AI is everywhere in
Managed Services. New copilots, triage tools, virtual agents, workflow automation, and predictive analytics are arriving faster than many organisations can properly assess them.
On paper, that looks like progress. In practice, more
AI does not automatically mean better service.
The opportunity is significant.
McKinsey estimates generative AI could lift productivity in customer care by 30–45% when applied well. But the organisations gaining the strongest results are not simply deploying more tools. They are changing how work flows between people, processes, data, and technology.
Research shows that distinction matters.
In 2026, McKinsey found only 21% of organisations had redesigned their operating models around AI. Companies generating the greatest value from AI were three times more likely to undertake broad operating-model redesign and twice as likely to redesign workflows before choosing AI tools.
For leaders, that reframes the challenge. AI for Managed Services isn't simply a technology initiative. It is an operational transformation initiative. The objective is not more automation. It's faster resolution, less downtime, better employee and customer experience, greater operational efficiency, and measurable business value.
Why more AI tools do not always lead to better service outcomes
There is a simple reason unmanaged AI sprawl disappoints: service delivery is connected work.
Service requests, alerts, approvals, asset records, historical data, knowledge articles, incident histories, and user context all influence how quickly and accurately a service desk can resolve issues. If artificial intelligence is applied to only one point in that chain, the improvement can remain frustratingly narrow.
An AI assistant might summarise a ticket faster. A machine learning model might categorise it more accurately. A
chatbot might handle routine customer inquiries or direct a new employee towards self-service options.
Each capability has value. But consider what often happens in real service environments:
- AI classifies a request, but a service desk employee still has to re-route it because the workflow or ownership rules are wrong.
- A ticket is summarised automatically, but the next team cannot access the relevant system or knowledge base and must gather the information again.
- A virtual agent provides an answer but can't complete the required action, so the user repeats the issue when human intervention becomes necessary.
- Several automation solutions operate independently, leaving employees to transfer data manually between systems or validate AI-generated outputs.
- A monitoring tool identifies a problem, but the alert is disconnected from the incident response process required to resolve it.
The technology exists. The process remains fragmented.
And users don't measure success by AI sophistication. They experience it through response times, resolution times, and how much effort it takes to get back to work.
That's the “so what?” for customers. Better AI should mean fewer delays and hand-offs, less need to repeat information, quicker issue resolution, and a more consistent support experience.
In
Managed Services, the goal isn't to automate individual moments. It's to improve outcomes across the entire service lifecycle.
"The real value of AI isn't found in how many tools an organisation deploys. It's found in how effectively those tools work together to improve service outcomes, reduce effort, and create better experiences for both users and support teams."
Shawn Sellar, Head of Service Excellence at Canon Business Services ANZ (CBS)
Rethinking the operating model, not just the technology stack
This is where the conversation around AI in Managed Services needs to change.
For years,
automation has often been introduced incrementally: one tool for ticket classification, another for self-service, another for monitoring, and another for reporting. Each promises increased efficiency. Each solves a specific problem.
Yet organisations can still struggle to translate these point improvements into better business outcomes.
Why? Because the operating model underneath them hasn't changed.
An operating model determines how work actually gets done: who owns a process, how decisions are made, how tasks move between teams, what data is available, where controls sit, and how performance is measured.
Recent McKinsey research makes the distinction clear. The strongest AI performers are redesigning workflows and operating models rather than simply using AI to accelerate existing activities. Technology supports the transformation; it doesn't substitute for it.
For Managed Services, that means connecting:
- Front-end service requests
- Service management processes
- Workflow automation
- Knowledge bases and self-service portals
- Monitoring tools
- Business applications and operational data
- Escalation and approval controls
- Human expertise
- Reporting and measurement
"Many organisations have invested heavily in automation, but automation alone doesn't guarantee better service. The biggest gains come when AI, workflows, knowledge, and operational processes are connected into a single service model rather than operating as isolated capabilities," Shawn says.
Seamless integration allows the output of one process to become useful context for the next.
When you embed AI-powered automation this way, the benefits compound. Better classification improves routing. Better routing gets the issue to the right person sooner. Connected knowledge helps teams find answers without repeating diagnostics. Effective workflow automation reduces hand-offs. Clear escalation rules ensure complex issues reach human expertise at the right point.
