Most organisations are still adapting to AI. Initial experiments with the technology have focused on chatbots, pilots and small-scale proofs of concept. While these were useful for building awareness, they rarely shifted the dial on business performance in a significant way – until now.
For most firms, the question is no longer whether they should adopt AI, but how deeply it should be embedded in core operations. This is why agentic AI has become the business buzzword of the year. But unlike many trends, this one has the potential to be a genuine differentiator for organisations.
Agentic systems move beyond narrow automation to create autonomous workflows. They don’t just execute tasks, they set steps, adapt dynamically and are capable of achieving outcomes with minimal human input. Gartner forecasts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues. That level of independence marks the dawn of AI autonomy, where agents become collaborators rather than tools.
“We’ve moved from that chatbot phase of AI into thinking about how we embed it into our core workflows,” explains Amanda Grant, chief product officer at OneAdvanced. “The differentiator now isn’t whether you use AI, but how deeply and intelligently it’s integrated into your operating model.”
For business leaders in the middle of the AI journey, agentic AI represents the critical inflection point. Getting it right means shifting from proof-of-concepts to structured workflows, from generic tools to sector-aware agents, from cost savings to customer experience and from fear of risk to confidence in trust.
Here, we lay out how agentic AI is moving from hype to practical business advantage, with six key shifts that business decision-makers should pay attention to.
The shift from experimentation to real value
The last wave of AI pilots helped test what was possible but often sat on the edge of the business. Now, organisations are embedding AI directly into workflows. Instead of a chatbot in a corner, businesses want systems that operate “in the flow of work”.
That change requires integration with data, applications and decision-making processes. The focus is on AI’s measurable value.
Organisations that just experiment with AI without a focused plan will probably fail to achieve their goals
With expertise in healthcare, government and legal services, OneAdvanced helps users design AI-enabled processes that complete real-world, business-critical jobs rather than experimental side projects. “Organisations that just experiment with AI without a focused plan will probably fail to achieve their goals,” says Grant. “Real progress comes when you understand the critical jobs to be done – summarising a medical document, progressing a legal case, processing a complaint and so on – and build AI into that flow of work.”
Agentic AI as a business differentiator
An AI agent handles a single task, such as summarising a document. An agentic system goes further: it identifies the problem, plans a sequence of steps and executes them autonomously.
That means processes can run continuously without waiting for human intervention. A single engineer could orchestrate a team of AI coders – one writing code, another testing it, a third checking architecture and so on. Humans become the conductors, not the performers.
“Agentic AI is a huge productivity gain because you can scale up without bottlenecks. Agents can work overnight while your team rests. That makes them genuine business differentiators,” says Grant.
Crucially, the advantage doesn’t come from generic tools. Success depends on sector-specific AI agents that understand industry knowledge, regulations and workflows.
From cost savings to customer impact
Early AI adoption was framed in terms of efficiency and cost-cutting. While savings still matter, the new frontier is customer experience.
Customers expect faster resolution, personalised interactions and predictive outcomes. Agentic AI, with its ability to anticipate needs and take action, sets a new standard.
Gartner’s forecast that eight out of 10 customer service issues will be resolved autonomously in the not-too-distant-future implies not just cost savings but a fundamental shift in expectations. The dream is that waiting in a queue for a human agent may soon be a thing of the past.
According to Grant, the conversation has moved from cost efficiency to customer value. For example, in healthcare, the goal isn’t just reducing admin time but improving patient outcomes by giving clinicians better information and more time with patients.
The importance of trust and security
Autonomy brings both opportunity and risk. For instance, an agent instructed to maximise performance might spin up a prohibitively expensive server unless boundaries are set. That’s why guardrails are non-negotiable.
“The more autonomy you give, the more you must have these rules in place. With agentic AI, trust and security step up a notch,” says Grant.
Every agent should operate against well-defined goals. At the same time, there should be clear limits on what the AI can and cannot decide on its own. At critical junctures, humans provide the judgment and accountability that machines cannot replicate.
Security also extends to the data itself. Controls, such as detecting and masking personally identifiable information, protect sensitive inputs and outputs from misuse. Auditability is equally important. Every decision made by an agent must be traceable, with transparency of the reasoning process and the data that informed it.
Compliance, too, is essential to ensure that systems remain lawful as well as trustworthy. Finally, strong cyber defences must underpin the entire stack, from the models themselves through orchestration layers to continuous monitoring.
By combining risk-based permissions with transparency and auditability, organisations can grow without losing control.
Real-world proof, not hype
Agentic AI is already delivering results. Take UK GP surgeries, where OneAdvanced has deployed its Workflow Assist systems.
Each surgery receives hundreds of electronic documents per week, ranging from specialist letters to hospital updates. Staff must summarise them, triage urgency, identify medication changes and assign SNOMED clinical codes – universal reference numbers that translate written medical details such as symptoms, diagnoses and procedures into a standardised code that both humans and computers can understand – from a library of 360,000 terms. The process is time-consuming, can be prone to error and adds to NHS staff pressures.
