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Pharmacy First has been described as a pivotal shift for community pharmacy. It signals trust in pharmacists as frontline clinicians, expands access for patients, and aims to relieve pressure on general practice. On the surface, it appears to be an elegant solution: care delivered closer to home, funded at a fixed rate, embedded within existing pharmacy settings.

Yet the true cost of Pharmacy First is more complex than reimbursement figures suggest. The visible financial payment per consultation tells only part of the story. Beneath it sits a deeper operational reality shaped by time, cognitive demand, governance responsibility, and system infrastructure. If we are serious about sustainability, these dimensions must be examined with the same attention as activity numbers.

The first cost is time, and it is consistently underestimated. A Pharmacy First consultation may be labelled as brief, but in practice it rarely exists in isolation. It is delivered within a live dispensing environment, interrupted by workflow pressures, queries from staff, and competing patient demands. The consultation itself requires structured history taking, red flag identification, clinical reasoning, documentation, safety-netting advice, and submission of claims. What might appear to be a ten-minute interaction often becomes twenty minutes of end-to-end clinical engagement. When multiplied across daily volumes, this incremental extension becomes significant. It does not simply absorb minutes; it absorbs capacity. Capacity that was previously allocated to dispensing, supervision, prescribing support, or managerial oversight is quietly redistributed.

Beyond time lies a more subtle cost: cognitive load. Pharmacy First elevates the clinical responsibility of pharmacists. With that elevation comes a shift in professional risk. Each consultation requires judgement under time pressure. Each red flag must be recognised. Each documentation entry must withstand audit scrutiny. The mental bandwidth required to maintain vigilance across repeated consultations in a high-throughput environment should not be underestimated. Cognitive fatigue is not visible on a balance sheet, yet it directly influences safety, consistency, and resilience. When a service expands rapidly without corresponding structural support, variability can increase. And variability, in clinical settings, is rarely neutral.

Risk is therefore not an abstract concern but a measurable dimension of cost. As community pharmacies assume greater frontline responsibility, their exposure to complaints, audit queries, and regulatory scrutiny naturally increases. Documentation standards must remain robust. Referral decisions must be defensible. Clinical reasoning must be reproducible. In a model that depends heavily on individual practitioner judgement, the absence of structured infrastructure can amplify variability between sites. Over time, such variability can undermine confidence in the service, regardless of initial success metrics.

At the same time, it would be short-sighted to frame Pharmacy First solely in terms of burden. Its value proposition is substantial. It offers earlier access to treatment, reduces unnecessary GP appointments, and reinforces the role of pharmacy within integrated care systems. For many patients, it delivers timely reassurance and intervention that might otherwise have been delayed. These benefits carry system-level economic implications that extend far beyond the pharmacy counter.

However, true value cannot be assessed purely through activity volumes or reimbursement totals. It must account for sustainability. If consultation time consistently erodes workforce capacity, if cognitive load increases burnout risk, or if governance pressures escalate without structured mitigation, then short-term gains may compromise long-term stability. Measuring value without measuring cost creates an incomplete narrative.

This is where infrastructure becomes decisive. Structured consultation pathways, intelligent documentation support, and embedded clinical guidance can influence time, risk, and value simultaneously. By standardising elements of assessment while preserving professional judgement, infrastructure reduces unnecessary variability. By streamlining documentation, it reclaims time. By creating audit-ready records, it strengthens governance. In doing so, it transforms Pharmacy First from an additional responsibility layered onto existing workload into a systematised service capable of scale.

The conversation about Pharmacy First must therefore mature. Success cannot be judged solely by the number of consultations delivered. It must also be judged by how intelligently those consultations are supported. Time should be measured end-to-end rather than assumed. Risk should be quantified rather than overlooked. Value should be assessed in terms of resilience as well as revenue.

Pharmacy First represents a structural shift in the role of community pharmacy. Structural shifts demand structural support. If we measure only what is visible, we risk misunderstanding what is sustainable. If we measure time, risk, and value together, we create the foundation for a model that strengthens pharmacy rather than stretching it.

The future of Pharmacy First will not depend simply on expansion. It will depend on whether the systems surrounding it evolve with equal ambition.

