Agentic AI: The Shift from Task Automation to Service Orchestration

Read this guest blog by Erica Livermore, Head of Innovation at Sopra Steria, for techUK’s Tech and Innovation Focus Week 2026.

Much of the current AI conversation remains focused on copilots, productivity tools and content generation. But the more significant shift may be what agentic AI exposes about organisations themselves.

If AI agents are expected to coordinate work, make decisions, trigger actions and operate across systems, then the question is no longer simply whether the technology is ready. It is whether the organisation is ready.

For UK sectors, the opportunity is substantial. Agentic AI has the potential to move organisations beyond task automation and toward intelligent service orchestration: services that can interpret context, coordinate activity, initiate next steps and support outcomes across complex operational environments.

But this is also where the challenge sits. Many organisations are still structured around fragmented systems, siloed functions, legacy processes and manual coordination. In that context, agentic AI should not be treated as another technology layer to be added onto existing complexity. Its real value will come when it is used to reimagine how services are designed, governed and delivered.

The Real Opportunity Is Service Redesign

The most significant opportunities for agentic AI adoption are likely to emerge in sectors where operational complexity, administrative burden and fragmented service journeys are already constraining performance.

This includes public services, financial services, healthcare, business process services and large-scale enterprise operations. These are environments where work often moves across multiple systems, teams, policies, data sources and decision points. Traditional automation has helped improve efficiency in parts of these journeys. But it has often done so at task level. Agentic AI creates the possibility of something more significant: the orchestration of work across the service lifecycle.

That distinction matters. The opportunity is not simply to make existing processes faster. It is to ask whether those processes still make sense when intelligent agents can coordinate information, actions and decisions in different ways. Without that service redesign lens, organisations risk using agentic AI to accelerate complexity rather than resolve it.

Public Services: Designing Around Citizens, Not Departments

The UK public sector represents one of the most important opportunities for responsible agentic AI adoption. Many public services still require citizens to navigate organisational boundaries that make sense internally but feel fragmented externally. People are often expected to understand which department owns which process, what information is needed, where to provide it and how to chase progress.

The burden of coordination frequently sits with the citizen or the frontline worker. Agentic AI could help change that. Used responsibly, AI agents could support end-to-end case management, identify missing information, coordinate activity across departments, trigger next-best actions and manage routine communications.

The real opportunity is not simply faster processing. It is reducing the burden that fragmented services place on citizens and public servants. In areas such as local government, healthcare administration, social care and benefits processing, this could be significant. AI agents could help services coordinate around the person rather than the department, case type or system boundary.

However, this must be approached carefully. Public services operate in contexts where trust, fairness, transparency and accountability are essential. Agentic AI should augment human judgement, not obscure it. The aim should be better service outcomes, not simply lower administrative cost.

Financial Services: Intelligent Orchestration in Regulated Environments

Financial services is also well positioned for agentic AI adoption. Banks, insurers and other regulated institutions already operate across high-volume, rules-based and data-rich environments.

There are clear opportunities in onboarding, fraud investigation, claims handling, customer servicing, complaints management, compliance monitoring and operational risk. In these areas, agentic AI could coordinate information across systems, interpret unstructured data, recommend actions and support faster, more consistent outcomes.

But financial services will also be one of the defining tests of responsible deployment. The sector cannot adopt agentic AI purely on the basis of productivity. Explainability, auditability, resilience and accountability will be central. Organisations will need to demonstrate not only that AI agents can act, but that those actions are governed, monitored and capable of human intervention. In regulated environments, the value of agentic AI will depend on trust in the operating model around it.

Business Services: From Labour Arbitrage to Intelligent Service Propositions

Some of the most immediate disruption may occur in business process services and enterprise operations. For years, many service models have been built around process standardisation, labour optimisation and incremental automation. Agentic AI changes the nature of that conversation.

If AI agents can coordinate work, interpret information and trigger actions across the service lifecycle, then providers need to rethink what they are selling. The proposition can no longer be only about capacity, process execution or service desks. It becomes about intelligent service orchestration, outcome assurance and scalable operational capability. This has significant commercial implications.

