Future of AI - Q2 2026

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Future of AI

“New AI tools based on large language models are changing what AI means for smaller firms.”

“We must work together to establish a verifiable, resilient intelligent digital infrastructure.”

Marco Bianchini,

Dr Dritan Kaleshi,

Economist, Centre for Entrepreneurship, SMEs, Regions and Cities, OECD

Director of Technology, Future Networks and Digital Infrastructure, Digital Catapult

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Why the UN’s new approach to AI could reshape global cooperation on governance For most countries, the AI debate has happened elsewhere, influenced by a few governments and companies with the resources to shape it. The science, safety frameworks and rules: all developed largely without the countries that will be most affected.

T WRITTEN BY Amandeep Singh Gill United Nations UnderSecretary-General and Special Envoy for Digital and Emerging Technologies

hat’s the problem the UN is trying to address, and the structure of its response matters as much as the ambition.

The Global Digital Compact, adopted by the General Assembly in September 2024, established two linked mechanisms. The first is an Independent International Scientific Panel on Artificial Intelligence and the second is a Global Dialogue on AI Governance, both created by a UN General Assembly resolution in August 2025.

The Panel produces the evidence, and the Dialogue is where that evidence meets political deliberation, with every country present, not just those that built the technology.

The Panel The Panel’s 40 members come from across disciplines, regions and development levels, serving in their personal capacity. Co-chaired by Canada’s Yoshua Bengio and the Philippines’ Maria Ressa, it publishes an annual policyrelevant report on AI’s opportunities, risks and impacts.

The mandate is bounded by design: not to tell states what to do, but to establish what the science shows.

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The Dialogue The Dialogue is where states decide what to do.

Convened annually, with the first convening July 6-7 in Geneva and in May 2027 in New York, it brings together governments, industry, civil society and the scientific community to work through topics such as safety, human rights and capacity-building. It produces a Co-Chairs’ summary, not binding decisions.

This openness is deliberate. The AI debate swings between miraculous productivity gains and existential risk dominated by a few wealthy countries. Neither helps a health ministry in Senegal weighing whether an AI diagnostic tool is safe, nor a labour ministry in Vietnam working out what automation means for its workforce.

AI won’t reach its potential for health, climate and development if the countries that need it most remain recipients of decisions made elsewhere. The Panel and Dialogue don’t make guarantees, but they’re a serious attempt to ensure that when rules get written, the whole world is present.

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FE AT U R ED TO PI C S: AI-Powered Mobility & Autonomous Vehicles Media & Entertainment in the AI Era The Future of Enterprise AI AI & The Future Workforce Cybersecurity & Robotics AI & Industrial Automation The Digital Health Summit AI in Energy & Sustainability

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Why is it so hard to create value from AI? Organisations globally are making unprecedented capital investments in artificial intelligence (AI), yet corporate leadership is increasingly asking a fundamental question: where is the return on investment?

D WRITTEN BY Matthias Holweg American Standard Companies Professor of Operations Management, Saïd Business School, University of Oxford

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espite significant investments, realised productivity gains consistently remain elusive.

The explanation for this stagnation lies in a historical lesson that is routinely overlooked: we are treating AI as an isolated, plug-and-play solution, forgetting that technology only generates economic value when effectively integrated into an optimised business process.

The modern productivity paradox To diagnose why AI so far has failed to transform bottom lines, it is worth revisiting the early deployment of personal computing.

During that era, economists identified a “productivity paradox,” noting that massive information technology investments yielded negligible macroeconomic productivity improvements. This occurred because businesses merely digitised existing manual workflows rather than re-engineering how work was performed.

Corporate correspondence provides a case in point. Historically, drafting a physical letter required significant time and administrative effort; consequently, organisations communicated selectively, ensuring each document possessed high utility.

The advent of email made communication instantaneous and virtually costless. Instead of capturing efficiency gains, however, inboxes were promptly flooded with a high volume of low-value electronic messages.

AI is now driving a structurally identical, yet far more severe, operational loop. Generative AI enables the mass production of content, resulting in the widespread “slopification” of business processes.

As employees offload critical thinking to algorithms, they generate

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polished but low-utility “workslop”. This forces downstream recipients to deploy intensive human labour just to verify and validate the output – an extra burden that routinely negates any initial productivity gains.

Mechanics of AI value creation The operational disconnect stems from a fundamental misunderstanding of the mechanism through which AI generates value.

Stripped of all hyperbole, AI functions strictly as a predictive engine.

The core financial and operational value of the technology is unlocked when historical data is used to identify latent patterns, which are then translated into a prediction.