When I describe PharmBot AI as an infrastructure company for pharmacy, I am not referring to another chatbot, another clinical application or a single piece of automation. I am describing a connected and governed foundation through which patients, pharmacists, pharmacy services and existing healthcare systems could work together more effectively. Pharmacy technology has traditionally been developed around individual transactions: one system processes prescriptions, another manages appointments, another records consultations, and separate platforms support ordering, adherence or patient communication. Although each system may solve a particular problem, the wider experience often remains fragmented. The patient moves between services without continuity, while the pharmacist must repeatedly gather information, navigate different systems and reconstruct the clinical context. The future of pharmacy will require more than digitising each of these activities separately; it will require an intelligent layer that connects them around the patient and the pharmacist.

PharmBot AI did not begin with this infrastructure vision fully formed. The original focus was prescription verification, using artificial intelligence to identify potential errors, inconsistencies and risks for a pharmacist to review. That work raised a broader question about whether AI could support the pharmacist before a prescription reached the final verification stage. This led to AIVAe, the virtual pharmacist concept, designed to interact with patients, gather relevant information and help them access appropriate pharmacy services. AIVAe then evolved into a more structured clinical tool capable of supporting Pharmacy First consultations by collecting symptoms, identifying potential red flags and organising information for pharmacist review. Each stage expanded the potential role of the technology, but it also revealed the same underlying limitation: prescription verification, consultations, triage, service booking, repeat ordering, adherence and follow-up cannot deliver their full value when they operate as disconnected products.

That realisation changed the scale of the problem PharmBot AI needed to address. Instead of continuing to build separate tools for individual pharmacy activities, the greater opportunity became the development of a modular infrastructure layer capable of supporting multiple services through a common foundation. This would allow relevant information gathered during one interaction to support the next stage of the patient journey, subject to appropriate consent, clinical governance and data protection. A structured consultation could inform a pharmacist’s assessment; prescription verification could draw attention to issues requiring professional review; and the same clinical context could support follow-up or adherence monitoring after the patient leaves the pharmacy. The infrastructure would not make clinical decisions independently or remove the pharmacist from the process. Its purpose would be to organise information, connect workflows and provide intelligent support while ensuring that the pharmacist remains the clinical authority.

The long-term vision is a pharmacy environment in which patients experience continuity rather than a series of isolated transactions. A patient might begin by describing a health concern through AIVAe, which could collect the relevant information, recognise potential warning signs and direct the patient towards the appropriate service. If a consultation is required, the pharmacist would receive structured information rather than beginning with an empty screen. If a medicine is supplied or a prescription is presented, an additional module could support the pharmacist by identifying risks or inconsistencies requiring attention. Where follow-up is clinically appropriate, the same infrastructure could help monitor progress, support adherence or reconnect the patient with the pharmacy. Each component would have a specific function, but their value would come from operating as part of one governed system with shared context and clear professional oversight.

Qatar Science & Technology Park is where I intend to begin turning this vision into an infrastructure model. The objective is not to take a finished product developed for the United Kingdom and assume that it can simply be transferred into Qatar. Healthcare systems have different workflows, priorities, regulatory requirements and relationships between patients and providers. The first stage must therefore involve understanding how pharmacy currently operates in Qatar, mapping the patient and pharmacist journey, identifying where information becomes fragmented and determining which problems would benefit most from a connected approach. Engagement with pharmacists, healthcare organisations, technology providers and other stakeholders will be essential in deciding where the first modules should be developed and validated. The technology must follow the workflow and the evidence, rather than forcing local practice to adapt around a predetermined product.

Beginning this work in Qatar also creates an opportunity to design the infrastructure from a systems perspective at an early stage. Rather than adding another application to an already fragmented environment, the aim is to understand how an intelligent layer could connect with existing clinical and pharmacy systems while remaining modular enough to evolve. The initial work may focus on a limited number of carefully selected use cases, but those modules should be designed as parts of a larger architecture from the outset. Prescription verification, AIVAe and structured Pharmacy First consultations represent the foundations of that architecture: verification provides the safety-support component, AIVAe provides the patient-facing interaction, and Pharmacy First provides the structured clinical workflow. The next phase is to connect these capabilities so that they can support a continuous pharmacy journey rather than functioning as separate demonstrations of AI.

Governance must be treated as part of this foundation rather than something applied after development. In a regulated clinical environment, every AI-supported function must have a defined purpose, clear limits and an accountable human decision-maker. Pharmacists should be able to understand what information has been collected, why an issue has been highlighted and where an AI-supported output has influenced the workflow. Activity should be appropriately recorded and auditable, with data protection, consent, access controls and clinical safety considered throughout the design. This governance-first approach is not a restriction on innovation; it is what will allow the infrastructure to move beyond experimentation and become credible within real healthcare practice.

