Listen to a short audio summary (4 min)
Prefer reading? The full article continues below.
Recently, I wrote that AI can make service management more intelligent, but it doesn’t make it unnecessary.
That raises a practical question:
When AI takes on more of the execution, how do we help people contribute more value?
Organizations have hired and developed people to support services, resolve incidents, manage changes and keep operations running. As AI becomes part of that work, some activities will require less manual effort. Others may be automated entirely.
The opportunity to optimize is real. So is the concern among people whose work is changing.
Both deserve attention in the same conversation.
This article reflects my perspective on how AI and people can work together in service management. Many of these considerations are common across organizations, but the right approach will vary with the service, its risks and the people involved. Other approaches may deliver even greater value—the aim is to open a practical conversation and learn from each other.
A Familiar Scenario
A recurring issue affects a collaboration service.
Tickets arrive. Several describe the same symptom differently. Some reach the wrong team. An incident is raised, engineers review logs, someone prepares stakeholder updates, and a change is proposed.
Much of the effort goes into gathering information, documenting it and passing it between teams.
AI is already supporting these activities. ServiceNow’s current ITSM documentation lists incident summaries, resolution-note generation, change-request summaries and change-risk explanations among its generative AI capabilities.
But a summary alone does not tell us whether a critical business process is interrupted, whether the proposed fix addresses the cause, or whether releasing it now is sensible.
Those questions require technical knowledge, business context and judgment.
As execution becomes more automated, organizations need to develop that capability alongside it.
Where the Work Can Evolve
Here is a practical way to think about the balance.
| Area | AI and automation can help with | People can focus more on |
|---|---|---|
| Ticket management | Classification, routing, summaries and execution of approved fixes | Complex diagnosis, exceptions and employee impact |
| Incident and problem management | Gathering evidence, suggesting investigative steps and preparing timelines | Coordinating recovery, validating causes and preventing recurrence |
| Change and CI/CD | Preparing records, supporting test creation and executing controlled workflows | Defining release criteria, assessing business risk and managing exceptions |
| Continual improvement | Identifying recurring patterns and repetitive manual work | Choosing improvements and measuring their effect |
| Agent-driven operations | Executing actions within defined permissions | Setting boundaries, monitoring performance and intervening when needed |
The balance will depend on the service and its risks. It should evolve as the organization gains evidence that the automation works reliably.
Optimization Should Include People Development
Organizations will naturally look at AI through cost and productivity.
If work can be completed reliably with less effort, preserving the manual process simply because it is familiar makes little sense.
But there is usually more useful work waiting.
Recurring incidents need investigation. Knowledge articles need updating. Monitoring gaps remain unresolved. Recovery procedures need testing. Service improvements stay in the backlog because daily operations consume the team’s time.
Automation can create room to address some of these weaknesses.
The question is what the organization will do with that capacity—and whether people are equipped to use it.
This makes learning and development part of the transformation roadmap.
A service desk analyst needs to know how to check an AI-generated summary and recognize an unsuitable recommendation. An engineer needs to validate generated code and tests. A service owner needs to understand how agent permissions, escalation paths and failures affect the service.
NIST’s AI Risk Management Framework explicitly addresses AI risk-management training, leadership responsibility and defined roles for human oversight.
Training should connect to actual responsibilities, with protected time to practise and opportunities to apply new skills.
Employees should also participate in automation design. They know the exceptions, workarounds and operational details that process documentation often misses.
For me, this is a stronger value proposition: use AI to improve execution while developing people to investigate, improve and govern the service.
Employees have a part to play in developing their skills. Organizations have a part to play in making that development achievable.
Touchless Change Still Needs Ownership
Change management is a useful example.
Continuous integration and continuous delivery or deployment—CI/CD—already support automated testing and deployment workflows. Azure Pipelines provides approvals and checks that control whether deployment stages proceed. AI can assist with surrounding activities, but the pipeline controls themselves are established automation.
For a repeatable, lower-risk deployment, a workflow could proceed when defined tests and policy checks pass, with exceptions routed for review.
Someone still needs to decide which changes qualify, what evidence is required, when execution must stop and how recovery will work.
ITIL guidance supports AI-enabled service management while emphasizing oversight, accountability and risk management.
Touchless doesn’t mean ownerless.
Reducing manual intervention should go together with clear service ownership and tested controls.
An Honest Conversation About Jobs
People are understandably concerned when activities they were hired to perform become easier to automate.
I do not think we can promise that AI will never replace anyone.
Some tasks will disappear. Roles may change significantly. Some organizations may reduce staffing.
The ILO’s 2025 occupational-exposure index identified job transformation as the most likely impact of generative AI. Its June 2026 evidence review finds that large-scale job displacement remains limited in the evidence reviewed, while highlighting uneven productivity gains and risks to employment opportunities and job quality. Neither finding guarantees security for an individual role.
What leaders can offer is a responsible transition: explain what is changing, involve the people affected, provide relevant learning and identify realistic opportunities in revised roles.
“Move into higher-value work” is only useful advice when people have somewhere to move and the support to get there.
Measure What Actually Improves
Time saved matters, but the service should tell us whether the investment is working.
Are repeat incidents decreasing? Is recovery faster? Are releases more reliable? Are employees receiving better support?
Incorrect routing, unsuccessful automated fixes, rework and unnecessary rollbacks also need attention. Faster execution has little value if it creates more problems downstream.
For the workforce, course completion is an early indicator. The stronger measure is whether people can apply what they have learned, assess AI outputs and manage the revised workflows confidently.
That gives leaders a fuller picture of optimization.
Closing Thoughts
AI creates an opportunity to rethink how service work gets done.
Organizations can reduce repetitive effort and improve operations while developing the people who understand their services.
That requires deliberate choices about investment, roles, learning and accountability.
The automation roadmap and the people-development roadmap should move together.
That is where I would start. Role design, learning pathways and the operating model for agents deserve a deeper discussion in a future post.
References
ServiceNow — Using ITSM Generative AI Skills — Official documentation, updated 10 September 2026.
Microsoft Learn — Azure Pipelines Approvals and Checks — Official deployment-control documentation.
PeopleCert — Official ITIL Frequently Asked Questions — Current guidance on ITIL, AI-enabled environments and governance.
NIST — AI Risk Management Framework Core — Foundational guidance from AI RMF 1.0 (2023); GOVERN sections 2.2, 2.3 and 3.2 cover training, leadership responsibility and human oversight.
ILO — Generative AI and Jobs: A Refined Global Index of Occupational Exposure — Published 20 May 2025.
ILO — The Impact of GenAI on Jobs, Productivity and Work Organization: A Review of the Empirical Evidence — Published 1 June 2026.
Related Reading
AnywhereExchange —The Digital Workplace Is Becoming an AI Workplace
No comments:
Post a Comment