In recent years, generative artificial intelligence—most notably ChatGPT—has burst onto the global stage, promising profound transformations in how we work, communicate, and innovate. Yet the question that stands at the intersection of economics and public life is: how will these AI advances reshape the competitive landscape for firms, and what does that imply for industrial upgrading in a country like the UK?
In this article I offer a commentary aimed at the general British public, weaving together economic insight and real-world evidence to explain how generative AI can act as a lever for competitiveness, what challenges it spawns, and what strategic responses are required. My goal is to make this not only intellectually illuminating but also a compelling read—so that it gets found, clicked, and shared.
Before diving into industrial upgrading, let’s clarify why ChatGPT (and similar AI models) are more than just clever toys or productivity aids. Historically, major leaps in computational technology (such as enterprise resource planning, cloud computing, mobile internet) have had long “creep phases” before their full impact emerges.
Generative AI differs in that it is creative, adaptive, and interactive. It can produce text, code, design, marketing campaigns, legal draft, summaries, translations—and adapt to prompts. That flexibility allows it to infiltrate many business processes. In effect, ChatGPT is a general-purpose innovation—comparable in strategic importance to electricity or the steam engine in prior centuries.
As a general-purpose innovation, its economic significance is rooted in diffusion across sectors, compositional shifts in tasks, and spillover effects—which, in turn, drive productivity, scale, and competitive repositioning.
“Industrial upgrading” refers to the process by which a nation’s industries evolve from lower-value to higher-value activities—through technology adoption, skill development, innovation, and supply chain sophistication. For the UK, whose manufacturing base is smaller now than a century ago, upgrading increasingly means pushing into high-value services, deep tech, and knowledge-intensive production.
The UK has strong advantages—world-class universities, clusters in fintech, biotech, AI R&D hubs (e.g. Oxford, Cambridge, London). But it also faces challenges: fragmented industrial policy, skills shortages, regional inequality, and lagging digital adoption in many SMEs (small and medium enterprises).
Against that backdrop, generative AI is not just an opportunity—it may become a threshold condition for effective upgrading. Firms that fail to integrate AI into their operations risk being outcompeted not gradually but suddenly, via a step-change in efficiency and innovation.
To understand how ChatGPT might enable industrial upgrading, we must dissect the mechanisms through which it affects firms’ productivity, capabilities, and competitive posture. Below are several channels:
Many business tasks are partially ritualistic: drafting reports, summarizing data, customer correspondence, coding boilerplate, ideation. ChatGPT can absorb the routine core of such tasks, leaving human workers to focus on higher-order, judgment-intensive work.
This task recomposition effect allows firms to reallocate labour from mundane tasks to creative, relational, or strategic roles. In aggregate, that raises labour productivity.
Generative AI can accelerate ideation, prototyping text or design, experimenting with business models, drafting content, or writing code scaffolds. As such, firms can move through development cycles faster, reducing time to market.
Faster innovation is critical in sectors with rapid turnover (digital services, marketing agencies, software) and can also help more traditional sectors (manufacturing, logistics) in peripheral but non-core areas (customer engagement, process documentation, internal training modules).
One of the constraints for firms is to tailor offerings or communications to individual customers. ChatGPT offers the possibility of mass customization in communications, marketing, customer support, and content, allowing firms to scale personalization without linear labour scaling.
This enhances customer experience, loyalty, and revenue growth potential.
SMEs often lack the in-house research capacity. ChatGPT can act as a knowledge aggregator, summarizing literature, regulatory guidance, or market trends, and providing digestible intelligence to firms. It lowers barriers to access external knowledge, leveling the playing field.
Generative AI adoption is rarely plug-and-play. Firms must invest in data infrastructure, digital literacy, workflow redesign, integration with legacy systems, and human capital. These complementary investments magnify the gains from AI.
Because of spillovers, even firms that adopt AI minimally benefit indirectly through ecosystem improvements (better tools, shared platforms, more skilled labour).
In industries where AI yields large margins or radical efficiencies, incumbents may be displaced abruptly by AI-native entrants. Existing firms must leap or be left behind. That dynamic can catalyze discontinuous upgrading, where entire sectors advance in productivity simultaneously.
While generative AI holds great promise, realizing that potential is far from assured. Some of the key challenges are:
Reallocating labour from lower-end tasks to higher-end roles assumes that workers can reskill. Without strong investment in training and education, falling jobs in middle segments may create social and political backlash.