Although Qatar will be the initial focus, the underlying challenge is international. Pharmacy systems differ between countries, but many face the same pressures: fragmented technology, expanding clinical responsibilities, growing demand, limited professional time and a lack of continuity between services. The infrastructure must therefore be capable of adapting to different regulations, formularies, languages and clinical pathways without requiring the entire platform to be rebuilt for every market. The ambition is to develop an architecture that can be configured locally while retaining a consistent foundation of governance, interoperability and human oversight.

Prescription verification was the entry point, AIVAe expanded the interaction, and Pharmacy First introduced the structured clinical workflow. Qatar now represents the opportunity to bring those elements together and begin building the layer beneath pharmacy. This is not a change in direction, but the result of understanding the problem more deeply. Pharmacy does not need more isolated technology competing for the pharmacist’s attention. It needs an intelligent and governed infrastructure that helps existing services work together, preserves clinical accountability and allows pharmacists to spend more of their time applying the judgement that technology cannot replace.

Earlier this year, I submitted written evidence to the House of Lords inquiry into Innovation in the NHS: Personalised Medicine and Artificial Intelligence. My submission, which can be read here, focused on an area that I believe is often overlooked in national conversations around healthcare innovation: the role of community pharmacy in delivering more personalised, technology-enabled care at scale.

The inquiry itself asks an important question: how can the NHS better support innovation in personalised medicine and artificial intelligence? It is a timely discussion. Across healthcare, there is growing excitement about the role of AI in improving outcomes, reducing pressure on overstretched services, and supporting more precise approaches to care. Yet despite rapid advances in technology, adoption across the NHS remains inconsistent and, in many areas, frustratingly slow.

Part of the challenge, in my view, lies in how we define personalised medicine. Public discussions frequently focus on genomics, precision oncology, advanced diagnostics, and specialist interventions delivered in hospital settings. These developments are important and undoubtedly represent part of the future of medicine. However, personalised medicine can also mean something much more immediate and practical: using individual patient information to make safer, more tailored clinical decisions during everyday healthcare interactions.

In many respects, community pharmacy has already been delivering a form of personalised care for years. Pharmacists routinely assess symptoms, medication histories, co-morbidities, contraindications, allergies, and patient preferences before making treatment recommendations. Every consultation involves balancing clinical guidance against the specific circumstances of the person in front of us. What changes from patient to patient is not necessarily the evidence base, but how that evidence is applied.

Artificial intelligence presents an opportunity to strengthen this process, not by replacing healthcare professionals, but by supporting them. When used appropriately, AI can help structure consultations, surface relevant guidance, identify patient-specific risks, highlight red flags, and reduce the administrative burden associated with documentation. This becomes particularly important in environments where clinicians are managing increasing workloads while attempting to maintain safe and consistent standards of care.

However, conversations around healthcare AI often become polarised. On one side, there is enthusiasm that AI will transform healthcare almost overnight. On the other, understandable concerns around safety, bias, accountability, and professional trust. The reality is likely to sit somewhere between these extremes. AI in healthcare should not be viewed as autonomous clinical decision-making. Instead, its greatest value may lie in functioning as clinical decision support — strengthening professional judgement rather than replacing it.

This distinction matters because healthcare operates differently from most other sectors. Success is not determined simply by whether technology is technically capable. It is determined by whether it can function safely, transparently, and consistently within the realities of clinical care. Healthcare professionals need systems they can trust, understand, and challenge when necessary. Patients need reassurance that technology is supporting care, not undermining accountability.

Community pharmacy offers an especially valuable lens through which to view this challenge. It remains one of the most accessible parts of the NHS and increasingly delivers clinical services that go far beyond traditional dispensing. Programmes such as Pharmacy First demonstrate how pharmacists are already managing structured clinical pathways, assessing patients, making treatment decisions, and supporting access to care closer to home.

Drawing on practical experience from structured Pharmacy First consultations, one lesson becomes increasingly clear: technology succeeds when it supports workflow rather than disrupts it. Pharmacists do not necessarily need more complexity. They need tools that help structure decision-making, reduce unnecessary administrative burden, improve consistency, and allow more time to focus on patient care. Importantly, the clinician must remain accountable, with technology acting as a support layer rather than a substitute for professional expertise.

