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.\u00a0<\/p>\n\n\n\n
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. <\/p>\n\n\n\n
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.\u00b9\u00a0<\/p>\n\n\n\n
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\u00a0value\u00a0or inadequate risk\u00a0controls.\u00b2\u00a0<\/p>\n\n\n\n
Your first deployment therefore matters. It\u00a0establishes\u00a0how your organisation defines value, assigns accountability, manages\u00a0risk\u00a0and evaluates performance.\u00a0<\/p>\n\n\n\n
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. <\/p>\n\n\n\n
Before committing to a use case, assess it against four practical criteria. A strong starting opportunity should perform well across all four.\u00a0<\/p>\n\n\n\n
A process becomes a stronger automation candidate when the same steps are completed frequently and at meaningful scale.\u00a0<\/p>\n\n\n\n
Examples include password resets, access provisioning, onboarding checklists, routine service\u00a0requests\u00a0and recurring compliance evidence collection.\u00a0<\/p>\n\n\n\n
Volume improves the economics of automation. It also creates enough historical examples to test the agent,\u00a0identify\u00a0exceptions and measure improvement.\u00a0<\/p>\n\n\n\n
Processes that occur infrequently or require a different approach every time are generally weaker starting points. <\/p>\n\n\n\n
An AI agent depends on the information,\u00a0permissions\u00a0and system access available to it.<\/p>\n\n\n\n
Before selecting a use case, ask: <\/p>\n\n\n\n
Data readiness is often a hidden obstacle. A use case may appear attractive until the team discovers that critical information is distributed across spreadsheets,\u00a0emails\u00a0and legacy systems with limited integration options.\u00a0<\/p>\n\n\n\n
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.\u00b3\u00a0<\/p>\n\n\n\n
Success should be defined before development begins. <\/p>\n\n\n\n
Strong use cases have measurable outcomes such as: <\/p>\n\n\n\n
Avoid goals such as \u201cimprove productivity\u201d unless they are supported by a specific baseline, target and measurement method.\u00a0<\/p>\n\n\n\n
Clear metrics create accountability. They also make it easier to decide whether the agent should be expanded, redesigned or discontinued. <\/p>\n\n\n\n
Not every workflow should be fully autonomous. <\/p>\n\n\n\n
The\u00a0appropriate level\u00a0of human involvement depends on the consequences of an error, the sensitivity of the data, the agent\u2019s permissions and the reversibility of its actions.\u00a0<\/p>\n\n\n\n
Define the oversight model before deployment. This should include: <\/p>\n\n\n\n
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\u00a0oversight.\u2074\u00a0Some transparency obligations begin applying on 2 August 2026, while the implementation dates for certain high-risk system requirements extend beyond that\u00a0date.\u2075\u00a0<\/p>\n\n\n\n
Even when a use case is not legally classified as high risk, these principles provide a useful governance foundation. <\/p>\n\n\n\n
Tier 1 service requests can be a strong\u00a0initial\u00a0use case because they are often repetitive, measurable and supported by established workflows.<\/p>\n\n\n\n
An appropriately scoped agent may help classify requests, retrieve relevant knowledge, complete approved routine actions, recommend resolutions or escalate unusual cases. <\/p>\n\n\n\n
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\u00a0plans.\u2076\u00a0ServiceNow also describes predictive AIOps capabilities that\u00a0identify\u00a0potential service issues and support automated\u00a0remediation.\u2077\u00a0<\/p>\n\n\n\n
The actual value will depend on process maturity, data\u00a0quality\u00a0and implementation design. Measure results against your existing baseline rather than relying on general industry performance claims.\u00a0<\/p>\n\n\n\n
Employee onboarding is another practical candidate because it is policy-driven, cross-functional and often repeated at scale. <\/p>\n\n\n\n
A carefully governed onboarding agent may: <\/p>\n\n\n\n
The strongest implementations retain human approval for sensitive decisions and restrict the agent to clearly defined actions. <\/p>\n\n\n\n
Metrics may include onboarding completion time, missed tasks, support enquiries, provisioning\u00a0delays\u00a0and manual follow-up hours.\u00a0<\/p>\n\n\n\n
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. <\/p>\n\n\n\n
An agent may help gather authorised information, organise evidence, flag missing items and maintain a record of completed actions. <\/p>\n\n\n\n
This use case is particularly suitable when the evidence requirements are documented, system access is controlled and a human remains responsible for validating\u00a0the final submission.\u00a0<\/p>\n\n\n\n
The value may be measured through hours saved per reporting cycle, evidence completeness, missed deadlines and the time required to respond to auditor requests. <\/p>\n\n\n\n
Before approving a use case, score it against five questions. <\/p>\n\n\n\n
Score well across all five and you have a production-ready candidate. Struggle on two or more, and you need more groundwork first. <\/p>\n\n\n\n
Successful agent adoption does not require automating an entire function at once. <\/p>\n\n\n\n
Begin with one bounded workflow. Limit the agent\u2019s permissions, define its approved actions, establish escalation rules and measure the result. <\/p>\n\n\n\n
Once the organisation has demonstrated value and strengthened its governance capability, the agent can be extended into adjacent workflows. <\/p>\n\n\n\n
The first deployment is not only a technology project. It is an opportunity to build organisational confidence, improve data readiness and\u00a0establish\u00a0a repeatable approach to AI governance.\u00a0<\/p>\n\n\n\n
The pressure to move quickly is real. Moving quickly in the right direction begins with choosing the right place to start. <\/p>\n\n\n\n
Ready to identify your first production-ready AI agent use case? <\/p>\n\n\n\n