Hudson and Hayes was engaged by a large UK-based public services organisation to support its AI transformation journey. The organisation aimed to enhance its employability services, improve participant experience, and strengthen job outcomes through the effective use of AI.
The organisation sought to identify where AI could deliver the greatest impact across the end-to-end participant journey, while ensuring solutions were scalable, practical, and aligned to its digital maturity and operational priorities.
Hudson and Hayes began with a six-week diagnostic, delivered in close collaboration with key stakeholders. This phase focused on analysing the participant journey and identifying opportunities for AI and automation across service design and operations.
To demonstrate early value, Hudson and Hayes developed a AI solution of concept.
Building on the proof of concept, HudsonandHayes supported the organisation through the first wave of implementation, moving from experimentation into deployment.
The programme delivered strategic and operational impact:
Moving from deployment to meaningful impact
Across the NHS, interest in Microsoft Copilot continues to grow. Trusts are exploring licences, digital teams are launching pilots, and AI is increasingly present in board-level conversations about productivity and transformation. However, many organisations are discovering that enabling Copilot technically is only the first step. The more complex challenge is ensuring that it is meaningfully adopted across clinical, operational, and corporate teams.
Deployment alone does not guarantee impact. Without a structured approach to adoption, even the most advanced tools risk becoming underused or misunderstood.
Below are the core principles that consistently drive successful Copilot adoption in NHS environments.
Adoption improves significantly when Copilot is introduced as a solution to existing operational pressures rather than as a standalone innovation initiative.
The NHS faces well-documented challenges, including administrative burden, documentation overload, meeting fatigue, policy drafting demands, and increasing reporting requirements. When Copilot is positioned as a practical tool to alleviate these pressures, staff are far more likely to engage with it. The focus should not be on what the technology can do in theory, but on how it can reduce specific pain points in daily workflows.
Early wins matter. When individuals experience tangible time savings within their first few uses, confidence builds naturally.
Vague guidance such as “use Copilot to improve productivity” is insufficient to drive behavioural change. Staff need clarity around how the tool fits into their specific responsibilities.
Successful adoption programmes identify concrete use cases tailored to different roles.
For example, operational managers may use Copilot to structure board reports, summarise long email threads, or convert meeting notes into action plans. Clinical leaders may use it to draft service improvement proposals, prepare briefing papers, or summarise lengthy policy documents. Corporate teams may rely on it for drafting communications, refining policy documents, or analysing large volumes of text-based information.
When expectations are clearly defined, hesitation decreases and usage becomes more consistent.
In complex systems such as the NHS, leadership behaviour has a direct impact on organisational culture. If Copilot usage is perceived as optional or experimental, adoption will remain limited.
However, when senior leaders actively use Copilot and openly discuss how it supports their work, it sends a clear signal that AI tools are both legitimate and encouraged. Leaders who demonstrate how they draft reports, prepare meeting summaries, or structure communications using Copilot help normalise its use across the organisation.
Cultural permission often determines adoption more than technical capability.
Concerns around data privacy, clinical safety, and information governance are both valid and necessary within NHS settings. Avoiding these concerns or addressing them too late can undermine trust.
A structured approach should include clear guidance on appropriate use cases, data handling boundaries, and mandatory human review processes. When staff understand the guardrails within which Copilot operates, they are more likely to engage with confidence.
Clear governance frameworks accelerate adoption by reducing uncertainty.
While formal training sessions are important during initial rollout, sustained adoption depends on reinforcement over time.
Embedding short demonstrations into team meetings, sharing internal examples of effective prompts, and appointing champions within departments are practical ways to encourage ongoing engagement. When colleagues see peers benefiting from Copilot in real scenarios, usage becomes more organic and less directive.
Adoption grows through visibility, repetition, and peer validation.
Licence activation rates alone do not reflect meaningful adoption. Organisations should instead focus on operational indicators such as reduced time spent drafting reports, improved turnaround times, lower email backlogs, and qualitative staff feedback on workload relief.