Yet despite the growing potential of AI-enabled care, innovation within community pharmacy often receives less attention than hospital-based technologies or specialist interventions. This may represent a missed opportunity. If the NHS is serious about expanding personalised medicine, then community pharmacy should form part of that conversation. Few parts of the health system are as accessible, scalable, and embedded within local communities.

At the same time, one of the clearest lessons from developing and piloting healthcare technologies is that innovation does not fail solely because the science is lacking. More often, it struggles because of the realities of implementation. In my submission to the House of Lords inquiry, I highlighted several recurring barriers that continue to affect innovators attempting to deploy solutions into routine care. These include fragmented procurement pathways, regulatory uncertainty, interoperability challenges, and commissioning models that do not always reward efficiency, prevention, or improved workflow.

These barriers are not abstract concerns. They directly shape whether promising technologies remain isolated pilots or become embedded into day-to-day clinical practice. While healthcare is rich with innovation, there often remains a significant gap between demonstration and deployment. Bridging this gap will require more than technological capability alone. It will require healthcare systems that are prepared to adopt innovation safely, consistently, and in ways that fit frontline practice.

The House of Lords inquiry arrives at an important moment for healthcare. Artificial intelligence is rapidly moving from concept to practical application, while expectations around personalised care continue to grow. The question is no longer simply whether AI can contribute to better healthcare. Increasingly, evidence suggests that it can. The more important challenge is ensuring that technology strengthens clinical practice, maintains patient trust, and supports professionals working under growing pressure.

If personalised medicine is to fulfil its promise, the conversation cannot be limited to specialist centres, advanced diagnostics, or future scientific breakthroughs. It must also include the realities of frontline care, where millions of clinical decisions are made every day. Community pharmacy, supported by carefully governed and clinically integrated technology, has the potential to become an important part of that future.

Further Reading
My written evidence submitted to the House of Lords inquiry into Innovation in the NHS: Personalised Medicine and Artificial Intelligence can be accessed here.

AI in healthcare looks impressive.  In a controlled environment, everything works exactly as expected. Questions follow a logical sequence, answers are clear, and documentation is generated instantly. It creates the impression that the problem has already been solved.

On the surface, it makes complete sense.  But real pharmacy practice doesn’t look like a controlled environment.

It is unpredictable, fast-moving, and constantly interrupted. A consultation does not happen in isolation. It happens alongside everything else, prescriptions to check, patients waiting at the counter, phones ringing, and staff asking questions. In the middle of that, a clinical decision still has to be made safely and efficiently.

This is where most AI systems begin to struggle.  Not because they lack intelligence, but because they are not designed for the environment they are entering.

In community pharmacy, the nature of consultations is changing.  What was once informal advice is becoming structured, accountable, and increasingly visible. Services such as Pharmacy First are transforming consultations into auditable clinical events. Pharmacists are expected to follow defined pathways, document decisions clearly, and operate within Patient Group Directions.

This shift raises the standard of care, but it also raises the operational pressure.

Consultations now require:

• structured questioning 
• clear documentation 
• defensible decision-making 

All within a time-pressured, retail-facing environment. At the same time, many AI tools entering healthcare are built in isolation.

They are designed to:

• generate responses 
• suggest outcomes 
• produce summaries 

And technically, they do this well. But they are not designed around workflow. In practice, even small inefficiencies become significant.

If a system:

• interrupts the natural flow of a consultation 
• forces the pharmacist into a rigid sequence 
• requires information to be entered more than once 

It creates friction. And in a busy pharmacy, friction does not get tolerated. It gets bypassed. There is also a deeper issue , responsibility. No matter how advanced the technology is, the pharmacist remains accountable for the final clinical decision. That responsibility cannot be delegated.

So if an AI system:

• lacks transparency 
• does not clearly explain its reasoning 
• behaves like a “black box” 

It does not reduce cognitive load. It increases it. Because every recommendation has to be checked, questioned, and verified.  At that point, the system is no longer supporting the consultation. It is adding another layer of work.

This is the gap that is often missed. The problem is not that AI is not capable enough. The problem is that it is not embedded into real-world practice. In reality, success is not defined by how impressive a system looks in a demonstration. It is defined by whether it is used consistently in day-to-day practice.

That means:

• it must fit into existing workflow 
• it must support, not disrupt 
• it must reduce effort, not increase it 
• it must be transparent and clinically defensible 

This is where the conversation needs to shift.