Policymakers and firms must anticipate transition support, lifelong learning, and retraining pathways.
Large firms, tech-savvy ones, or those with strong data infrastructures will adopt AI faster, deepening the competitive chasm between them and laggards. Industrial upgrading thus may become uneven across regional and sectoral divides.
Hence, adoption support, shared platforms, and infrastructure subsidies may be essential for inclusive upgrading.
Generative AI systems require high-quality, clean, relevant data. Many firms—especially in smaller or older industries—lack organized, digital data. Without strong data governance and integration, AI can underperform or backfire.
Moreover, privacy, data protection, intellectual property, and regulation (including the evolving EU/UK AI regulation frameworks) could impose constraints on usage and innovation.
AI-generated outputs may carry biases, propagate errors, or lack accountability. Firms must embed robust oversight, transparency, and sometimes human-in-the-loop checks. Misuse or errors can damage reputation.
Major AI model providers (OpenAI, Google, Anthropic, etc.) may dominate the supply side. Firms reliant on external API access could become dependent or subject to pricing power. Ensuring interoperability, open models, or national capacities might counter excessive concentration.
AI workloads (especially large models or fine-tuning) consume computing power, energy, and storage. Without affordable access to cloud or edge compute, many firms cannot fully exploit capabilities.
Given the above, what should British businesses and industrial policy actors do to harness ChatGPT and accelerate upgrading?
Firms should not concentrate on a single “killer use” but experiment across multiple business functions—marketing, operations, R&D, support, and knowledge work. That diversifies risk and captures synergies.
Small-scale pilots, internal “AI labs,” and cross-functional teams can help identify high-leverage use cases.
High returns only come when foundational systems are in place—clean data pipelines, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, digital supply chain systems.
Strengthening data hygiene, interoperability, and APIs will pay high dividends.
Don’t replace humans but augment them. Cultivating prompt engineering literacy, evaluation skills, AI oversight, and domain–AI bridging roles will maximize gains. Firms should design workflows that embed AI as a collaborator.
Especially for SMEs, cooperation in shared AI infrastructure, domain-specific models, and industry consortia can lower costs and accelerate adoption. Local clusters (e.g. in Manchester, Cambridge, innovation districts) can become hubs for experimentation and spillovers.
Firms and government should jointly invest in reskilling programs, vocational training, bootcamps, and continuing education in AI-adjacent areas: prompt engineering, data science, AI audit, human-AI teaming.
Early efforts in universities, apprenticeship programmes, adult education can cut the friction in transition.
Leadership in responsible AI deployment can build trust and safeguard firms from regulatory backlash. British firms should engage with standard setting, transparency frameworks, and ethical guardrails early—not after failure.
A balance between fostering innovation and safeguarding consumers is critical.
To avoid overdependence on external AI providers, the UK government or industry consortia might invest in national or domain-specific models. Publicly accessible AI infrastructure can level the playing field.
Public-private labs, grants, shared compute infrastructure, and open model initiatives are possible levers.
Let me briefly sketch a few stylised case studies to make these arguments concrete for a general audience.
Suppose a UK SME making industrial pumps wants to upgrade into “smart pumps” (i.e., pumps with digital sensors, predictive maintenance, and connected services). Historically, it competed on cost and engineering.
With generative AI, it can:
Use ChatGPT to generate technical documentation, user guides, maintenance protocols, or training modules for clients, at lower cost.
Use generative models to summarise field sensor data, predict failures, and generate alerts or maintenance suggestions in natural language.
Provide personalized maintenance guides to customers via conversational interface.
Use AI to draft marketing, proposals, bids, and custom content in multiple languages.
These enhancements differentiate from commodity pumps. The SME upgrades from hardware-only to hardware + AI-enabled services, increasing margins and lock-in.
Consider a small British content agency serving clients in travel, retail, or education. Historically, they hire writers, editors, illustrators, translators.
With ChatGPT:
They can accelerate drafting, produce multiple variants of copy, translate, or generate marketing content at scale.
Human editors refine and shape tone, freeing bandwidth for strategy, ideation, or client customization.
They can offer lower-cost content bundles, localised content, and rapid turnaround, expanding their addressable market.
This changes their competitive dynamics—when margins are compressed, scale and speed become core assets, powered by AI.
Firms in finance or law already deal in information. ChatGPT can assist in drafting contracts, summarising regulations, generating regulatory filings, performing due diligence, or aiding client interviews.
