From AI strategy and Copilot readiness through to automated workflows and analytics, applied to the processes where it changes the outcome, with governance in place first.
AI is the easiest technology in a decade to buy and one of the hardest to get value from. The failure mode is consistent: licences assigned broadly, no use cases defined, no data governance done first, and usage that fades after the first month.
We treat AI as a change programme with a technology component. That means selecting use cases where the work is genuinely repetitive or language-heavy, fixing the permissions and labelling problems that Copilot will otherwise expose, measuring a pilot honestly, and scaling only what demonstrably works.
The capabilities we deliver within AI & Automation, and what each one covers in practice.
Use-case discovery and prioritisation against value and feasibility, a responsible-AI and governance framework, build-versus-adopt decisions, and a roadmap that sequences quick wins ahead of the more ambitious work, including custom solutions on Azure AI where an off-the-shelf Copilot does not fit.
A readiness assessment covering permissions hygiene, over-sharing, sensitivity labelling, data lifecycle and licensing; then pilot design, role-based enablement, prompt guidance for real tasks, usage measurement and a scale-up plan based on evidence rather than enthusiasm.
Process discovery and prioritisation, then automation with Power Automate, Power Apps and Azure Logic Apps: approvals, document handling, data movement between systems, and the manual routines that quietly consume a meaningful share of the working week.
The foundation AI depends on: data platform design on Azure and Microsoft Fabric, pipelines and modelling, Power BI reporting and governed self-service analytics, so decisions are made on one trusted set of numbers instead of competing spreadsheets.
Automation applied to high-volume manual routines gives measurable hours back to teams that are already stretched.
Permissions, labelling and responsible-AI guardrails handled before rollout, not after an incident makes them urgent.
Prioritised by value and feasibility, so pilots are chosen to succeed and results are worth scaling.
Baselines and usage measurement defined up front so the business case can be evaluated honestly at the end of a pilot.
A governed data foundation, because AI output is only as reliable as what it draws on.
Role-based enablement and champion support that keep usage alive past the first few weeks.
A staged method with clear checkpoints, so you can evaluate progress and change direction before committing to the next phase.
Use-case workshops, process analysis, data and permissions readiness assessment, licensing review.
Governance framework, permissions and labelling remediation, pilot group selection and success measures.
Controlled rollout with enablement, prompt guidance, support and honest measurement against the baseline.
Extend what worked, automate adjacent processes, and build the reporting to keep tracking value.
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