The result is more than simple task automation. It's more reliable service delivery.
What connected service delivery looks like in practice
Ticket triage is one of the clearest examples.
In many service environments, incomplete information, inconsistent categorisation, and manual decisions that differ between teams or shifts slow triage. AI algorithms can analyse, categorise, and prioritise service requests using intent, context, and historical data.
But classification is only useful if the wider process can act on it.
A connected service management environment can use that information to route a request according to expertise, priority, and availability, draw relevant guidance from the knowledge base, and trigger the appropriate workflow. If the issue exceeds a defined threshold, it can escalate to the right person with the context already attached.
That has direct benefits for the customer or employee:
- Faster response and resolution times
- Fewer transfers between teams
- Less repetition
- More accurate responses
- More consistent service
- Quicker return to productive work
Recent service benchmarks show the potential.
Freshworks' 2025 benchmark drew on more than 187 million tickets across 10,743 organisations. It found business teams applying service management practices achieved first-contact resolution of 79.5%, while its analysis associated AI-powered automation with 76.6% faster resolution. Organisations using knowledge bases also recorded approximately 5% lower average resolution time.
The important point? AI didn't create these results alone. Automation, knowledge, service management processes, and self-service worked together for a better outcome.
Self-service should resolve problems, not simply deflect tickets
Self-service is another area where activity can easily be mistaken for an outcome.
AI-powered self-service portals and natural-language interfaces can help users find answers, handle routine tasks, and access support across various channels without waiting for a service desk analyst.
That can significantly improve customer experience and workforce productivity. Password resets, common software requests, and straightforward information queries are good candidates for self-service automation because users can resolve issues quickly and support staff can concentrate on more complex tasks.
But measuring how many enquiries were “deflected” can create a misleading picture.
A user who abandons a chatbot and later raises a ticket hasn't had their problem resolved. Neither has someone who receives a generic answer but can't complete the task they came to perform.
Recent service-management thinking increasingly distinguishes AI response from AI resolution. A genuine automated resolution means the issue is completed end-to-end without repeat contact or unnecessary escalation.
That's a much more useful measure of customer satisfaction and operational efficiency.
Knowledge quality also matters. Organisations that actively manage and expand their knowledge resources can
improve service efficiency and reduce resolution times. Well-maintained knowledge bases can make self-service more effective by helping users find relevant answers faster and reducing the need for assisted support. Accurate, current, and connected knowledge helps AI provide useful answers; fragmented or outdated information simply enables it to generate the wrong answer faster.
From reactive support to proactive service operations
The next opportunity extends beyond responding more efficiently after something goes wrong.
AI and predictive analytics can help Managed Services teams take a more proactive approach to IT operations.
Machine learning can analyse vast amounts of operational data, system logs, and performance metrics to identify patterns that may indicate a developing fault, unusual behaviour, or an emerging capacity issue. Monitoring tools can continuously monitor infrastructure and highlight anomalies that would be difficult for people to detect manually.
Used appropriately, this can help service teams:
- Detect potential issues earlier
- Prioritise incident response according to likely business impact
- Identify security risks or anomalous behaviour
- Improve resource allocation and capacity planning
- Reduce avoidable downtime and business disruption
- Move from scheduled maintenance towards more predictive interventions
The customer benefit is straightforward: fewer interruptions and more reliable services. This is particularly important as Managed Services environments become more complex. AI can help teams manage growing infrastructure and service volumes without increasing resources at the same rate.
But again, monitoring is only the first step. Detecting a potential problem creates little business value if the alert sits in one system while remediation depends on disconnected processes elsewhere.
The operating model needs to connect detection, decision-making, and action.
The hidden operational cost of disconnected AI
One of the biggest misconceptions in service environments is that any automation represents progress.
It doesn't.
These problems rarely appear in demonstrations. They become visible once solutions enter real operations.
A tool may perform an individual task well while failing to improve the overall process. Service teams then spend considerable time stitching together systems, correcting outputs, and compensating for gaps in automation.
