Agentic AI represents a meaningful opportunity for IT services firms. Unlike rule-based automation or early-stage ‘copilots’, agents can handle complex, multi-step workflows such as autonomous customer support, code review and incident response. The potential to reshape how services are delivered and how teams operate using these tools is real.
So, what does taking an agent from pilot to production look like in practice, and what separates the successful projects from the ones that stall? Our advisers have worked on several of these deployments, and for firms that get the foundations right, the competitive advantage can be substantial.
Moving beyond automation to true autonomy
Unlike earlier automation solutions, agentic AI allows systems to set goals and execute independently, rather than following a fixed set of rules. Where robotic process automation (RPA) or first-generation copilots required human direction at each step, agents can navigate complex, multi-step workflows and adapt to changing conditions without intervention.
For IT services firms, this opens up significant new applications. Managing end-to-end incident response, conducting code reviews or handling customer support queries that require contextual judgement rather than scripted responses are all within scope.
Significant capital is flowing into agentic AI as a result, and startups in the space are attracting investment on the promise of rapid returns. But deployment tells a different story; Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls.
Whether the current pace of investment is sustainable is a topic for another day. For now, the more pressing question for IT services leaders is how to be among the firms that make it through to deployment.
Why do most agentic deployments stall?
A combination of inadequate infrastructure, data and governance are usually at the root of failed deployments.
Infrastructure and data
Most existing enterprise systems simply were not designed for agentic interaction. Application programming interfaces (APIs) built for occasional, human-directed requests struggle when AI-powered agents need to query systems at scale.
Traditional data warehouses compound this issue, having been built for human workflows rather than autonomous systems requiring real-time context. Data integrity presents a further challenge, as agents depend on accurate, accessible data to function reliably. Gaps in quality or governance can undermine even well-architected deployments.
Governance gaps
Conventional IT frameworks lack the necessary guardrails for systems that make independent decisions. Many firms are deploying agents without first establishing who is accountable when those decisions go wrong.
Agent washing
On a market level, vendors rebranding basic automation as agentic AI are obscuring genuine value and making it harder for firms to identify real capability. According to McKinsey, key markers of a genuinely agentic deployment include multiagent orchestration, vendor-agnostic architecture, cost monitoring and autonomy controls.
Understanding what genuine agentic capability looks like is the starting point. Getting the implementation foundations right is what separates pilots from production.
Deployment discipline
GOV.UK’s AI Insights guidance states that deploying straight from proof-of-concept prototypes to production is one of the most common causes of failure. Once a system is working, teams often assume the hard work is done — but pilots that haven’t been adequately tested or profiled rarely meet the demands of a live environment.
What separates successful implementations?
The firms that succeed tend to share a clear set of execution principles. Crucially, they put these in place early rather than trying to retrofit them later.
Process redesign over process layering
The firms making it to production treat agentic AI as an opportunity to redesign processes entirely. Rather than asking where agents can assist, they ask what a function would look like rebuilt around them. The resulting efficiency improvements can go well beyond what task-level automation delivers.
Hybrid human-digital delivery
Successful implementations often share a hybrid human-digital model where agents handle execution, and humans focus on exceptions and governance. Delivery is cross-functional, with domain experts, process designers and AI engineers working together as transformation teams rather than in silos.
Centralised governance
Early RPA taught us that automation proliferating without governance quickly becomes fragmented and unmanageable. Firms that establish autonomy levels, decision boundaries and audit mechanisms before scaling are far less likely to see the same pattern repeat with agentic AI.
Strategic execution determines outcomes
Agentic AI represents a genuine step-change for IT services firms. However, competitive differentiation will come from execution quality — the how, not the what.
The firms that will benefit are those addressing infrastructure, governance and culture simultaneously rather than sequentially. First-mover advantage can be a reality, but only for firms embedding proprietary agents into client workflows with the discipline to back it up.
Opus Strategic Advisory can provide guidance on process redesign, architectural foundations, governance frameworks and more. We have offices nationwide with technology services experts who can discuss options with you. Contact us on 0203 995 6380 to get immediate assistance from our Partner-led team.