Earlier in the year, Kaseya’s State of the MSP report found that 48% of managed service providers ranked AI and automation as the top client need for 2026, ahead of security services and backup. However, only 13% said they had turned AI and automation into a meaningful revenue stream.
The figures suggest that the market is moving quickly, yet not always with a clear direction. Clients are interested in AI-enabled managed services, but rather than simply seeking ‘more automation’, they want faster service, clearer reporting, and better use of data. They also want to know where human oversight sits in the process.
In life sciences, managed services now support systems, data, and infrastructure across research, clinical operations, manufacturing, and commercial teams. AI can certainly make processes more efficient, but it also raises questions about oversight, accountability, and whether providers understand the environment in which their clients operate.
As life sciences organisations continue to invest in AI, the expectations on managed service providers are changing.
How AI is changing the managed services model
Managed services have traditionally been associated with outsourced IT support, infrastructure management, cybersecurity and service desk solutions. With the introduction of AI, the way this work is delivered is changing. Providers can use automation to triage tickets, route issues more quickly, and support security monitoring, reducing some of the manual work previously handled by technical teams.
However, the move towards AI-enabled managed services has also made clients more focused on how those services operate. They want to understand what is being automated, how decisions are reviewed and who has oversight when AI is used to support delivery.
In life sciences, this becomes especially important when managed services are connected to sensitive data, lab systems, and applications that support clinical research.
AI may improve efficiency, but safety, security and governance still take precedence. Clients need visibility over service quality, escalation points, reporting and the people responsible for interpreting what the technology is doing.
What clients actually want from AI-enabled managed services
Clients want services that are faster and more responsive, but they also want to know how those improvements are being delivered. If AI is used to triage requests, analyse alerts, or produce reports, the process needs to be sufficiently clear for the client to trust it.
In life sciences, that trust depends on governance. Managed services may be connected to sensitive data, regulated workflows, lab systems, and applications that support clinical research, so clients need to know how issues are escalated, how risks are reported, and how decisions are reviewed when AI is used to support the service.
Reporting also becomes more important. Clients need information that helps them understand recurring issues, system performance, cyber risk, and where future investment may be needed. AI can help providers identify patterns more quickly, but those insights still need to be interpreted by people who understand the client’s operating environment.
The people behind the technology are still central. As AI becomes more embedded in service delivery, sector knowledge, technical judgement, and the ability to explain complex issues clearly become more important.
Managed services in a regulated environment
Life sciences organisations operate in an environment where technology decisions are closely connected to compliance, research activity and commercial risk. If AI is introduced into a managed service model supporting cloud platforms, lab systems, data, and applications, clients need confidence that proper controls and reviews have been maintained.
AI-enabled monitoring and reporting can help providers identify issues sooner, but life sciences clients still need to understand how data is handled, where it is stored, and whether the service model aligns with their governance requirements. Faster responses are useful, but secure processes and clear accountability still need to come first.
The same applies to regulated workflows. Managed services that support validated systems, clinical platforms, or manufacturing environments need to be delivered with an understanding of documentation, audit requirements, and operational continuity.
Without this context, providers may be able to manage technical issues, but they could miss the broader implications of when and how changes are made.
The skills behind AI-enabled managed services
Providers still need people who can configure systems correctly, interpret alerts, manage escalations, and explain technical issues to clients in a way that supports decision-making.
A technical team may be managing infrastructure, cybersecurity, service desk provision or application support, but the client also needs confidence that the provider understands the data, regulatory requirements and operational demands involved in life sciences work.
Even when managed services are outsourced, clients still need internal people who can manage the provider relationship, review performance, challenge recommendations, and understand how the service fits within the organisation’s wider technology strategy.
What clients should be asking
As AI becomes part of managed service delivery, clients need to look beyond whether a provider is using the technology and understand how it is being used. Services should still have clear escalation routes, defined ownership, and people with enough technical judgement to review the output.
When the service supports regulated systems or sensitive data, clients need clear answers on how information is handled, where automation is applied, what level of human review is involved, and how decisions or recommendations are documented.
Service performance also needs closer review. Clients need to understand whether recurring issues are being identified, whether reporting is clear enough to support internal decision-making and how the provider explains risk in a way that is relevant to the organisation’s environment.
If the technology makes the service harder to understand, the model is unlikely to give life sciences organisations the confidence they need.
The talent behind the service
AI is changing how managed services are delivered, but it has not removed the need for specialist technology talent. Providers need people who can configure tools properly, interpret the information being produced, and explain what it means in a way that clients can act on.
For internal teams, the people overseeing those arrangements need enough technical and sector knowledge to know whether the service is working as it should.
At nufuture, we work with life sciences organisations to recruit specialist technology talent across managed services, AI, cybersecurity, cloud, data and the wider IT function. If you are reviewing your internal capability or need people who can manage increasingly complex technology partnerships, get in touch.