When improvements are measurable and clearly linked to Copilot usage, the case for further scaling becomes stronger and easier to justify.
In clinical and operational environments, clarity around the role of AI is essential. Copilot should be positioned as a drafting and productivity assistant that enhances professional capability rather than replaces it.
Professional judgement, clinical expertise, and decision-making remain firmly with NHS staff. Copilot supports those processes by reducing administrative friction and accelerating preparation tasks.
Clear positioning reduces resistance and builds trust.
Driving Copilot adoption in the NHS requires more than technical readiness. It demands alignment with operational pressures, visible leadership sponsorship, strong governance, and structured behaviour change.
The organisations that succeed will not simply be those that deploy AI quickly. They will be those that integrate it thoughtfully into everyday workflows and demonstrate tangible improvements in staff experience and operational efficiency.
When Copilot is aligned with real needs, supported by leadership, and embedded within clear guardrails, adoption becomes sustainable rather than superficial.
The opportunity is significant. Realising it requires disciplined, people-focused execution.
We were brought in by a fast-growing financial data firm with a clear problem statement from the Director: growth was accelerating, but the operating model underneath it was creaking.
Teams were firefighting. Manual workarounds had become business-critical processes. Every increase in volume felt like it required more people. AI was on the agenda, but only in the abstract. There was no stable foundation to build on.
The temptation, as ever, was to jump straight to automation and AI.
That would have been a mistake.
Before a single bot or agent was built, we slowed things down.
We worked with the Director to step back and redesign how the function should actually operate:
who owns what, where decisions are made, how demand flows, and which problems are structural versus symptomatic.
Only once that was clear did we move into process redesign, automation, and selective AI enablement.
The work ended up spanning everything from onboarding and data quality to client query management, RPA, and an internal AI knowledge assistant built on existing tooling. Crucially, it was delivered jointly with internal teams, embedding new ways of working rather than creating dependency
The headline numbers were strong:
material efficiency identified, recurring OpEx avoided, and a clear path to scale without linear headcount growth.
But the more important outcome was subtler.
The executive leadership now had a repeatable model for improvement. Teams could identify, prioritise, and fix problems themselves. AI stopped being “the thing we’ll do later” and became something grounded in real operational needs, with governance already in place.
AI readiness is rarely about technology.
In the field, we consistently see that the organisations who get the most value from AI are the ones who first do the unglamorous work:
clarifying ownership, fixing broken processes, and building capability into the business.
AI then accelerates a system that already makes sense, rather than propping up one that doesn’t.
That difference is where most “AI transformations” quietly succeed or fail.
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Diagnostics are the foundation of effective treatment, yet they remain one of the most resource intensive and time critical areas of the NHS.
Backlogs, rising demand and staff shortages have made timely diagnosis a national priority.
To address this challenge, Hudson & Hayes partnered with NHS England Diagnostics in the Midlands to explore how AI and automation could reduce DNAs, improve patient information and eliminate unnecessary appointments. Together we developed a structured AI roadmap and delivered a live proof of concept that demonstrated how Agentic AI can support diagnostics at scale.
Now, with the rise of AI in the NHS, intelligent systems such as Agentic AI and AI Agents are reshaping how diagnostics are delivered, managed and scaled. The Midlands programme represents one of the most practical examples of how this can work in real clinical environments.
Diagnostic delays lead to treatment bottlenecks, unnecessary admissions and worse patient outcomes.
A 2025 University College London study found that while AI in diagnostics shows significant potential, most trusts struggle with governance, interoperability and scaling.
The Midlands programme confirmed this. Before Hudson & Hayes began, AI literacy and promising ideas were present, but efforts across trusts were fragmented, inconsistent and not yet prepared for investment. Our role was to bring structure, clarity and a unified regional strategy that supported the NHS Long Term Plan and ensured that opportunities were not missed or duplicated.