Clients may become less interested in paying for inputs and more focused on outcomes, resilience, speed, quality and continuous improvement. That changes how services are designed, priced, governed and measured.

For service providers, the opportunity is substantial. But it requires more than adding AI into existing delivery models. It requires a rethink of propositions, tooling, operating models, workforce shape and evidence of value. The organisations that win in this space will be those able to connect AI capability to service outcomes, not those that simply describe AI functionality.

The Hidden Barrier: Automation Debt

For many organisations, agentic AI will not arrive on a blank canvas. It will arrive into environments already shaped by years of investment in robotic process automation, workflow tools, rules engines, macros, scripts, tactical integrations and manual workarounds.

Much of this delivered value. RPA and workflow automation helped organisations improve efficiency, reduce manual effort and create consistency in repetitive processes. But in many cases, automation was layered onto fragmented processes rather than used to redesign services end-to-end.

The result is a growing layer of automation debt. Many organisations now operate complex estates of brittle scripts, duplicated logic, point-to-point integrations and heavily maintained workflows that struggle to adapt when policies, systems or customer needs change. Agentic AI represents a shift away from deterministic, rules-based automation toward systems capable of reasoning, adapting and coordinating dynamically across operational environments.

That does not mean existing automation estates disappear overnight. But it does mean organisations will need to rationalise them. The question is not simply how to add agentic AI alongside RPA. It is where existing automation remains useful, where it becomes a constraint, and where it should be replaced by more adaptive forms of orchestration. For many enterprises, the next phase of transformation may involve not only adopting AI, but unwinding parts of the complexity created by previous transformation waves.

The Technical Levers: Architecture, Data, Identity and Observability

The technical challenge is much bigger than access to AI models. Agentic AI systems are only as effective as the environments in which they operate. If data is poor, systems are disconnected and processes are unclear, autonomous coordination becomes unreliable and difficult to scale.

The key technical levers are therefore foundational. Organisations need better data quality, clearer data ownership and more interoperable architectures. They need integration patterns that allow agents to operate safely across systems. They need identity and access models that define exactly what agents can see, do and trigger. Permissions become especially important. When AI agents are capable of acting, organisations need strong controls over authority, escalation and intervention. Observability is equally critical. Leaders and operational teams need to understand what agents are doing, why decisions are being made, where exceptions are occurring and when humans need to step in Without this, agentic AI becomes difficult to trust and difficult to govern.

The Organisational Levers: Operating Model, Accountability and Trust

While technical readiness matters, organisational readiness may be the greater constraint. Many organisations are still structured around siloed functions and fragmented accountability.

Agentic AI cuts across technology, operations, customer experience, risk, legal, workforce strategy and commercial performance. That means adoption cannot sit only with technology teams. It requires cross-functional ownership and a clear operating model. Organisations will need to define who is accountable for agent behaviour, who owns service outcomes, who manages exceptions, who monitors performance and who has authority to pause or change automated activity. Workforce trust will also be essential.

If agentic AI is framed purely as a cost reduction mechanism, organisations risk resistance and low adoption. If it is positioned as a way to remove operational burden, improve service quality and enable people to focus on judgement, empathy and complex problem-solving, the adoption conversation becomes more constructive. The workforce impact is real and should not be minimised. But responsible deployment should focus on redesigning work, not simply replacing tasks.

The Policy Lever: Governance as an Operational Capability

The UK has an opportunity to lead in responsible AI adoption, particularly in regulated and service-intensive sectors. But this will require practical governance, not just ambition.

Responsible deployment will not be achieved through policy documents alone. Organisations need live governance: clear accountability, audit trails, human intervention points, escalation routes, monitoring, assurance and the ability to pause or override agentic activity when required.

A risk-based approach is essential. Higher-impact use cases, particularly in healthcare, financial services and public sector decision-making, should require stronger transparency, explainability and human oversight. Lower-risk internal use cases may be able to move faster. The policy environment should enable innovation while protecting public trust. Too much uncertainty may slow adoption. Too little oversight may damage confidence.The priority should be practical standards and assurance mechanisms that help organisations deploy agentic AI safely, consistently and at scale.