The barrier is higher (because accuracy, liability, legal responsibility matter), but firms that embed careful AI oversight can leap in productivity, reduce backlogs, and focus human effort on high-stakes judgment.
These cases illustrate how AI doesn’t replace sectors—it repositions them.
For the promise of AI-driven upgrading to be realised across the UK economy, firms alone cannot carry the burden. Strategic public policy must accompany the shift. Below are key policy prescriptions:
Government support—through public investment in compute infrastructure, shared AI models (open-source, domain-specific), and national labs—can mitigate barrier of entry for smaller firms.
Industrial strategy should view AI infrastructure as analogous to broadband or electrification.
Targeted grants or matching funds for AI adoption projects—especially in SMEs and lagging regions—can reduce the upfront cost barrier. R&D tax credits should explicitly cover AI-related development.
Regionally, subsidy schemes could encourage uptake in the North, Midlands, and Scotland to reduce digital polarization.
Update curricula at all levels (secondary, further education, universities) to include AI literacy, data skills, prompt engineering, and human–AI interface training.
Fund nationwide adult retraining programmes. Incentivize industry–academia partnerships and apprenticeships in AI-related domains.
To avoid stifling British innovation, regulation should be risk-based, adaptive, and co-designed with industry. Fast-track sandboxes, standards bodies, and oversight systems can provide guardrails without throttling growth.
Fund AI innovation hubs outside London. Encourage anchor institutions (universities, local businesses, councils) to form AI clusters. Provide incubator grants, shared facilities, and networking support.
Establish institutional frameworks (data trusts, data commons) that allow firms to access shared datasets in privacy-safe ways. Promote standards, APIs, and safe environments for data exchange.
The UK government, as a major purchaser, can act as early adopter of AI-augmented services, setting demand signal and reducing risk for firms investing in AI. For example, in local services, healthcare, education, public administration.
No revolution is without risk. Let me flag possible failure modes and how to hedge against them.
Overhype and Disappointment
AI may promise more than it can deliver in practice. Firms that overcommit might suffer backlash or disillusionment.
Mitigation: Encourage staged pilots, human-in-the-loop control, performance monitoring, and managed expectations.
Lock-in to Foreign Supplied Models
If British firms rely entirely on external models, they may lose strategic autonomy or pay rising fees.
Mitigation: Support open models, local AI ecosystems, and collaborative development.
Widening Inequality and Regional Divide
The gains from AI could accrue unevenly, exacerbating socio-economic divides.
Mitigation: Focus on inclusive policy, targeted support for lagging firms, upskilling in all regions.
Regulatory U-turns or Overreach
Overly restrictive regulation may discourage innovation or prompt firms to relocate.
Mitigation: Use co-regulation, agile policy, testing environments, and industry consultation.
Ethical/Legal Liability or Reputational Risk
If AI makes mistakes (biased outputs, legal errors), firms may face lawsuits or reputational loss.
Mitigation: Maintain human oversight, audit logs, explainability, insurance mechanisms, and internal ethical review boards.
Energy, Environmental, and Sustainability Burdens
Large AI models consume energy, with carbon implications.
Mitigation: Encourage green computing, carbon offsets, energy-efficient model architectures, and regulation of model energy use.
For a non-specialist British audience, here are key takeaways:
AI is not just for tech giants: ChatGPT can boost competitiveness even in traditional sectors if used smartly.
The future is collaboration, not replacement: Firms combining human judgment with AI tools will be winners.
Timing matters: Early adopters may gain step-change advantages; delay may become costly.
It’s a national competitiveness issue: Whether the UK leads or lags in AI adoption may shape its economic future.
This is not only a business issue—it’s a social issue: Ensuring equitable diffusion, reskilling, and fair regulation are essential for broad public buy-in.
ChatGPT and generative AI herald more than incremental change—they may usher in a new kind of industrial revolution, one centered on knowledge, adaptability, and human–machine collaboration. For Britain, this technology offers a route to meaningful industrial upgrading—provided that firms, educators, and policymakers act with foresight.
To capitalise on this era, British businesses must adopt AI experimentally and strategically, invest in data and human capital, and weave AI into the core of their operations. Meanwhile, public policy must lower adoption barriers, support shared infrastructure, promote fairness, and steward regulation.
If the UK can orchestrate that alignment—between generative AI, firm strategy, and industrial policy—then we may look back on ChatGPT not as a novelty but as a pivot point: the hinge on which Britain’s next wave of economic dynamism swings.