For the NHS, digital transformation in healthcare is no longer optional. AI provides a data driven path to reduce waiting times, accelerate triage and optimise clinical capacity, directly improving patient experience and reducing cost per diagnosis.
One of the core use cases developed by Hudson & Hayes as part of the Midlands roadmap was a patient facing diagnostic assistant. It supports patients through procedures such as colonoscopy and MRI, improves preparation and reduces DNAs.
Our proof of concept focused on an NHS Colonoscopy Assistant that uses Agentic AI to guide patients through every step of the journey.
The assistant includes:
Patients can access the assistant from QR codes on letters, web portals, SMS links, email links or the NHS App. Secure access uses one time passwords and is designed to move toward NHS App single sign on.
Trusts can configure appointment details, location, preparation instructions and AI support.
The assistant uses pre approved clinical content and adapts responses based on patient cohort, language and anxiety level. Red flag questions generate alerts for clinical review.
Videos and interactive content help reduce uncertainty and improve readiness.
Email and SMS reminders guide patients at key moments. Voice call support improves accessibility for those with limited digital literacy.
The benefits model that Hudson & Hayes created for the Midlands region showed that reducing DNAs by only 1 percent could release nearly 90,000 appointments per year across 11 ICBs. It also improves Friends and Family scores and reduces administrative time for staff.
This is automation in healthcare with purpose. It creates a confident, informed and reliable diagnostic journey.
Hudson & Hayes incorporated existing national imaging evidence into the Midlands roadmap to build practical AI imaging pathways.
Imaging is one of the most mature domains for AI in the NHS.
Examples include:
In the Midlands roadmap, these imaging capabilities were designed to connect directly to the patient facing assistant. Agentic AI can:
This closes the loop from scheduling to reporting and creates a more predictable diagnostic pathway.
Pathology, genomics and digital biomarkers form another key area highlighted in the AI roadmap created by Hudson & Hayes.
The National Pathology Imaging Co operative (NPIC) already uses AI to analyse cancer slides and identify early stage changes.
In future, AI Agents will allow the Midlands region to:
This moves pathology from passive analysis to active diagnostic orchestration.
Predictive diagnostics were a major component of Hudson & Hayes’ future state vision for the Midlands.
One example is the Imperial College AI ECG model that can identify ten year mortality risk with 78 percent accuracy.
Agentic AI can:
This shifts diagnostics from reactive to predictive care.
Hudson & Hayes ensured that all diagnostic AI recommendations aligned with national guidance from HFMA, NHS England and the UK Government’s AI safety policies.
The Midlands roadmap followed a structured delivery approach designed by Hudson & Hayes:
This matches national developments such as the UK Government’s AI screening platform launched in 2025.
Ethical design and AI governance are not obstacles. They are the foundations of safe, scalable innovation.
Because Hudson & Hayes designed the patient-facing assistant with NHS England Diagnostics, the solution directly supports personalised, safer and more accessible patient journeys.
Patients receive tailored updates, reminders, preparation guidance and answers to common questions.
Non clinical queries are resolved instantly, and clinical queries are escalated safely.
Across imaging, pathology and predictive diagnostics, this work reduces time spent searching for information and increases the time clinicians spend with patients.
The Midlands engagement confirmed that while AI presents major opportunities, benefits depend on governance, data quality and alignment across ICSs and trusts.
Hudson & Hayes identified the main barriers:
Without coordination, NHS organisations risk duplicating pilots and competing for the same resources. The regional roadmap ensured shared platforms and shared learning.
Diagnostics sit at the heart of AI transformation in the NHS.
Through our partnership with NHS England Diagnostics in the Midlands, Hudson & Hayes demonstrated how AI Agents and Agentic AI can reduce DNAs, release capacity and improve patient confidence through practical and scalable use cases.
The roadmap and proof of concept work show how these ideas can be turned into investment ready opportunities that support national priorities.
