Professional and financial services firms innovate constantly, consequentially, and almost entirely outside the frameworks that public policy uses to identify, measure, and support innovation. That mismatch has costs: for the sector, for the regions where it operates, and for the industrial strategy designed to grow it.
Hilary Smyth-Allen · CEO, SuperTech WM
If you ask a partner at a West Midlands law firm whether their firm innovates, the honest answer is almost certainly yes, followed, in many cases, by a pause, and then a qualification. Because innovation, in the policy sense, implies something the firm does not quite recognise in itself: a dedicated R&D function, a research pipeline, a relationship with a university, a patent, a grant application. None of those things are part of how most professional and financial services firms think about what they do, even when what they are doing is genuinely and significantly innovative.
This gap between how innovation actually happens in professional and financial services and how public policy is designed to recognise and support it is not a minor administrative inconvenience. It is a structural problem with real consequences for how the sector is understood, how it is resourced, and how places like the West Midlands, where the sector is large, growing, and increasingly technology-enabled, are positioned in national industrial strategy conversations. Understanding the gap requires being clear about what innovation in this sector actually looks like, and why it is so consistently invisible to the frameworks built around a different model.
The innovation model that policy supports
The dominant model of innovation in public policy draws heavily on the experience of manufacturing, life sciences, and deep technology. In that model, innovation is a discrete activity that can be separated from the delivery of products and services: a firm identifies a research question, invests in answering it, produces a protectable or scalable output, and either commercialises it directly or transfers the knowledge to partners who do. The support infrastructure built around this model, with R&D tax credits, Knowledge Transfer Partnerships, Innovate UK funding, and university research commercialisation units, is designed with that sequence in mind. It works well for sectors where the sequence is real.
The innovation model in professional and financial services
Professional and financial services firms innovate, but they do so in ways that are relational, incremental, client-reactive, and deeply embedded in the delivery of the service itself. The innovation is rarely separable from the work. It happens in the room, at the point of the transaction, in the redesign of a process that has become inefficient, in the development of a new analytical framework for a problem a client has not encountered before. It accumulates over time rather than arriving in discrete, fundable moments. And it is driven far more by competitive pressure, client demand, and the emergence of new technology than by any deliberate research agenda.
This is not a lesser form of innovation. The legal firm that has built its own closed AI system because waiting for the procurement process to identify and validate an external product would have taken longer than building something fit for purpose – this is innovating. The accountancy practice that has redesigned its entire client relationship model around real-time data access, moving from backward-looking compliance work to forward-looking commercial advisory, is innovating. The recruitment business that is undergoing a fundamental transformation from a people-led matching service to a technology platform that happens to employ recruiters is innovating. The financial services firm that has deployed AI to enable agents to have more genuinely human conversations with vulnerable customers by removing the cognitive burden of navigating complex affordability processes in real time, is innovating. None of these would show up in R&D expenditure data. None of them would straightforwardly qualify for R&D tax credits under the current tightened definition. Most of them would not map onto the scope of a Knowledge Transfer Partnership as it is conventionally understood.
The innovation is real. The problem is that the frameworks designed to identify and support it are looking in the wrong place, for the wrong things, at the wrong pace.
Pace is worth dwelling on. One of the most consistent observations across our engagement with West Midlands professional and financial services firms, including through a senior leaders’ roundtable convened in Birmingham in June 2026 as part of our work to update the Next Generation Services evidence base in partnership with City-REDI at the University of Birmingham and the West Midlands Combined Authority, is that the speed at which the technology landscape is moving has fundamentally changed the relationship between problem and solution. Firms described completing procurement processes for AI tools only to find that the landscape had moved on before the contract was signed. One firm built its own system from scratch, in less time than the evaluation of external options would have taken, because internal capability and AI-assisted development had made that the faster and more reliable route. Another opened up its entire workforce to an AI platform with minimal central direction, on the basis that the people closest to the friction are better placed than any central team to identify where innovation will add value, and was vindicated when a former executive assistant developed a consumer-facing tool that earned recognition at the highest level of the platform provider.
These are not stories about firms with dedicated innovation functions or structured R&D pipelines. They are stories about organisations responding intelligently to a rapidly changing environment, deploying new capability in ways that are directly embedded in how they serve their clients, and finding that the most effective innovations often emerge from the edges of the organisation rather than from its centre. That is how innovation works in this sector. It is not inferior to the laboratory model; it is different from it, and the difference is not incidental but structural.
The university engagement gap
The mismatch between how innovation happens in professional and financial services and how policy is designed to support it is perhaps most visible in the relationship, or more accurately, the non-relationship, between professional services firms and universities. In manufacturing, close working relationships between firms and university research departments are standard practice, driven by the need to validate technical performance, access specialist expertise, and develop the next generation of products and processes. Those relationships are the foundation on which Knowledge Transfer Partnerships, collaborative research grants, and university spinout activities are built.
In professional and financial services, equivalent relationships are rare, and the reasons are instructive. It is not that the expertise available in universities is irrelevant to the challenges firms are navigating. It is that the interface between academic research timelines and business problem-solving timelines has become almost unbridgeable as the pace of change accelerates. A firm grappling with how to deploy agentic AI across its client onboarding process, or how to redesign its graduate development pathway in a world where AI can perform the foundational technical work that trainees historically did, does not have the months required to scope a research brief, identify the right academic partner, negotiate a collaboration agreement, and wait for the research cycle to run its course. By the time that process completes, the firm has either solved the problem through other means or the problem has changed shape.