OneAdvanced’s system uses two AI agents: one summarises documents, flagging urgency and next steps, while another suggests relevant SNOMED codes.
But humans still validate outputs and 90% of user feedback has been positive on accuracy. With estimated average time value savings of £12,000 per practice per year, and even greater improvements in patient care, the solution is proving its worth.
“What matters is not just saving admin time,” says Grant. “It’s the better outcomes – if GPs get clearer summaries and accurate coding, patients get better care. That’s the real value of agentic AI.” See the detailed case study below for more on this deployment.
How agentic AI is transforming GP surgeries
The NHS faces a perfect storm: constrained funding and staff shortages set against a backdrop of rising demand. GP surgeries are on the frontline of this pressure.
Every day, surgeries process up to 600,000 documents nationwide – referral letters, hospital discharge notes and more. Each document must be read, assessed, summarised, coded and filed into patient records.
With 360,000 SNOMED clinical codes to choose from, accuracy matters. Coding errors can impact patient care and even funding, as certain codes drive resource allocation. Yet the manual workload drains staff time and risks burnout.
OneAdvanced worked with GP surgeries to co-develop the Workflow Assist system. It includes two AI agents: one that processes and condenses multi-page letters into clear summaries, highlighting urgency, medication changes and next steps, and one that suggests the correct SNOMED clinical codes to align records and ensure consistency.
A human remains in the loop to validate outputs, ensuring safety and building trust. Over time, more autonomy will be added to the AI.
“At the moment we’ve got humans validating, but the next stage is agentic – where the system doesn’t just suggest but also acts, like auto-filing non-urgent letters,” says Grant. “But it’s all about trust. That’s why auditability and transparency are essential.”
Workflow Assist has been transformative. Users report a 90% approval rating for the accuracy of processed documents. On average, each practice can save £12,000, largely thanks to reduced time spent on administrative tasks. Patients benefit, too, with GPs receiving clearer information more quickly, which leads to better outcomes. Notably, 35% of new customers now come through referrals, reflecting strong trust and satisfaction.
Grant says: “The cost savings are significant at a national scale, but the bigger impact is freeing up GPs to spend more time with patients – potential capacity to deliver 150,000 more patient appointments a week. That’s what matters most.”
The deployment succeeds because it meets three key criteria. First, it focuses on clearly defined jobs to be done – summarisation, coding and filing – each with measurable impact.
Second, clinicians remain in the loop, retaining oversight that ensures trust in the system.
Third, the solution reflects OneAdvanced’s understanding of GP workflows, regulation and funding models. Unlike AI for the sake of it, this system delivers real-world results precisely because it is tailored to the context.
The roadmap includes adding greater autonomy, such as automatic filing of non-urgent documents and workflow orchestration. Each step will balance increased automation with the safeguards of auditability and transparency.
Ultimately, this case demonstrates how agentic AI can move from hype to genuine transformation – not by replacing humans but by enabling them to focus on higher-value work.
Preparing for the future of work
Headlines about “the end of the entirely human workforce” miss the point. The future is not human versus AI, but human with AI.
Agentic systems will handle repetitive and predictable work, freeing people to focus on creativity, strategy and complex decision-making. Roles will evolve, where an engineer can become the orchestrator of a team of digital coders, not the sole producer.
Grant explains: “Organisations must focus AI on the jobs to be done, embed it in the flow of work and build trust through transparency. That’s how businesses will unlock the real promise of AI autonomy.”
Beyond the pilot phase: scaling with confidence
What distinguishes organisations making real progress with agentic AI is not experimentation but scale.
Moving from pilots to production requires confidence that the system can deliver consistently, safely and in compliance with regulation. That’s why the focus is increasingly on auditability, trust and sector expertise. Leaders want to know not just that an agent can perform a task once, but that it can repeat it thousands of times across complex workflows, without drift or unintended consequences.
Equally important is the change management that accompanies deployment. Agentic AI isn’t just a technology upgrade – it reshapes roles, responsibilities and even measures of performance. Successful organisations invest as much in training and culture as in algorithms. They recognise that humans remain accountable and that transparency is essential to maintain trust with employees, regulators and customers.
By moving from AI experiments to purposeful agentic solutions, firms can unlock the transformative potential of the technology.
For more information please visit oneadvanced.com
Most organisations are still adapting to AI. Initial experiments with the technology have focused on chatbots, pilots and small-scale proofs of concept. While these were useful for building awareness, they rarely shifted the dial on business performance in a significant way – until now.
For most firms, the question is no longer whether they should adopt AI, but how deeply it should be embedded in core operations. This is why agentic AI has become the business buzzword of the year. But unlike many trends, this one has the potential to be a genuine differentiator for organisations.
Agentic systems move beyond narrow automation to create autonomous workflows. They don’t just execute tasks, they set steps, adapt dynamically and are capable of achieving outcomes with minimal human input. Gartner forecasts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues. That level of independence marks the dawn of AI autonomy, where agents become collaborators rather than tools.
