Every IT leader is facing similar pressure. Boards want progress on AI, technology vendors are promoting autonomous agents, and organisations are trying to move beyond pilots that never reach production.
The question is no longer simply whether to deploy an AI agent. It is which process to start with and how to choose it without spending months on a use case that cannot deliver measurable value.
Why the First Use Case Matters
The adoption curve is accelerating. Gartner predicts that 40 per cent of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5 per cent in 2025.¹
That growth does not guarantee successful implementation. Gartner also predicts that more than 40 per cent of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls.²
Your first deployment therefore matters. It establishes how your organisation defines value, assigns accountability, manages risk and evaluates performance.
Rather than launching numerous loosely defined pilots, start with a small number of well-scoped opportunities. Each use case should have a named business owner, measurable outcomes, appropriate controls and a clear path into production.
The Four-Criterion Filter
Before committing to a use case, assess it against four practical criteria. A strong starting opportunity should perform well across all four.
1. Repetition at Volume
A process becomes a stronger automation candidate when the same steps are completed frequently and at meaningful scale.
Examples include password resets, access provisioning, onboarding checklists, routine service requests and recurring compliance evidence collection.
Volume improves the economics of automation. It also creates enough historical examples to test the agent, identify exceptions and measure improvement.
Processes that occur infrequently or require a different approach every time are generally weaker starting points.
2. Data Availability and Quality
An AI agent depends on the information, permissions and system access available to it.
Before selecting a use case, ask:
- Is the relevant data accurate and sufficiently complete?
- Is it structured consistently?
- Can it be accessed through supported integrations or APIs?
- Are the required permissions clearly defined?
- Is there enough historical data to test the workflow safely?
Data readiness is often a hidden obstacle. A use case may appear attractive until the team discovers that critical information is distributed across spreadsheets, emails and legacy systems with limited integration options.
Data also needs sufficient business context. Gartner has warned that inadequate semantic context can make AI agents inaccurate and inefficient while increasing governance risks and unnecessary spending.³
3. Clear and Measurable Outcomes
Success should be defined before development begins.
Strong use cases have measurable outcomes such as:
- Reduced average resolution time
- Higher first-contact resolution
- Lower manual handling volume
- Faster time to provision
- Improved onboarding completion
- Fewer missed compliance tasks
- Reduced cost per completed transaction
Avoid goals such as “improve productivity” unless they are supported by a specific baseline, target and measurement method.
Clear metrics create accountability. They also make it easier to decide whether the agent should be expanded, redesigned or discontinued.
4. Human Oversight Requirements
Not every workflow should be fully autonomous.
The appropriate level of human involvement depends on the consequences of an error, the sensitivity of the data, the agent’s permissions and the reversibility of its actions.
Define the oversight model before deployment. This should include:
- Actions the agent can complete independently
- Decisions that require approval
- Confidence thresholds for escalation
- Exceptions that must be routed to a person
- Logging and audit requirements
- Procedures for stopping or reversing an action
The EU AI Act entered into force on 1 August 2024 and introduces risk-based requirements for AI systems. Applicable high-risk systems are subject to obligations that include logging, documentation, risk management and appropriate human oversight.⁴ Some transparency obligations begin applying on 2 August 2026, while the implementation dates for certain high-risk system requirements extend beyond that date.⁵
Even when a use case is not legally classified as high risk, these principles provide a useful governance foundation.
Three Starting Points Worth Considering
IT Service Management
Tier 1 service requests can be a strong initial use case because they are often repetitive, measurable and supported by established workflows.
An appropriately scoped agent may help classify requests, retrieve relevant knowledge, complete approved routine actions, recommend resolutions or escalate unusual cases.
Modern IT service management platforms are also introducing agentic workflows that can diagnose issues using device and application information, conduct root-cause analysis and produce targeted resolution plans.⁶ ServiceNow also describes predictive AIOps capabilities that identify potential service issues and support automated remediation.⁷
The actual value will depend on process maturity, data quality and implementation design. Measure results against your existing baseline rather than relying on general industry performance claims.
HR Onboarding
Employee onboarding is another practical candidate because it is policy-driven, cross-functional and often repeated at scale.
A carefully governed onboarding agent may:
- Trigger approved IT provisioning tasks
- Send pre-boarding information
- Monitor incomplete forms or acknowledgements
- Answer common employee questions
- Notify owners when deadlines are approaching
- Escalate policy exceptions
The strongest implementations retain human approval for sensitive decisions and restrict the agent to clearly defined actions.
Metrics may include onboarding completion time, missed tasks, support enquiries, provisioning delays and manual follow-up hours.
Compliance Evidence Collection
Compliance evidence collection can involve significant repetitive work, including retrieving audit artefacts, preparing access review reports, verifying records and collating evidence from cloud or business systems.
An agent may help gather authorised information, organise evidence, flag missing items and maintain a record of completed actions.
This use case is particularly suitable when the evidence requirements are documented, system access is controlled and a human remains responsible for validating the final submission.
The value may be measured through hours saved per reporting cycle, evidence completeness, missed deadlines and the time required to respond to auditor requests.
A Simple Use Case Scorecard
Before approving a use case, score it against five questions.
- Repetition: Does the process occur frequently enough to justify automation?
- Data Access: Is the required data accurate, structured, accessible and integration-ready?
- Clear Outcomes: Can success be defined using measurable business or operational results?
- Oversight Design: Is it clear where human review, approval or escalation is required?
- Reversibility: Can an incorrect action be detected, stopped and corrected quickly?
Score well across all five and you have a production-ready candidate. Struggle on two or more, and you need more groundwork first.
Start Narrow, Then Expand
Successful agent adoption does not require automating an entire function at once.
Begin with one bounded workflow. Limit the agent’s permissions, define its approved actions, establish escalation rules and measure the result.
Once the organisation has demonstrated value and strengthened its governance capability, the agent can be extended into adjacent workflows.
The first deployment is not only a technology project. It is an opportunity to build organisational confidence, improve data readiness and establish a repeatable approach to AI governance.
The pressure to move quickly is real. Moving quickly in the right direction begins with choosing the right place to start.
Take the Next Step
Ready to identify your first production-ready AI agent use case?
Insentra’s AI Momentum practice helps IT leaders move from AI ambition to measurable outcomes through structured use case selection, governance and deployment.
Sources
- Gartner, “Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025”,
- Gartner, “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027”
- Gartner, “Gartner Says Lack of Semantics Causes Inaccurate Artificial Intelligence Agents and Wasted Spending”
- European Parliament and Council of the European Union, Regulation (EU) 2024/1689, Artificial Intelligence Act
- European Commission, “AI Act” and “Commission Publishes Guidelines on Transparency Obligations”
- ServiceNow, “Agentic AI in the DEX Application”, ServiceNow product documentation.
- ServiceNow, “Predict and Prevent Service Issues and Automate Remediation with Predictive AIOps”, ServiceNow product information.