The real opportunity lies in creating fully connected diagnostic systems that learn, adapt and collaborate across the NHS ecosystem.
Achieving this will require strong AI governance, strategic planning and a benefits led approach that keeps patients at the centre.
With the right leadership, diagnostics will not only become faster and more accurate. They will also become smarter, fairer and more human.
In the face of growing demand, rising costs and constrained resources, the National Health Service (NHS) in England is under acute pressure to deliver more with less. Cost optimisation is no longer a choice but a necessity. Fortunately, the rise of artificial intelligence in healthcare offers new pathways for sustainable efficiency gains. The emergence of agentic AI and AI Agents, which act autonomously, initiate workflows and interact intelligently with humans and data, represents a major step forward for the NHS digital transform
The NHS faces significant financial and operational challenges. These include ageing populations, complex chronic diseases, workforce shortages, outdated IT infrastructure and heavy administrative burdens.
A briefing from the Royal College of Radiologists states that “AI, the greatest value to the NHS lies in automating or augmenting administrative and organisational tasks, thereby improving patient pathways” because clinicians currently spend too much time on non-clinical work.
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One pilot deployed by NHS England found that AI-driven appointment management software at one trust reduced did-not-attend (DNA) rates by around 30 percent and generated estimated savings of £27.5 million per year for that trust alone.
NHS England
A recent trial of a productivity tool, Microsoft 365 Copilot, across 90 NHS organisations found that staff saved around 43 minutes per day on routine tasks. This equates to around 400,000 hours every month and “millions of pounds every year” in potential cost savings.
GOV.UK
These results demonstrate the scale of opportunity. Cost optimisation enabled by technology is central to the NHS AI roadmap.
Artificial intelligence in healthcare delivers value in two broad ways: operational efficiency and clinical effectiveness.
The NHS highlights that “AI has the potential to give health and social care practitioners back time to care by removing time-consuming repetitive tasks” and that AI can “decrease costs”.
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In short, intelligent automation supports both cost reduction and enhanced clinical value.
While many NHS AI deployments currently focus on narrow tasks such as predicting DNAs or automating form completion, the next phase of transformation is centred on agentic AI and AI Agents. These systems can initiate actions, collaborate across multiple data sources, adapt in real time and manage workflows from start to finish.
In practice, this can include:
These capabilities make it possible to shift from reactive cost management to proactive cost optimisation and value creation across the NHS.
Strategy, governance and implementation
To harness AI effectively, several strategic elements are required:
Some worry that cost optimisation might reduce the quality of care. In reality, when AI is applied strategically, it improves both efficiency and patient experience.
Administrative workloads are reduced, allowing staff to focus more on clinical activities. Optimised pathways shorten waiting times. Predictive models improve flow, reduce DNAs and ensure that patients receive care at the right time.
Agentic AI that connects data across care pathways and triggers timely interventions strengthens both experience and outcomes. The ideal scenario, better care at lower cost, becomes achievable.
These examples demonstrate the scale at which AI and agentic AI can drive cost optimisation.
AI offers significant value, but there are challenges:
With strong governance, strategy and leadership, these issues can be mitigated.
Cost optimisation in the NHS is a strategic priority. AI, including agentic AI and AI Agents, is emerging as a critical enabler of that transformation. By automating repetitive tasks, optimising resources, improving patient pathways and freeing clinicians to focus on care, the NHS can achieve significant efficiency gains without compromising quality.
Success requires alignment with digital transformation goals, investment in data, strong governance, meaningful measurement and a culture of adoption. The journey from pilot to system-wide deployment will not be easy. However, the evidence is clear. Artificial intelligence in healthcare has the potential to deliver substantial cost savings and improved outcomes.
Now is the time for NHS leaders to move beyond experimentation and place AI, AI Agents and agentic AI at the centre of the next era of healthcare innovation and value-based care.