That creates costs beyond the obvious manual effort. Employees wait longer to resolve issues and get back to their business priorities. Service desk teams spend time on avoidable, repetitive tasks.
Operating costs rise through rework and unnecessary escalation. Users lose confidence in self-service and revert to human support. And leaders struggle to demonstrate a return on their technology investments.
The technology may look modern. The service experience still behaves like a patchwork.
Governance is part of the service design
Trust is what separates useful AI from automation that outpaces oversight.
In mature service environments, governance isn't added after implementing an AI solution. It forms part of the operating model, including clearly defining:
- Who owns each AI-supported process?
- What AI can do autonomously
- Where is human intervention required?
- How do we escalate complex or sensitive issues?
- Which data can the system access?
- How outputs are checked
- How to manage security risks
- What happens when the system is wrong?
- How do we continuously measure performance?
Human oversight remains crucial where service management intersects with
cybersecurity, regulatory obligations, sensitive business applications, or critical operations. AI can assist with decisions, automate routine tasks, and surface insights from large volumes of data. But people remain accountable for determining appropriate controls and managing exceptions where context, judgement or risk demands it.
The goal isn't to insert a human into every AI process. That would simply recreate the bottleneck automation was intended to remove. Instead, organisations need deliberate decision points: clear rules defining where AI can act, where it should recommend, and where people retain control.
“Having led global service operations supporting more than 25,000 employees across multiple regions, I've seen that
successful AI adoption isn't about deploying the most tools. It's about integrating AI into the service model with the right governance, oversight, and accountability so organisations can improve outcomes while maintaining trust,” says Shawn.
Measure outcomes, not automation activity
This may be the most important shift of all.
If Managed Services refers to the ongoing management and improvement of business technology and service environments, then success should be measured by the outcomes those services deliver, not by the number of AI tools deployed.
This is where
AI investment becomes easier to connect to business value.
For example, shaving seconds from an individual service desk task may sound useful. But reducing end-to-end resolution time gives an employee more productive time back. Avoiding repeat contact reduces service effort and user frustration. Preventing an outage protects business continuity. Improving self-service lets service operations scale without a proportional increase in resources.
Those are outcomes senior leaders can evaluate.
McKinsey's 2026 work on AI value measurement makes a similar distinction: AI reaches full scale when it becomes part of standard workflows, governance, and budgeting, and produces sustained improvement in operational KPIs, not simply when a technical solution is deployed.
“The conversation is shifting from ‘Where can we apply AI?’ to ‘What business outcomes are we achieving because of AI?’ Success should be measured through improvements in service quality, responsiveness, consistency, and customer experience, not simply by the number of AI features deployed,” Shawn says.
What leaders should ask next
For leaders reviewing their Managed Services strategy, the key question is no longer whether AI belongs in service delivery. It already does. The better questions are:
- Are we redesigning the process or simply automating an existing one?
- Can information move seamlessly between AI, systems, and people?
- Are users getting issues resolved faster with less effort?
- Is automation reducing downtime, rework, and unnecessary escalations?
- Are self-service options genuinely resolving requests?
- Do people know when AI can act and when human intervention is required?
- Can we demonstrate greater operational efficiency and customer satisfaction?
- Is AI enabling our operations to scale in line with business needs?
- Can we connect the investment to measurable business outcomes?
That's where the next phase of value will come from.
- Not from isolated automation wins, but from redesigning service processes around outcomes.
- Not from adding more tools, but from seamless integration and better orchestration.
- And not from artificial intelligence for its own sake, but from an operating model that combines AI, people, data, and governance to deliver better service.
How CBS helps
We help organisations move beyond isolated automation to create connected, outcome-focused Managed Services environments.
By aligning AI with service management processes, workflow automation, knowledge, data, governance, and human expertise, we help organisations improve resolution times, reduce operational friction, strengthen service reliability, and scale more effectively.
Our goal isn't just greater automation. It's better service delivery, stronger operational efficiency, improved user and customer experience, and measurable business value from your technology investment.
Get in touch to explore how Canon Business Services ANZ can help make your Managed Services stronger with AI, better service performance, and long-term business value.
