Article 3.2 · Regionalism, Community Wealth, and the Local Professional

Written by Will Whawell. Human intelligence throughout; AI assisted with the drafting.

T3PS Legal Dynamics · Refreshed June 2026

There is a paradox at the heart of the current AI revolution that tends to be glossed over in the rush of enthusiasm about productivity gains and efficiency frontiers. AI is, by its nature, borderless — a technology developed in the valleys of California and the server farms of Virginia that can be accessed equally from a laptop in Edinburgh or a desktop in Stoke-on-Trent. Its benefits, its disruptions and its risks should, in theory, be distributed democratically across geography.

They are not. And understanding why they are not — and what professional services firms can do about it — matters enormously for anyone thinking seriously about the future of regional economies.

The Geography of Disruption

The OECD has found that urban workers face an average AI exposure rate of 32%, compared with just 21% for rural workers. This sounds, at first reading, like good news for rural and regional areas — lower exposure means less disruption. But it is more complicated than that. Lower AI exposure in regional economies also means lower access to AI-driven productivity gains. The regions that have historically struggled with underinvestment, skills shortages and economic leakage are not being protected from AI disruption. They risk being left out of AI-driven growth.

The OECD's analysis is unambiguous about the risk: this trend could widen existing urban-rural income and productivity gaps, compounding the digital divide that was already a feature of the pre-AI economy. The regions that most need investment and capability are at risk of receiving less of both.

This is the paradox. AI could be a profound equaliser between firms of different sizes, as explored in the previous article. But it could simultaneously be a profound divider between regions — accelerating the concentration of economic value in already-wealthy urban centres whilst the periphery falls further behind.

The question for the local professional firm — the solicitor in Preston, the accountant in Inverness, the legal project manager in Coventry — is not whether AI disruption is coming. It is whether AI can be harnessed as a tool of local economic resilience rather than a mechanism for extracting value upwards and outwards.

What Preston Knew Before the Algorithms Arrived

Before we get to AI specifically, it is worth understanding the conceptual framework that some UK regions have already been building — because it turns out to be remarkably relevant.

Preston is a post-industrial Lancashire city that was, by the early 2010s, in serious economic difficulty. Central government austerity had halved the council grant. A £700 million city-centre development scheme had collapsed in the wake of the 2008 financial crisis. The Index of Multiple Deprivation placed the city in the bottom 20% nationally, with a sixteen-year life expectancy gap between its wealthiest and poorest neighbourhoods.

What happened next is one of the more instructive stories in modern UK economic development. Working with the Centre for Local Economic Strategies (CLES), Preston City Council developed what became known as the Preston Model — a community wealth building approach centred on anchor institutions: the university, the college, the constabulary, the council itself, the housing association. These organisations were asked to do something simple: where possible, buy local.

The results, measured over the following decade, were significant. Over £70 million was redirected back into the Preston economy and £200 million into the wider Lancashire economy. Unemployment fell from 6.5% in 2014 to 3.1% in 2017. Preston moved out of the bottom 20% on the deprivation index. The city was named one of England's best places to live. None of this required a technology revolution. It required a change in how existing institutions directed their spending.

The five pillars of community wealth building — inclusive and democratic enterprise, locally rooted finance, fair work, just use of land and property, and progressive procurement — are not inherently technological. But every single one of them is amplified when the local professional firms that serve those anchor institutions are themselves embedded in and committed to the local economy.

Scotland's Legislative Moment

The Preston Model has been an influence — sometimes explicit, sometimes implicit — on policy thinking across the UK. In Scotland, that thinking has now produced legislation. The Community Wealth Building (Scotland) Bill was introduced in March 2025 and has since received Royal Assent, becoming an Act of the Scottish Parliament. It places duties on local authorities to publish and implement community wealth building action plans, and requires Scottish Ministers to set out their own CWB commitments.

This is significant not merely as a Scottish policy development but as a signal of direction. Community wealth building is moving from voluntary aspiration to legislative duty. The professional firms that are embedded in those communities — that understand the anchor institutions, the local procurement landscape and the economic priorities of the area — have a role to play in this architecture that an office in London cannot replicate. The local solicitor who has advised the local university for twenty years is an asset to that CWB ecosystem in a way that a parachuted-in national firm is not.

AI's Economic Geography: The Small Business Evidence

The evidence on AI and local economic activity is emerging, and some of it is more optimistic than the regional divide story might suggest — provided the technology is accessible and actually used by local businesses.

