so is the temptation to act quickly, broadly, and without much of a plan.
An expensive education for the market.
The pattern of the last two years is consistent: adoption is near-universal, value is not. The cause is rarely the model. It is poor data, unclear problem definition and misalignment with how the business actually works.
None of this is an argument for waiting. It's an argument for not buying the whole vision at once.
- 42%
Abandoned most of their AI initiatives in 2025 — up from 17% the year before. On average, 46% of proofs of concept never reached production at all.
S&P Global · 1,000+ enterprises - 95%
Of generative AI pilots delivered no measurable impact on the bottom line. Not model quality — poor integration, unclear problem definition and misalignment with how the business actually works.
MIT · The GenAI Divide, 2025 - 88% → 6%
Adoption is near-universal. Value is not. Around 88% of organisations use AI somewhere and roughly 80% of users report productivity gains — yet only about a third report any measurable earnings impact, and only 6% report significant impact.
McKinsey · State of AI, 2026 - Wrong place
Most AI budget goes where the return is lowest. Spend concentrates in sales and marketing; the strongest, most measurable returns have consistently come from bounded back-office and operational processes.
Market pattern
- Poor data quality
- Inadequate risk controls
- Escalating costs
- Unclear business value
We go narrow on purpose.
Rather than a broad AI transformation programme that takes eighteen months to prove anything, we identify a small number of specific use cases where today's AI is genuinely strong — bounded problems, clear inputs, measurable outputs — and where your data can actually support them.
You move now, you learn fast, and your investment is defensible at every step.
Every initiative stands on its own.
Each one gets its own business case, owner, baseline measurement and defined success criteria before a line of code is written. We track ROI per initiative, independently. No blending wins and losses into a portfolio number that tells you nothing.
Each initiative has to earn the next one.
If it proves itself, we scale it and use what we learned on the next use case. If it doesn't, you find out in weeks rather than quarters — at a fraction of the cost, with the foundations you built still intact and still useful.
The foundations come with it, not after it.
Data quality, integration, governance and risk controls aren't a separate workstream we sell you later. They're what makes the difference between a demo and something that survives contact with production.
Measured against a baseline, every time.
Every build is delivered against a measured before-and-after baseline, then reviewed independently: scale, hold or stop, on evidence rather than momentum.
Four steps, each one earning the next
- Step one
Readiness and data baseline
An honest assessment of what your data, systems and teams can currently support.
- Step two
Use case shortlist
Candidates scored on business value, feasibility with current AI capability, data readiness and risk.
- Step three
Contained build
Delivered against a measured before-and-after baseline, with governance and controls built in.
- Step four
Independent ROI review
Scale, hold or stop, on evidence rather than momentum.
Foundations, insight, and the AI on top
- Foundations
What everything else stands on
- AI Strategy and Readiness Assessments
- Data and Analytics Strategy
- Data Warehousing and Integration
- AI Governance and Risk Management
- Intelligence and insight
Seeing what the business is doing
- Business Intelligence and Reporting
- Data Visualisation and Executive Dashboards
- Customer and Operational Intelligence
- Predictive Analytics and Forecasting
- AI and machine learning
Bounded problems, measurable outputs
- Machine Learning Solutions
- Large Language Model (LLM) Enablement
- Process Automation
The other pillars
People & Performance
Leadership, capability and adoption — so technology actually lands with your teams.
Learn moreProcess Optimisation & Automation
Customer journey mapping, workflow redesign and end-to-end automation.
Learn moreTechnology
Cloud contact centre, unified comms and customer engagement platforms.
Learn more