Key Stats at a Glance
NHS Diagnostics in the Midlands faced mounting pressures: increasing demand, workforce strain, and ambitious targets set by the NHS Long Term Plan. Reducing “Did Not Attends” (DNAs) and unnecessary appointments was central to improving both patient outcomes and operational efficiency.
While AI and automation were already being explored across individual Trusts, efforts were fragmented. Without a unified, strategic approach, the region risked duplicating work, missing ROI, and failing to deliver on national objectives.
Hudson & Hayes was brought in to design and deliver a cohesive strategy for AI adoption in diagnostics, aligned with both clinical needs and operational realities.
The key pain points included:
At stake was the ability to improve patient access, reduce anxiety and waiting times, and release clinical capacity to focus on those most in need
Hudson & Hayes applied its GenAscend methodology, moving from education through discovery to solution design and prototyping.
The engagement delivered measurable benefits:
By taking a structured, collaborative approach, Hudson & Hayes helped NHS Diagnostics transform fragmented experimentation into a unified AI-enabled programme. The elimination of 90,000 unnecessary appointments demonstrates both the scale of efficiency achievable and the direct positive impact on patient outcomes.
With AI literacy embedded and a validated roadmap in place, NHS Diagnostics is now positioned to scale adoption further, improving access, efficiency, and patient experiences across the region.
Organisations worldwide are investing heavily in Microsoft Copilot, hoping to unlock a step-change in productivity. But simply handing out licences doesn’t guarantee results. To turn Copilot into a genuine productivity engine, you need the right structure, culture, and guardrails in place.
In this post, we’ll walk through how to build a high-impact Copilot adoption strategy—one that delivers measurable returns, mitigates risks, and sets the stage for the future of AI in the workplace.
Based on our work with organisations across multiple sectors, here are the building blocks that make the biggest difference:
Identify and train early adopters who are curious, influential, and enthusiastic. They should not only understand Copilot’s features but also know how to tailor them to their role. These champions become internal role models and trusted advisors for colleagues.
Generic training doesn’t work. Focus on embedding Copilot into specific processes. For example:
Copilot should feel like an accelerator for everyday tasks, not an extra layer of work.
A shared prompt library—built and refined by staff—lowers the barrier to effective use. Keep it dynamic, role-based, and continuously updated with best practices and new discoveries.
Adoption is cultural as much as technical. Leaders must actively use Copilot, share outputs, and ask their teams “Have you tried AI for this?”. Even small gestures—such as showing how a meeting transcript can be turned into action items—help normalise usage.
Treat Copilot agents like product releases:
Define simple KPIs such as:
Regularly review performance, retire low-use prompts or agents, and scale the high-value ones.
Once the foundations are in place, expand Copilot’s reach by integrating with:
This aligns with Microsoft’s own guidance: value compounds when Copilot is tied to business-critical workflows rather than used in isolation (microsoft.com).
Organisations that act now will not only capture immediate productivity savings but also build the cultural and technical maturity needed to harness the next wave of workplace AI.
Microsoft Copilot isn’t just another software upgrade but a catalyst for reshaping how work gets done. The evidence is clear: employees can save significant time every week, and organisations can capture enormous value at scale.
But success doesn’t happen by accident. It comes from intentional adoption strategies: role-based training, living prompt libraries, leadership modelling, strong governance, and clear measurement. Skip these steps, and the risk is wasted investment.
Done right, Copilot doesn’t just save hours. It changes the way people work, collaborate, and innovate, positioning organisations for long-term success in the AI era.
How AI and Automation Can Help Optimise Costs in Higher Education
Higher education institutions in the UK are facing unprecedented challenges. With Brexit making it more difficult to attract international students—who often contribute significantly to university revenues—the sector is feeling the strain. Adding to this pressure, the government has made it clear that it won’t be offering financial bailouts to universities, forcing institutions to take decisive action to manage their costs. As a result, cuts must be made, and universities are under increasing pressure to streamline their operations without compromising on the quality of education.