The question ‘why ask a professor when I can ask AI?’ is not a dismissal of academic expertise. It is an accurate description of the speed differential that now exists between the two.
This matters for the universities as much as for the firms. The traditional research commercialisation model, where academic expertise flows outward to industry through structured partnership mechanisms. was designed for a world in which the knowledge advantage lay predominantly with the university. In a world where the most pressing questions facing professional services firms concern the deployment of technologies that are themselves evolving faster than any research institution can track, that advantage is less obvious, and the structured mechanisms feel correspondingly less relevant.
None of this means that universities have nothing to offer professional and financial services firms. What they have to offer needs to be reframed: less as research findings to be transferred, more as analytical rigour, longer-term thinking, and the capacity to ask questions that firms under competitive pressure cannot easily make time for. The firms that have found value in university relationships in this sector have generally done so through informal, relationship-based connections rather than formal mechanisms, which suggests that a trusted intermediary capable of making rapid connections between the right firm and the right academic expertise could unlock value that the formal system is currently leaving unrealised.
The R&D definition problem
The R&D tax credit regime illustrates the mismatch precisely. Relief is available for AI-related work, but the bar is high and deliberately so: to qualify, a project must seek a genuine advance in science or technology and resolve technological uncertainties that a competent professional could not readily solve. Custom algorithm development, training proprietary machine learning models, or engineering novel integrations between complex legacy systems and new AI infrastructure can qualify. Purchasing a third-party AI tool, fine-tuning an existing model using standard methods, or deploying available technology, however intelligently and however transformatively, does not. That distinction matters because the overwhelming majority of what professional and financial services firms are doing with AI falls into the second category rather than the first. They are doing something that constitutes genuine business innovation: rethinking processes, redesigning service architectures, and deploying available capability in ways that create real value for clients. But they are doing it below the development threshold that the relief framework requires. The result is a policy instrument that is not quite the right shape for the innovation that is actually happening, and a sector that continues not to appear in the data as an innovative one, even as it transforms.
What the next wave of innovation looks like
Understanding why the current framework is inadequate requires being clear about what it is failing to capture, but it also requires a view of where innovation in professional and financial services is heading, because the next wave is likely to make the mismatch more acute, not less.
The firms and leaders we work with are increasingly focused not on AI as a tool for automating individual tasks, but on agentic AI as a framework for redesigning entire processes. The distinction matters. Task automation: using AI to draft a contract clause, screen a CV, summarise a case file, or generate a financial model, is already widespread and is generating real productivity gains. But the more transformative opportunity lies in deploying AI systems that can autonomously execute sequences of tasks across complex, multi-step processes: combining client onboarding, compliance checking, risk assessment, and relationship management into a continuous, AI-orchestrated workflow in which the human role shifts from doing the work to exercising judgement at the points where judgement is genuinely required.
That is a fundamentally different kind of innovation from anything the current support infrastructure is designed to facilitate. It requires deep domain knowledge combined with sophisticated technical capability. It requires firms to think about their entire service architecture rather than individual functions within it. It requires the kind of cross-disciplinary expertise, to include lawyers who understand technology, technologists who understand regulation, and data specialists who understand commercial context, that does not emerge naturally from either a professional services training pathway or a computer science degree. And it creates a genuine opportunity for regions like the West Midlands, with a deep professional services base and a growing technology ecosystem, to develop distinctive capability in deploying these systems in regulated, client-facing, high-stakes environments — if the support infrastructure can be designed to make that possible.
Agentic AI is not a technology story. It is a professional services story, and the West Midlands has the depth of sector expertise to be at the leading edge of it - if the conditions are right.
What better support infrastructure looks like
Designing support infrastructure that works for this sector requires starting from how innovation actually happens within it, rather than from the manufacturing and life sciences model and asking how the sector can be made to fit. That means a facilitation function operating at the speed of business rather than institutional procurement. It means R&D incentive frameworks that can recognise genuine innovation embedded in service redesign and proprietary AI development, even where those activities produce neither a patent nor a publishable output. It means university engagement models that value the practitioner’s question as much as the researcher’s answer. And it means a measurement framework that goes beyond R&D expenditure and patent counts to capture the relational, incremental, client-embedded forms of innovation that are actually driving value creation in this sector.
Professional and financial services firms in this region are innovating. They are doing so in ways that are consequential for their clients, their workforces, and the regional economy. The task now is to make that innovation visible — to the firms themselves, to the policy makers who design the support frameworks, and to the national industrial strategy conversation that will determine where resources flow. That is a harder task than it sounds, because it requires changing not just the data but the mental model. But it is the right task, and the evidence is there to support it.
Hilary Smyth-Allen is CEO of SuperTech WM, the West Midlands’ cluster body for financial and professional services technology and is a partner of CIty-REDI, co-producing the evidence base for the region’s Next Generation Services cluster sector. This piece draws on discussions from a Senior Leaders’ Roundtable held in Birmingham on 12 June 2026.