Research by GoDaddy in partnership with UCLA Anderson economists has quantified something genuinely striking. In the period following the rollout of AI tools for small business website creation, each additional website published using those tools was associated with the creation of 20 jobs per county. Among small business owners using AI tools, 72% reported increased productivity and 61% observed higher revenues within six months. This is AI working at the local level — not as a macro force reshaping global capital markets, but as a practical tool enabling small businesses to compete, grow, and generate employment.

The professional services dimension of this matters. The small business that uses AI to build its website still needs a local solicitor to advise on its terms and conditions, its employment contracts, its lease. It still needs an accountant to handle its tax affairs. The AI-enabled small business economy creates demand for local professional services, not demand for a national firm to handle it remotely from a central hub.

This is the counter-narrative to the fear that AI will hollow out regional economies by enabling the consolidation of professional services into a handful of national platforms. The evidence from the small business data suggests something more nuanced: AI enables more small businesses to operate effectively, and more small businesses means more demand for the local professional adviser who knows the landscape.

The Automotive Parallel

It is worth briefly considering a sector that is ahead of the legal profession in thinking about AI and regional supply chains: automotive manufacturing.

The automotive industry has been grappling with regional supply chain questions for decades — where to source components, how to manage just-in-time delivery logistics, how to balance cost efficiency with resilience. AI is now being used across the sector to optimise these regional supply chains: predicting component demand, flagging supply disruptions before they materialise, identifying local alternative suppliers. The result, in the best cases, is not the elimination of regional suppliers but the more intelligent engagement with them.

The small supplier in the West Midlands does not lose business to a competitor in Bratislava because the algorithm optimises for price alone. The algorithm can be made to optimise for resilience, carbon footprint, and local economic value as well.

The parallel for professional services is instructive. AI used thoughtlessly will drive consolidation and homogenisation — a few large platforms serving everyone from the centre. AI used thoughtfully can strengthen regional professional capacity by making local firms more capable, more responsive and more able to serve complex needs. The choice of how to use the tool is not made by the algorithm. It is made by the humans directing it.

Boutique Firms as Anchors of Local Wealth

Here is the structural argument that deserves more attention. When a regional business uses a national firm to handle its legal work, a proportion of the value generated leaves the region. Profits are distributed to partners in London. The junior lawyers doing the work may be trained and developed, but their career trajectory likely leads them away from the region rather than deeper into it. The economic relationship is extractive, even if inadvertently so.

When the same business uses a local boutique firm, the value circulates differently. The profits stay local. The lawyers build their careers locally. The expertise compounds in the region rather than draining from it. The firm becomes, in the language of community wealth building, an anchor institution in its own right — not as grand as a university or a hospital, but embedded in the local economy and committed to it.

AI makes this proposition more powerful because it means the local boutique can no longer be dismissed on grounds of capability. The solicitor in Carlisle with access to the same AI research tools, drafting platforms and project management systems as her counterpart in a London firm is not offering an inferior service because she is in Carlisle. She may in fact be offering a superior one — because she understands the local land ownership patterns, the regional court culture, the commercial practices of the local business community — and she no longer has any technology deficit to apologise for.

Competing Globally, Rooted Locally

There is one more dimension to this that deserves careful attention. The claim that professional firms must be in London, or must be large, to serve clients with complex or cross-border needs has been significantly weakened by technology — well before the current AI wave. Video conferencing, cloud document sharing, digital signatures and remote working normalised during the pandemic have all made geography less determinative.

AI extends this further. A boutique firm in Newcastle advising on international arbitration, in partnership with specialist counsel in other jurisdictions accessible through networks and referral relationships, can serve a multinational client as effectively as a London full-service firm — and can do so whilst retaining and reinvesting the economic value of that work in the North East.

This is what AI-powered regionalism actually looks like in practice. It is not a retreat into parochialism. It is competing globally whilst being economically rooted locally. The talent in regional cities that has historically flowed south can stay, build, and compound — because the tools and the client expectations are no longer aligned against them.

The Preston Model showed what could be achieved with local procurement and anchor institution commitment, before AI arrived. The question now is what becomes possible when those same principles are combined with technology that gives the local professional firm the capability to compete with anyone, anywhere, whilst keeping the economic value of their success in the communities they serve.

Questions worth sitting with:

1.    If AI-enabled boutique firms can now serve complex client needs as effectively as national practices, what is the legitimate justification for professional services value continuing to flow overwhelmingly towards London and away from regional economies?

2.    Scotland has legislated for community wealth building. Should England follow, and what role should professional services play in any such framework?

3.    As AI reduces the time-cost of legal and professional work, who captures that efficiency gain — the client, the firm, or could some mechanism be designed so that the regional economy benefits too?