The Slow Road of Traditional Cost-Cutting Measures
In response to financial strain, many universities are resorting to organisational redesigns, such as restructuring departments and cutting underperforming courses. While these measures can lead to cost reductions, they often take a long time to materialise. Courses may need to be wound down over several years, leading to a delayed realisation of benefits and return on investment (ROI).
Given the urgency of the current situation, relying solely on traditional approaches may not be sufficient to safeguard the financial sustainability of universities.
The Fast-Track Solution: AI and Automation
This is where AI and automation come into play. By integrating these technologies into university operations, institutions can accelerate cost-saving measures while unlocking long-term value. AI and automation provide immediate relief by reducing the administrative burden, freeing up staff capacity to focus on higher-impact work, and improving operational efficiency.
The key benefits of implementing AI and automation include:
Critical Success Factors for AI Implementation
While AI and automation present exciting opportunities, their success hinges on certain critical factors:
Conclusion: The Way Forward
With Brexit-related challenges and limited government support, it is a critical time for UK universities to rethink their cost structures. While organisational redesigns and course reductions are traditional approaches, they may take too long to deliver the necessary impact. AI and automation offer a faster, more sustainable solution, providing immediate cost relief and enabling universities to focus on what they do best—delivering high-quality education.
By embracing AI and automation, universities can streamline their operations, enhance student experiences, and unlock long-term savings, all while positioning themselves for a successful future in an increasingly competitive landscape.
Bradford University faced a significant increase in demand for a critical academic process, revealing the need for enhanced efficiency and productivity. This case study outlines how Hudson & Hayes partnered with the university to implement Lean principles and Intelligent Automation, ultimately improving the student and teaching experience.
Bradford University experienced a substantial rise in demand for one of its key processes, necessitating a more efficient approach. There was a strong desire to boost awareness of Lean principles and Intelligent Automation across the organisation. Hudson & Hayes was engaged to work closely with the university to redesign the process, applying Lean techniques and exploring automation solutions. The primary objectives were to increase productivity, ensure consistency, and enhance both student and teaching experiences.
Hudson & Hayes utilised the Elevate Process and Customer Journey methodology to take a structured approach to process improvement. Our team collaborated with the university's internal staff to:
These efforts resulted in an identified 40% increase in productivity. Additionally, we developed a proof of concept (POC) for a workflow, demonstrating how the process could be digitised from end to end, moving away from the existing manual system. We also provided multiple educational sessions to build internal capabilities, focusing on Intelligent Automation in Higher Education and Lean principles.
The initiative delivered a future-state process that would reduce lead times and improved productivity by up to 40%. We presented a first iteration and a strategic plan to ensure ongoing process evolution, introducing the right solutions at the appropriate times.
"The University of Bradford recently had the pleasure of working with Hudson & Hayes to review our processes relating to how we manage academic misconduct allegations and investigations. From the outset, H&H colleagues worked in a proactive and collaborative way, helping us not only make our chosen process more efficient, streamlined, and fit for the future but also educating us about some of the fundamentals of lean process design and automation, which will be beneficial for multiple areas of the University. This opportunity for development was extended beyond the immediate project team to colleagues across our organisation, demonstrating Hudson and Hayes’ commitment to supporting partners holistically and not gatekeeping expertise within a single project that could benefit a wider audience. Throughout the project, the team at H&H took time to fully understand our context through engagement with a range of stakeholders and proposed solutions which were bespoke and realistic to this context, taking into consideration both our specific needs as well as any project parameters. On a personal level, the H&H team has been really enjoyable to work with. They have been very approachable and supportive, as well as accommodating and patient as together we have navigated the murky world of higher education administrative processes! I highly recommend them to other Universities or other organisations wishing to improve their ways of working through process redesign and automation."
Hudson & Hayes successfully supported Bradford University in enhancing process efficiency through Lean principles and Intelligent Automation. This collaboration not only improved productivity and reduced lead times but also equipped the university with essential knowledge for ongoing process enhancement.
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