Data & AI

Rarely because the model is wrong. Pilots stall because the foundations underneath aren’t there – data isn’t governed, there’s no repeatable route from proof of concept to live deployment, security and responsible-AI controls are an afterthought, and no one owns the consumption cost.

The pilot proves the idea; the operating model is what gets it into production and keeps it there. That’s the gap we close: governed data, a repeatable delivery model, security by default and a clear line from spend to value.

No – but you do need to know where you stand.

Copilot and agents amplify whatever they’re grounded in, so ungoverned data produces unreliable, hard-to-trust outputs. The assessment gives you an evidenced view of data maturity, governance and readiness, then we prioritise: classify and govern the data your first use cases actually depend on, rather than boiling the ocean.

You start delivering value on trusted foundations while the wider estate improves in parallel.

Agent sprawl is one of the fastest-growing risks in AI – enterprises are going from a handful of agents to thousands, with only a fraction under any formal governance.

We apply agent lifecycle governance from the start: one repeatable route from use case to pilot to governed live deployment, a registry so you know what exists and who owns it, and responsible-AI and security controls applied by default.

You get to scale agents without losing visibility or control.

Shadow AI is employees using unsanctioned AI tools – often pasting sensitive data into them – outside any governance or security control.

It’s now the norm rather than the exception, and it’s how confidential data ends up exposed.

We tackle it on two fronts: giving people governed, sanctioned tools that are genuinely useful (so the temptation drops), and putting security posture and monitoring in place – Microsoft Purview and Cisco AI Defence – so exposure is visible and controlled rather than invisible and rising.

This is the piece most organisations miss.

AI is consumed per token and per agent, and finance teams often can’t forecast or attribute that spend – so cost climbs with no line to value. We baseline your consumption, put cost-per-agent and cost-per-use-case visibility in place, forecast spend, and attribute it back to outcomes.

Combined with quarterly value reviews, that means you can show what AI is returning, not just what it’s costing – and switch off what isn’t working.

Copilot is the primary interface between people, data and AI – but it delivers when it sits on a governed, well-organised data estate with confident users, and stalls when it’s bolted onto sprawl and inconsistent adoption.

The fix is rarely more licences; it’s data readiness, governance and structured adoption with a champions network.

We assess Copilot readiness first, fix the blockers, then run enablement that makes adoption stick — so the licences you’re paying for actually get used.

No – that fragmentation is part of why AI stalls.

We bring dual Microsoft and Cisco specialism across the whole picture: data platforms and Fabric, Copilot and agents, machine learning, AI security and responsible AI, adoption and change, and AI cost management.

One partner across data foundations, the value layer and the governance around it – with the hybrid cloud platform underneath handled by the same team – means fewer handoffs and a single line of accountability from foundation to outcome.

It starts with the AI & Data Readiness Assessment – an evidenced view of your data, AI and security readiness.

From there our five-step approach takes you through aligning AI to business goals, prioritising the use cases that pay back, preparing trusted foundations, and scaling what works into governed production – with managed AI operations if you want ongoing support.

You choose how far along that path we go with you.