For too long, the conversation about artificial intelligence has been stuck in the experimentation phase. Businesses from every industry and geography have explored its potential through countless pilots and proofs of concept, but this cautious exploration is no longer enough. The real transformation – the kind that reshapes industries and redefines what is possible – begins when we move AI from a theoretical exercise to an active participant in our daily work. Now is the time for leaders to stop experimenting with AI and start embedding it into the very fabric of their operations.
Agentic AI, which harnesses autonomous agents to plan and execute tasks within your existing software, is available and ready for deployment. This is not a call for a radical overhaul or a leap of faith, but for a practical, considered, and decisive step forward. The distinction is vital: agentic AI is not about replacing your people with technology. It is about using advanced tools to give your teams more bandwidth, freeing up their time for higher-value work, and allowing organisations to deliver better, faster, and more reliable services.
Consider the UK’s health service, for instance. The NHS’s frontline staff are under immense and growing pressure, tasked with meeting rising patient demands, sometimes with limited resources. Manual administrative tasks, such as processing referrals or updating records, still occupy significant portions of clinical and support staff’s time, hours that could be spent on direct patient care. By embedding agentic AI into these administrative workflows, the NHS can automate the bulk of repetitive tasks in patient referrals, appointment scheduling, and initial triage. Importantly, any ambiguous or high-risk case is automatically flagged for human review, ensuring safety and accountability. The result? Quicker appointments, more time for clinical teams, and greater accuracy in medical records – a win for staff and patients alike.
And it’s not just healthcare. The same logic applies wherever people are buried in repetitive work that technology can now manage in a fraction of the time.
In government, the efficiency offered by agentic AI becomes even more compelling. Local authorities grapple with high volumes of services – from council tax handling to benefit applications – each governed by complex, ever-changing rules. An agent equipped with domain knowledge can crosscheck eligibility, flag inconsistencies, and process routine claims in minutes, not days. Through design, these agents understand regulatory nuances and know when to escalate for human approval, ensuring compliance without unnecessary delays. For example, an unusually complex housing benefit application that requires local nuance or judgment isn’t simply rejected but automatically surfaced for staff intervention. This is technology working for people, not against them.
Legal services have also begun to witness tangible advances. Many firms are already using AI to automate initial document review, helping to sift through thousands of pages in litigation or due diligence far more efficiently than manual review ever could. With agentic AI, this goes further. Agents can sequence the review, flag exceptions, and pass only the most ambiguous or risk-laden contracts to lawyers for review and sign-off. This helps legal professionals focus on strategy and advocacy, rather than drowning in paperwork, while clients benefit from quicker response times and lower costs.
Of course, moving beyond pilots is not always straightforward. Inertia, concern for job security, worries about regulatory compliance, and the very real challenge of integrating new tools into complex legacy systems have all slowed AI adoption in critical sectors. Addressing these issues requires a straightforward yet robust approach.
First and foremost, success with agentic AI demands solutions that are tailored, not generic. AI trained on general datasets or created without sector expertise creates more problems than it solves. Agents must be designed with a deep appreciation for existing workflows, sector regulations, and the local context in which they operate. This is why customisation, ongoing training, and domain immersion are essential for developers and implementation teams. Engaging with end-users early on, listening to pain points, concerns, and operational realities, can drastically reduce friction and ensure a smooth transition.
Security and data sovereignty also loom large, especially in sectors like health and government where data privacy is sacrosanct. The shift towards Sovereign LLMs – large language models that process and store data within national borders on secure, trusted infrastructure – is a cornerstone of safe AI deployment. Alongside this, Model Context Protocols (MCPs) provide a secure framework to ensure that agents call only the specific data or tools they and the user are authorised to use, reducing the risk of policy breaches. Secure APIs enable these agents to integrate across a patchwork software, providing both power, performance and flexibility.
There are also cultural challenges to consider. Successful organisations do not simply ‘hand over’ tasks to AI, they build a culture where AI is a partner in their mission. This requires transparency, ongoing upskilling, and clear communication. Users need to understand not just how the agent makes decisions, but how exceptions are managed, and what the escalation path is when human expertise is critical. Training, clear user interfaces, and strong governance play major roles here.
Crucially, adopting agentic AI requires focusing on the right metrics. Pilots often fail because success is measured in ambiguous terms, such as tasks automated or theoretical improvements. In live workflows, focus must shift to tangible, measurable Key Performance Indicators (KPIs): How much faster are cases processed? Are exception rates dropping? Is accuracy improving, as measured by error reduction or fewer complaints? Are staff and user satisfaction scores increasing? By building a KPI-driven feedback loop, organisations secure leadership buy-in and create a blueprint for scaling success.
The benefits of this transformation stretch further than numbers. To return to the previous sector examples where OneAdvanced specialises, in the NHS, faster decision-making and more efficient triage help reduce patient waiting times, freeing clinical teams to provide the compassionate care that matters most. In government, faster handling of benefits or local services supports financial wellbeing and public trust in institutions. In legal services, faster review times and lower error rates enhance client relationships, reduce risk, and unlock new opportunities. These are direct benefits to people, not just process improvements.
As with all technology, agentic AI is not a silver bullet. It requires informed leadership, a structured implementation plan, and a willingness to adapt. But the time has come to move beyond half-measures. The technology is proven. The frameworks are secure. The examples are multiplying – and the risks of standing still are growing clearer. Transformation leaders who grasp this opportunity will deliver not only greater productivity but deeper trust, better services, and a more resilient organisation ready for whatever the future brings.
The era of AI pilots is over. Agentic AI is no longer a promise; it is a proven partner. The next step, for those ready to turn potential into performance, is simply to act.
Astrid Bowser
Astrid Bowser is a Principal Product Manager at OneAdvanced, overseeing software and AI initiatives. With a degree in Computer Science and an MBA from Warwick University, she seamlessly blends technical expertise with strategic business acumen. She also serves as Co-Chair of OneAdvanced’s AI Steering Committee, playing a key role in defining and driving the company’s AI-focused vision.