This prediction is intended to inform a concrete corporate decision, which subsequently allows management to execute a more efficient allocation of organisational resources.

Reversing this logic reveals a critical boundary condition. If a business process does not conclude with a concrete decision capable of being informed by historical data, AI cannot yield value — a reality that directly surfaces two fundamental limitations for leadership: process variation and structural innovation.

Modern AI is built upon machine learning, which is a subfield of statistics. Hence, its performance is bound by core statistical principles: more data and lower process variation directly impact the quality of the output.

As enterprises transition toward agentic AI – deploying autonomous software agents to execute multistep workflows – this statistical dependency becomes a tangible operational challenge. Processes that are standardised and highly

repeatable can be successfully “learnt” and, in turn, augmented or even automated. Conversely, inherent process variability and structural exceptions will severely limit the productivity gains agentic AI can offer.

And for businesses relying on novel strategy, market intuition or operating in highly volatile contexts devoid of historical precedents, AI-generated predictions have little to offer. Because these systems function on probabilistic pattern matching, they are structurally biased toward what can be termed “backward similarity”: evaluating new ideas based entirely on what has already succeeded in historical datasets.

Consequently, AI will struggle to recognise a genuine strategic breakthrough or a novel operational paradigm; it merely replicates and enforces past conventions.

Beyond the hype of shiny AI models To capture true economic value, we must look beyond what is technologically feasible and analyse AI models within the broader intra- and interorganisational workflows in which they reside.

The real productivity return of AI is not measured by the speed of its algorithms, but by the net efficiency gains it brings to business processes.

Until we stop marvelling at the novelty of new AI tools and start systematically improving the workflows they support, AI will remain a compounding capital expense rather than a structural value driver.

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Agentic AI deployments and the elusive ROI Agentic AI deployments in financial services are no longer theoretical; they’re becoming a boardlevel priority.

A WRITTEN BY Alicia Ngomo Fernandez, Global AI Lead for Visa Consulting & Analytics, Visa Europe

utonomous agents, multi-step orchestration and AI systems that don’t simply answer questions but execute work are rapidly moving from experimentation to implementation.

Despite the momentum, ROI remains elusive.

Deloitte’s latest State of AI report found that while 74% of organisations hope AI will drive revenue growth, only 20% are seeing that outcome.1 Across client engagements, the success of agentic AI is rarely a model problem. It’s almost always a deployment and workflow design problem.

As foundation models become increasingly commoditised, competitive advantage is shifting from raw model capability toward the operational infrastructure required to embed AI safely and effectively into the enterprise.

Too many organisations ask, “How do we deploy agents?” The better question is, “Which workflow should be redesigned to create measurable business value?” If the workflow is poorly selected, inefficient or badly understood, introducing agents simply accelerates dysfunction.

Start with measurable outcomes, not technology The objective isn’t to “use AI”; it’s to improve the business. This requires disciplined value mapping alongside a realistic understanding of token costs, infrastructure overhead, human review requirements and exception handling. Around 30% of European banks cite cost reduction as their primary AI driver, though the opportunity to create entirely new value pools may prove equally important.2 Redesign the workflow before deploying the agent Legacy workflows were built for humans navigating system constraints, not AI operating with structured decision logic. They’re often full of inherited approvals, duplicated reviews, unnecessary handoffs and fragmented context. Simply inserting an agent creates inefficiency, not transformation.

Leaders must ask which decisions are repetitive, where the bottlenecks sit, which approvals are necessary and where human oversight must remain essential.

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Continuous outcome tracking Deployment isn’t the finish line; it’s the start of an optimisation cycle. Organisations need visibility into adoption, operational KPIs, token consumption, exception rates, model reliability and business impact over time.

Without this, it becomes difficult to distinguish transformation from expensive experimentation. The strongest AI programmes behave like optimisation engines, combining deployment with disciplined change management.

Embed delivery resources close to the business The strongest outcomes come from technical teams embedded directly alongside business operators.

Forward-deployed engineers, embedded consultants and cross-functional delivery squads consistently outperform distant implementation models because they understand how work is executed. Agentic AI is not a plug-and-play software rollout; it’s operational transformation.

Common industry pitfalls Organisations may consider avoiding pilots in search of a problem. Isolated experiments without executive ownership or measurable outcomes rarely scale. Token economics must also be taken seriously: workflows that look compelling in demonstrations can quickly become financially unsustainable at enterprise scale.

CFO scrutiny of AI cost architecture is increasing accordingly.

Governance cannot be replaced by optimism.