Away from:

• models 
• features 
• capabilities 

And towards:

• workflow 
• governance 
• integration 

AI in pharmacy will not succeed because it is technically advanced. It will succeed if it becomes part of the system — something that works quietly in the background, supports decision-making, and integrates into how consultations already happen. Not something that sits alongside it.

Because in real practice, the test is simple.

• Does it help when things are busy?
• Does it work under pressure?
• Does it make the consultation easier, not harder?

That is what determines whether a system is adopted. And ultimately, whether it lasts.

Over the past two years, I have spent a significant amount of time thinking about a question that continues to shape almost every discussion around artificial intelligence in healthcare: why does so much of the technology appear so promising, yet struggle to translate meaningfully into everyday clinical practice?

This question did not emerge from academic theory or observation from a distance. It came from practical experience. As a pharmacist and founder building within healthcare, I have had the opportunity to observe first-hand the growing excitement surrounding artificial intelligence, while also witnessing the realities of how healthcare actually functions in practice. What became increasingly clear to me was that there appeared to be a widening gap between how AI was being discussed and how healthcare professionals actually work.

Healthcare is often spoken about as though it were a straightforward environment for technological implementation. In reality, it is anything but. Clinical practice exists within a world of interruptions, uncertainty, competing priorities, regulation, governance, and professional accountability. Decisions are rarely linear, consultations rarely unfold exactly as planned, and no patient presents as a textbook example. A healthcare professional must constantly balance evidence, judgement, risk, patient preference, and practical constraints, often under considerable pressure.

Yet many of the conversations surrounding healthcare AI seemed to focus almost exclusively on capability. How advanced is the model? How quickly can it process information? Can it outperform clinicians in specific tasks? While these questions are important, I increasingly felt they were not addressing the issue that mattered most. In healthcare, the success of artificial intelligence will not be determined solely by how intelligent a system appears in isolation, but by whether it can function safely, responsibly, and meaningfully within the realities of clinical workflows.

This distinction became increasingly important to me as I continued developing solutions through PharmBot AI and AIVAe. The more time I spent working within pharmacy workflows and structured consultations, the more convinced I became that many failures in healthcare AI are not necessarily failures of technology. Rather, they are failures of implementation and context. Too often, solutions are designed around idealised environments rather than the realities of practice, overlooking the complexity of clinical responsibility and underestimating the importance of governance, accountability, and workflow integration.

This growing conviction ultimately became the reason for writing AI Isn’t Failing in Healthcare — It’s Built in the Wrong Place.

The purpose of this book was not to position myself as having definitive answers, nor to dismiss the extraordinary progress that has been made within artificial intelligence. On the contrary, I remain deeply optimistic about the role AI can play in transforming healthcare over the coming decade. However, I have increasingly come to believe that meaningful transformation will not occur simply because models become more sophisticated. Instead, it will depend on whether we are able to design systems that complement the realities of clinical practice, support rather than disrupt professional workflows, and operate within frameworks that healthcare professionals can trust.

At its core, the book argues for a relatively simple but, I believe, increasingly important idea: healthcare AI succeeds not when it attempts to replace professional judgement, but when it is designed around the environments in which healthcare professionals already operate. Technology in healthcare cannot exist independently of workflow, governance, safety, or accountability. If these factors are treated as secondary considerations, even the most technically advanced systems are unlikely to achieve meaningful adoption or sustained impact.

Writing this book was also, in many ways, a reflection of the personal journey of building within healthcare innovation. Like many founders working in difficult sectors, the process has included setbacks, uncertainty, rejection, and periods where progress has felt slower than expected. Building something meaningful in healthcare rarely follows a straight line, particularly when attempting to navigate clinical complexity, regulatory considerations, and limited resources simultaneously. Nevertheless, throughout this process, my belief in the importance of the problem itself has remained unchanged.

Healthcare deserves technologies that genuinely understand the environments in which they are expected to operate. If artificial intelligence is to fulfil its potential, it must move beyond impressive demonstrations and begin solving problems in ways that align with the realities of frontline care.

This book is my attempt to contribute to that conversation. Not as a conclusion, but as a starting point for asking better questions about how we build, implement, and ultimately trust artificial intelligence within healthcare.

If it encourages reflection, constructive debate, or even a different way of thinking about healthcare AI, then I believe it will have served its purpose.