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Put AI to work, with people in control

AI strategy, governance, automation, agentic AI and enterprise AI that your people use, your board trusts and your regulators can audit.

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Animated AI agent network Signals flow from data sources through a model layer to a central AI agent, which passes consequential actions to a person for approval and keeps an audit log before acting.

AI by the numbers

  • 88%of organisations use AI in at least one functionMcKinsey, The state of AI in 2025
  • 7%have fully scaled AI, and about two-thirds have not yet begun to scale itMcKinsey, 2025
  • 23%are scaling an AI agent system somewhere in the businessMcKinsey, 2025
  • 63%lack, or are unsure they have, the data practices AI needsGartner, 2025
  • 60%of AI projects without AI-ready data predicted to be abandoned through 2026Gartner, 2025
  • 97%of organisations with an AI-related breach lacked proper AI access controlsIBM and Ponemon, 2025

What Enterprise AI does

What we do in AI

AI strategy, governance, automation, agentic AI and enterprise AI with people in the loop.

01

Generative AI, Copilots and Agentic Automation

Secure assistants and agents that act within permissions, with human approval gates.

 02

AI Governance, Risk and Compliance

Policy, inventory, risk tiers and oversight, mapped to recognised standards.

 03

Data Foundations and Advanced Analytics

Clean, governed data, dashboards and models that AI can rely on.

 04

AI Prototyping and MVP Development

From a working prototype to a minimum viable product, built on real data.

 05

Enterprise AI

AI built into enterprise workflows with security, governance and cost control.

 06

AI for small and mid-sized businesses

A practical way to start with one task, one measure and simple rules.

Know the difference

Automation, generative AI and agents: what is the difference?

AI automationGenerative AIAgentic AI
What it doesRuns a fixed workflow fasterCreates summaries, drafts and answersPlans, decides and acts across systems toward a goal
ExampleRead an invoice and post it to financeSummarise a supplier contractChase a late shipment, update the order, notify the customer
Main riskBreaks on exceptionsWrong or invented contentWrong action at speed
Human controlException handlingReview and source checkingPermissions, limits, logging and approval gates

Use cases

AI case studies

Examples of AI delivered inside real operations, from document processing to assistants and early-warning systems.

Finance team members reviewing supplier invoices at their desks
Finance

Invoice processing with exceptions to people

Staff keyed invoices by hand and chased approvals, which caused delays, errors and late-payment penalties.

Result. Less manual entry and faster posting.

Read the case study
Store colleague checking stock levels in a well-stocked aisle
Retail

Replenishment forecasting for a multi-store retailer

Store managers ordered by habit, so stock-outs and overstock occurred together, and promotions distorted ordering.

Result. Fewer stock-outs.

Read the case study
Clinical administrator working on documentation in a hospital office
Healthcare

Documentation support with strict privacy controls

Clinical administrators spent hours summarising referrals and letters.

Result. Less administrative time.

Read the case study
Maintenance engineer inspecting an injection-moulding press
Manufacturing

Early warning from maintenance and sensor data

Unplanned stoppages disrupted schedules and cost heavily.

Result. Fewer unplanned stoppages.

Read the case study
Consultants working on a proposal together around a table
Professional services

A proposal assistant over approved content

Consultants searched old proposals for reusable content, which took time and mixed outdated material.

Result. Proposals were drafted faster from approved material.

Read the case study
Family speaking with an admissions officer at a school reception
Education

An admissions enquiry assistant with escalation

The same questions arrived repeatedly and staff were stretched in peak season.

Result. Faster responses to routine questions.

Read the case study
Lorries at the loading docks of a distribution warehouse
Logistics

Freight invoice matching

Matching carrier invoices to shipments was manual and caused disputes.

Result. Quicker invoice checks.

Read the case study
Caseworkers at their desks in a public-sector office
Public sector

A policy guidance assistant with citations

Staff struggled to find the right policy and gave inconsistent guidance.

Result. Staff found guidance faster.

Read the case study

Governance

Our Trusted AI Framework

Accountable

A named owner for every AI system and decision.

Transparent

Clear records of what AI does, with what data, and why.

Fair

Testing for bias and unintended harm.

Secure and private

Data protection, access control and supplier checks.

Reliable

Measured accuracy, monitoring and incident response.

Human-centred

People stay in charge of consequential decisions.

How we deliver

AI with a human in the loop

The more consequential the decision, the more a person should be involved. We design every AI workflow with clear points where people review, approve, correct and learn.

  1. Propose

    AI drafts, classifies or recommends.

  2. Review

    A person checks what matters, with the evidence in front of them.

  3. Approve

    Consequential actions need explicit approval.

  4. Act

    The system executes within set permissions and limits.

  5. Learn

    Corrections and overrides feed back into improvement.

Insights

AI insights

Executive and risk manager discussing AI governance in an office corridor
Guide

AI governance, risk and compliance: the complete guide

Governance is not a brake on AI. It is the set of rules, owners and checks that lets you use it at scale and defend what it does. A practical 90-day plan.

AI governance, risk and compliance / 6 min read

Support team reviewing an AI-suggested reply before sending
Guide

Generative AI, copilots and agentic automation: the complete guide

Generative AI drafts and answers; agents plan and act. How to choose use cases, deploy copilots safely, automate documents and set limits that keep agents in check.

Generative AI, copilots and agentic automation / 6 min read

Team preparing to launch an MVP to real users
Article

From prototype to MVP: what changes

A prototype shows that something can work. An MVP must work reliably for real users, with proper security, real integrations, named owners and monitoring from day one.

AI Prototyping and MVP Development / 5 min read

Team agreeing success measures for a prototype
Guide

Writing success measures before you build

Agreeing how you will judge a prototype before you start prevents arguments later. Pick the metric, set the threshold and write the stop rule on day one.

AI Prototyping and MVP Development / 6 min read

Team working through a readiness checklist for a predictive model
Checklist

Predictive analytics: is your problem ready?

Prediction needs history, consistency and a decision that will change as a result. Use this checklist before you commission a forecasting or machine learning model.

Data foundations and advanced analytics / 3 min read

Team agreeing shared definitions at a whiteboard
Perspective

Definitions matter more than tools

If sales and finance define revenue differently, no analytics platform will settle the argument. Agreed, published definitions do more for data quality than any new tool.

Data foundations and advanced analytics / 4 min read

Questions

Trending in AI

What leaders are asking about right now.

Next step

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Tell us what you want to fix, grow or connect. We will tell you honestly how we would approach it.

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