In regulated industries, teams typically focus on auditability, approval logic, compliance ownership and fallback mechanisms. Research among banking leaders shows that data privacy, security and compliance concerns outweigh even legacy systems as barriers to AI execution.2 This article is provided for informational purposes only and doesn’t constitute legal, regulatory, financial or other professional advice. Readers should obtain appropriate independent advice for their specific circumstances.

Security and risk are key It cannot be treated as a post-deployment checklist.

References: 1. Deloitte. (2026). The state of AI in the enterprise. https://tinyurl.com/bp5u88kb.

2. VCA. (2026). From AI promise to AI performance.

Clear human-agent ownership Teams need absolute clarity on who owns decisions, approves actions, handles escalation and is accountable when something goes wrong. Employees won’t trust systems where responsibility feels unclear, and leadership will hesitate to scale deployment if accountability is undefined.

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When autonomous agents access financial systems, handle sensitive data or execute transactions, the risk surface expands significantly. Identity, access control, fraud prevention, auditability and decision integrity become considerations from the start.

Clear security boundaries are essential around what agents can access, what actions they’re authorised to take and where human approval remains mandatory.

An agent that can approve invoices or place orders without strong controls isn’t innovation; it’s unmanaged risk.

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Agentic commerce: The next major shift in digital trade Agentic commerce is emerging as one of the most important shifts in digital trade since the rise of mobile commerce.

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t its core, agentic commerce refers to AI agents autonomously managing the purchase journey from discovery and comparison to payment and post-purchase support. Rather than simply recommending products, these systems can interpret user intent, evaluate options, apply budget or delivery preferences, select payment methods, execute transactions and support through fulfilment, returns or service interactions.

In that sense, agentic commerce isn’t just a new shopping interface; it’s a redesign of how transactions happen.

The shift is already happening For years, digital commerce has been shaped by search engines, marketplaces, branded storefronts and mobile apps.

Consumers are increasingly asking AI tools to narrow options, compare products and guide them to the best outcome. AI therefore becomes not just a new distribution channel but an agent in the commerce flow, one that could reshape visibility, brand influence and conversion in much the same way mobile transformed expectations around speed and convenience. McKinsey estimates agentic commerce could orchestrate $3T–$5T globally by 2030.1 Users arriving from AI Early signals already point to meaningful demand. Traffic from assistants often come with generative AI platforms to merchant stronger purchase intent sites reportedly surged 4700% yearon-year to July 2025,2 and this traffic because filtering, comparison appears more qualified than traditional and evaluation have already search traffic. Users arriving from AI assistants often come with stronger happened upstream. purchase intent because filtering, comparison and evaluation have already happened upstream. Data suggests lower bounce rates, higher engagement and improving conversion patterns.3 While these aren’t yet fully autonomous purchases, they indicate consumers are becoming increasingly comfortable delegating parts of the shopping process to AI.

How does it change the power dynamics?

For merchants, the opportunity comes with tension. Agentic commerce could unlock incremental demand, especially for smaller or niche brands that can be surfaced by AI without massive advertising budgets.

At the same time, it threatens merchant control over how products are presented, ranked and experienced. Businesses will need to decide how openly they participate — whether through structured product data for AI discovery, direct integrations with AI platforms or proprietary agents of their own.

Financial institutions, issuers and acquirers face a different challenge: supporting a new type of customer interaction. There are gains to be made by becoming the trust infrastructure of agentic commerce. Meanwhile, AI platforms stand to benefit by controlling discovery, orchestration and agent access.

The time to mobilise is now Payments remain the final piece of the puzzle.

Autonomous transactions typically rely on trust, permissions, identity verification, authentication, secure credential access and clear liability rules. The industry is now building these foundations through tokenisation, passkeys, agent identity standards, merchant validation flows and delegated authorisation frameworks.

Visa’s Intelligence Commerce solutions are one example of this evolution. Without such infrastructure, agentic commerce remains partially manual. With it, AI agents could securely execute end-to-end transactions on behalf of users.

This is why agentic commerce matters now. It represents the next major shift after mobile commerce, and it’s currently being designed. As trust, merchant readiness and payment infrastructure mature, commerce may increasingly become agent-mediated. Those slow to adapt may pay a high price.

This article is provided for informational purposes only and doesn’t constitute legal, regulatory, financial or other professional advice.

Readers should obtain appropriate independent advice for their specific circumstances.

References: 1. McKinsey. (2025). The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchants. https://tinyurl. com/3u8kc9x6.

2. Forbes. (2025). Gen-AI driven traffic to U.S. ecommerce sites up 4,700%, Adobe Reports. https://tinyurl.com/5y5evude.

3. Pandya, V. (2025). Adobe Analytics: Traffic to U.S. retail websites from Generative AI sources jumps 1,200 percent. https://tinyurl.com/ajbn2vdb.

