Why AI Needs a Managed Services Mindset

Written by Rob Quickenden, Chief AI Officer

Originally Published on HETT Insights.

Healthcare organisations already understand the importance of managed services. Networks are monitored. Security operations centres protect critical systems. Infrastructure is maintained and optimised. Identity and access controls are continuously reviewed.

As healthcare and NHS trusts do more with AI, it too increasingly needs the same (if not more) level of operational discipline.

The NHS England rollout of Microsoft 365 Copilot to more than 500,000 staff is a major milestone. The headline saving of 43 minutes per person per day is impressive, particularly in an environment where time is one of the most constrained resources. But that is only part of the story.

The wider opportunity lies in what happens next,  when AI starts to reshape the workflows that sit around those individuals. This is where  personal productivity develops into operational AI.

Copilot can help someone summarise a meeting, draft a document, analyse information or find an answer more quickly. That is valuable, and it matters. But agentic AI takes the conversation further.

AI agents can support tasks, trigger workflows, coordinate processes, retrieve information, interact with systems and operate with increasing levels of autonomy. In the right context, and with the right controls, they can support the work that happens between people, teams and systems.

That is where things start to get really interesting. Much of the pressure across healthcare is not caused by one person being inefficient. It is caused by complex processes, fragmented systems, aging processes, every growing demand, duplicated administration and too many people spending too much time trying to connect the dots.

This is why AI needs a “managed services” mindset. Not because AI is “just another technology platform”, but because once AI becomes part of day-to-day operations, it needs to be monitored, governed, secured, measured and continuously improved.

As healthcare organisations look to build and deploy agents they will need visibility into performance, permissions, usage, security, cost, risk and value. They will need to understand what AI is doing, what data it is using, who owns it, how it is behaving and whether it is delivering the intended outcome.

In simple terms, once AI becomes part of the workforce, somebody needs to manage it.

Where This Starts to Show Up in the NHS

There are already signs of this shift happening across the NHS and wider healthcare ecosystem. The NHS App is a good example. AI-powered triage capabilities are being explored to help direct patients to the most appropriate service, whether that is a GP appointment, pharmacy, urgent treatment centre, community service or self-care guidance.

This is a very different use case from summarising a meeting or drafting an email. Here, AI starts to influence patient access, service navigation and demand management. That does not mean AI replaces clinical judgement. It means AI can help guide people more effectively through complex service pathways, provided the right safeguards, escalation routes and human oversight are in place.

Another example is the use of AI to identify patients at risk of becoming high-intensity users of emergency services. By analysing healthcare data, NHS teams can identify patterns earlier and offer more personalised preventative support before pressure lands in A&E.

In pathology, AI-assisted analysis is helping specialists review growing volumes of diagnostic information more efficiently, while still keeping clinical experts accountable for interpretation and decisions. These examples show the direction of travel: AI is moving beyond personal productivity and into the operational fabric of healthcare.

That is exciting, but it also raises the stakes. The more AI becomes embedded in real healthcare workflows, the more important it becomes to govern it properly, operate it safely and maintain trust.

The Trust Gap: Governance Alone Is Not Enough

Governance is important. But governance on its own is not enough. Healthcare is built on trust. Patients trust clinicians. Staff trust systems. Organisations trust processes. Regulators trust that appropriate controls are in place.

AI has to strengthen that trust, not weaken it. That means we need to move the conversation beyond whether an AI tool has been approved or whether a policy exists. The bigger question is accountability.

Every AI agent needs an owner. Not just a technical owner, but a business, operational or clinical owner who understands what the agent is there to do, what data it can access, what actions it can take, what risks it introduces and when a human needs to step in.

If an AI agent recommends an action, who reviews it? If it performs an action, who approved it? If it gives the wrong answer, how is that detected? If something goes wrong, who is accountable?

The answer cannot be “the AI made the decision”. Accountability always remains with people. That is especially important in healthcare, where decisions can impact access, prioritisation, care quality, patient communication and trust.

As AI becomes more capable, organisations will need stronger operating models around ownership, monitoring, audit, exception handling and escalation. In other words, AI governance needs to become operational governance.