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WRITTEN BY Alicia Ngomo Fernandez, Global AI Lead for Visa Consulting & Analytics, Visa Europe

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The next digital divide is not access to AI – it’s knowing what to do with it Small firms are no longer watching AI from the sidelines. Across OECD countries, the share of small businesses using AI rose from 7% in 2023 to 17% in 20251 — more than doubling in two years.

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edium-sized businesses show a similar pattern, rising from 14% to 30%2 over the same period. New AI tools based on large language models, now cheap and easy to access online, are changing what AI means for smaller firms.

WRITTEN BY Marco Bianchini Economist, Centre for Entrepreneurship, SMEs, Regions and Cities OECD

For now, uptake appears broad but shallow In 2025, the OECD supported the Canadian G7 Presidency by proposing a taxonomy of SME AI adopters, based on digital maturity, scope of AI adoption and the tools they use. The aim was to look beyond whether firms use AI and ask why and how they use it.

deployed agentic AI. Off-the-shelf AI is most often used for marketing, cited by 70% of its users. Customised AI is most often used for demand prediction (39%).

More than half of surveyed SMEs (54%) reported at least moderate benefits from AI. But only 21% saw a significant or transformational impact.

The danger is a new digital divide. Across the OECD, 52% of large firms used AI in 2025, compared with 17% of small firms. The gap is widening too: the firm-size gap in AI use reached about 35 percentage points in 2025, up from 23 points in 2023.

Policy can help bridge the gap The OECD identifies four key enablers: connectivity data, algorithms and computer skills and finance.

That means affordable high-speed access, shared data infrastructure, trusted AI tools, awareness campaigns, practical training and patient funding.

Support should be tailored. A shop testing chatbots doesn’t need the same help as a manufacturer building predictive systems. Many OECD governments recognise this challenge, and are working to make AI adoption simple, productive and safe for small businesses.

Support should be tailored. A shop testing chatbots doesn’t need the same help as a manufacturer building predictive systems.

In a group of 2,000 digitally active SMEs surveyed by the OECD in 2026,2 61% reported using at least one AI-enabled application. Of these, 76% were “AI novices”: firms using standard, off-the-shelf tools for isolated tasks. Only 5% used customised AI, and just 3.6%

References: 1. OECD (2026), “Empowering SMEs in the age of AI: The 2026 OECD D4SME Survey”, OECD SME and Entrepreneurship Papers, No. 78, OECD Publishing, Paris, https://doi. org/10.1787/bf5a9816-en.

2. OECD ICT Access and Usage By Business database, Accessed May 2026 (https:// data-explorer.oecd.org/vis?df[ds]=DisseminateFinalDMZ&df[id]=DSD_ICT_B@ DF_BUSINESSES&df[ag]=OECD.STI.DEP)

Harnessing the power of AI to transform UK financial services Fintech has transformed financial services, and now we stand at another pivotal moment as AI will further redefine our industry.

F WRITTEN BY Janine Hirt CEO, Innovate Finance

or consumers, AI can help enable increasingly hyper-personalised financial support, smarter budgeting tools and more accessible everyday services, helping people make money work better for them.

Imagine a world where you can ask your AI assistant when your car insurance is due, and it will seek out quotes at the optimal time, securing you the best value without the hassle of paperwork and ultimately execute the entire payment on your behalf.

AI can also be critical in strengthening fraud detection and cybersecurity, protecting customers and businesses alike.

Advancing key layers like smart data – structured data with secure connections firms can use, smart contracts — which can complete when triggered, fraud data sharing and digital ID, help create world-leading infrastructure that enables AI to operate at its full potential in financial services, driving productivity, innovation and growth across the wider UK economy.

Becoming a world leader in AI innovation and adoption The UK can become a world leader in AI innovation and adoption, and our innovative fintech industry can lead in facilitating this leadership within the financial services sector.

However, this ambition will only be fully achievable if the Government and regulators collaborate with industry to progress the areas outlined above.

To maintain our global competitiveness in financial services and cement our position as an international AI leader, we need bold and decisive action now. Other countries see the opportunity and are surging forward. It’s vital to our prosperity and security that we don’t let this opportunity pass us by.

The UK can become a world leader in AI innovation and adoption.

Building confidence in AI and the UK tech stack For UK fintechs to deploy and secure AI services at scale, they must be built on high-quality, reliable data.

Additionally, it must be connected to an individual, giving both security and confidence to the solutions being provided. This is where the broader tech stack becomes critical.

Innovate Finance, as the industry body representing UK fintech, has set out the necessity of developing a fully interconnected UK tech stack to maximise the AI opportunity.

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WRITTEN BY Evelyn Woodland Marketing Manager, GOVNET TECHNOLOGY

From Bletchley to algorithms: Britain’s new AI security drive

AI will be foundational for other areas of deep tech innovation The convergence of AI with other areas of deep tech innovation will be critical to unlocking new opportunities to grow the economy.

The UK is evolving from an AI user to a global leader, with new infrastructure and innovation drives designed to secure our national digital future.

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uring World War II, British breakthroughs at Bletchley Park helped secure the nation and the world.

Now threats emerge in digital systems, data infrastructures and autonomous decision-making. As the only government outside the US given access to Anthropic’s Claude Mythos upon launch, it cements the UK’s credibility and international recognition of its role in AI safety and governance.

From adopter to leader January’s update on the AI Opportunities Action plan has shown benefits across departments.

New tools are looking to save officials 75,000 days of manual analysis yearly.1 One-third of NHS chest x-rays are AI-assisted, speeding up diagnoses and improving care.2 Beyond AI deployment, funding is being poured into creating AI growth zones, with the Isambard-AI supercomputer being turned on in Bristol and a £2 billion commitment to increase the UK’s computing capacity twentyfold by 2030.2 The UK is no longer positioning itself as a user of AI systems but as a shaper of how they’re understood, evaluated and made safe at scale.

With uncertain global power dynamics, the UK Government is looking inwards to protect the nation’s future and safeguard against emerging threats. Security Minister Dan Jarvis MBE stresses, “Basic cyber hygiene is no longer optional, but the absolute minimum

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we should expect of any serious organisation operating in the modern economy.” The Government is calling out industry to be ready and is actively looking to collaborate with them to establish the UK as a leading player.

With uncertain global power dynamics, the UK Government is looking inwards to protect the nation’s future and safeguard against emerging threats.

Forums for innovation The DigiGov Expo (23rd-24th September) will bring together the Government to debate AI development. Over two days across six theatres, including a dedicated AI and cybersecurity theatre, the events look at how to foster innovation and best practices.

Minister Jarvis says it best, “We want you to work with us directly.

Partner with the UK Government to co-develop AI for national cyber defence.”

References: 1. DSIT, et al. (2025). Ground-breaking use of AI saves taxpayers’ money and delivers greater government efficiency. https://tinyurl.com/2u5jdpj8.

2. DSIT. (2026). AI Opportunities Action Plan: One Year On. https://tinyurl.com/bdzd5dtm.

WRITTEN BY Dr Dritan Kaleshi Future Networks and Digital Infrastructure, Digital Catapult

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n converging AI with other capabilities, including advanced connectivity, we must work together to establish a verifiable, resilient intelligent digital infrastructure critical to make the UK future-ready.

The UK’s AI industry is on track to increase the country’s GDP to over £550 billion by 2035, but the opportunity lies in how AI-enabled solutions can be integrated with other areas of deep tech.1 The UK’s deep tech ecosystem attracts significant global investment, with UK startups receiving the most funding last year, raising $5.2 billion in capital.2 Converging AI with deep tech innovation thus represents a significant opportunity to grow the economy and futureproof the UK in areas like advanced resilient connectivity.

Industrial value of convergence At Digital Catapult, we often refer to the digital economy DNA: Data, Networks and Artificial Intelligence. True economic success is achieved when they work together. AI’s ability to create resilient, adaptive, secure and inclusive systems at a global scale means that it’s foundational to a resilient and AI-ready connectivity infrastructure.

For example, the next generation of AI-enhanced connectivity will see networks autonomously manage, optimise and self-heal in real time, responding to changing demand across industries, communities and regions. These capabilities will be strengthened by additional convergence with digital twins and quantum-safe encryption, protecting communications against cyber threats.

Convergence in action The era-defining technology maturation we’re witnessing with AI now is unlocking unprecedented opportunity.

Converging areas of deep tech innovation, however, aren’t easy. This is where convening capabilities and establishing frameworks to ensure that AI is used in a safe, resilient and open way are essential.

Digital Catapult’s accelerator and innovation programmes drive collaboration to develop and deploy AI-enabled solutions that see the practical application of deep tech innovation across industry, and ensure that AI adoption in infrastructure is resilient, safe, secure and open when converged with data-driven networks.

References: 1. Microsoft. Unlocking the UK’s AI potential: harnessing AI for economic growth. https:// tinyurl.com/23stmk9e.

2. King, et. al (2026). The European deep tech report. https://tinyurl.com/46mnemt5.

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