European executives warn AI & net zero risk stalling

European technology executives warn that AI adoption and net zero strategies could stall without a sharper focus on implementation, governance and measurable impact. Their comments come as investment in AI surges and scrutiny of corporate climate action intensifies.

Across the region, AI funding and enthusiasm remain strong. S&P data shows European private equity and venture capital firms invested USD $6.8 billion in AI companies in 2025, up 83% year on year. In the first five months of 2026, they closed 141 deals worth USD $2.8 billion. Yet research from Personetics shows only 18% of banks have embedded generative AI into day-to-day operations, even though 80% of executives describe it as transformational.

Theo Wasserberg, Head of UK and Ireland at treasury platform Embat, drew a parallel with the early days of cloud computing, when many pilots failed to deliver systemic change.

"Whether it's payments, banking or treasury, everyone can list a dozen AI use cases they were pitched or saw in a demo. The challenge wasn't innovation, identifying a problem on paper, or imagining solutions. It was turning an exciting demo into something teams could trust and use every day. Top-down AI mandates produced impressive presentations that never changed how work got done. We learned that real progress happens when the people closest to the work can design and own their agents. The lesson is simple: without the right infrastructure, AI can't scale beyond the team that built it. AI adoption shouldn't be a cosmetic upgrade on top of legacy systems. AI delivers value only when it's tightly scoped, with defined users, datasets and measurable outcomes. This is what I call contained value: specific, auditable use cases where you know what AI will do, who it serves and how success will be measured. We're entering a period reminiscent of cloud adoption circa 2015. Patterns are emerging, but mass adoption is still ahead. The next five years will be about depth: understanding where AI delivers real value, where human oversight remains critical, and how to build systems that work with people, not around them. Leaders must look at both time-to-value and strategic impact when measuring the ROI of advanced AI. Unlike legacy systems, AI-native solutions can, and must, show measurable results rapidly," said Wasserberg.

Enterprise leaders are also grappling with geopolitical and economic risks around AI infrastructure and spending. Kurt Muemehl, Head of AI Strategy at Dataiku, said concerns over potential US government stakes in major model providers raise strategic questions for European executives.

"The news that the US government could take a stake in OpenAI, and that other models could follow suit, is a wake-up call for Europe on two fronts: dependence on foreign-owned AI and the risk of critical AI capabilities becoming increasingly controlled by powers on another continent. Enterprise leaders should now be asking themselves a simple question: Where does our intelligence come from? What happens if it's suspended? Can we keep running without asking permission from a government on another continent? If the answer to any of those questions is uncertain, it's time to reassess your AI strategy. You need to ensure that your core assets remain under your control: your agents, workflows, data, policies and governance. The model itself should be treated as a supplier that can be replaced if necessary, not as the foundation on which the entire business depends. For anyone questioning what to do now, the focus should be on setting a strategy that minimises dependency risk in AI. First, assess how you build new AI so you can switch to non-US models if needed. Then assess your existing AI landscape and whether you can migrate critical processes to open source, either as failover if systems are shut down or permanently. In the rush to act, enterprises must still make time to ensure their governance layer is intact. That will allow them to see and prove what each model is doing and ensure output stays within policy regardless of the underlying model. Also prepare for future issues by testing backup models in advance with real evaluations, so that if and when the primary model disappears, the alternative is approved for immediate deployment. If the model you depend on can be turned off by someone other than you, such as the US government, the key question is whether your enterprise can continue operating when that happens," said Muemehl.

Cost visibility is emerging as another constraint. Kevin Dunn, Vice President and General Manager, EMEA at cloud storage firm Wasabi, highlighted the impact of data infrastructure on AI returns.

"Determining value from corporate AI deployments is increasingly an economic race defined by predictability over how much it costs to store, move and copy data. Our latest Cloud Storage Index found that organisations are now allocating around two-thirds (62%) of their AI budgets to the data, storage and compute needed to support AI, yet only a quarter (25%) say they're seeing a positive return on their AI investments today. At the same time, most UK organisations (84%) cite storage fee-related charges as a reason for overspending their cloud budgets. As AI adoption accelerates, the real constraint is visibility and control over what it actually costs to innovate at scale. When a significant share of spending is driven by variable fees rather than predictable capacity, organisations lose the ability to forecast and optimise AI economics with confidence," said Dunn.

Several executives describe a shift from experimentation to what they call production-grade AI in core operations. Dharam Gurbani, Chief Growth Officer at AI engineering firm Ascendion, said the focus is moving from pilots to reliability in critical services.

"The real test of AI, in the UK and beyond, isn't ambition, pilots or strategy. It's whether AI makes critical work more reliable in the institutions people depend on every day. Across the UK enterprises we work with, leaders have stopped asking whether AI can be adopted. They are asking a harder question: where does it improve productivity, reduce operational drag, strengthen compliance and improve customer experience without adding pressure to already stretched systems? The answer is that AI has to prove itself inside the operating fabric of the enterprise, not as something bolted on. In practice, that means modernising outdated systems, clearing bottlenecks, improving decision support, making compliance easier to evidence and giving teams the confidence to do higher-value work. The most credible AI programmes keep accountability with people. They help teams move faster while governance and judgement stay firmly in place. That is the distinction that matters. AI earns trust through measurable outcomes in the institutions people rely on, which means it must be production-grade. That is the AI worth appreciating," said Gurbani.

Trust in AI output remains a recurring concern. Evan Reiss, Senior Vice President of Marketing and Innovation at PDF software provider Foxit, said verification work is eroding productivity gains.

"Of course, AI Appreciation Day is an opportunity to recognise how quickly AI has evolved. But let's also ask whether organisations actually trust the work it produces. Our latest research found that while 89% of executives believe AI is improving productivity, those gains are often undermined by the time spent verifying AI-generated outputs. Executives save an average of 4.6 hours a week using AI, but spend almost as much time checking its work. Without confidence in the output, productivity gains quickly disappear. The burden is now on technology companies to help close this trust gap. The conversation needs to move beyond AI adoption and towards AI confidence. For the past few years, organisations have focused on where they can deploy AI. The bigger challenge is knowing where they can rely on it. Success won't be measured by the number of AI tools a business adopts, but by whether those tools help people make better decisions, reduce rework and enable work to move faster with confidence. As AI agents become more capable and take on increasingly complex tasks, trust will become the defining competitive advantage. That starts with the quality of the information AI is given, alongside the governance and verification processes that support it. Organisations that get these foundations right will give employees the confidence to spend less time checking AI and more time acting on its insights," said Reiss.

Use cases are also beginning to concentrate around document handling, transactions and decision support. Luis Blando, Chief Product & Technology Officer at application platform OutSystems, said most enterprises are pursuing pragmatic deployments rather than full autonomy.

"Despite the hype around fully autonomous systems, most enterprises today are using AI in far more practical ways, and that's where the real value is showing up. The strongest use cases cluster around three areas: processing documents that would otherwise require human review, handling high-volume transactional work such as mapping incoming orders, and supporting decision-making by making sense of unstructured data. In these scenarios, AI excels at summarising complexity and offering recommendations, but not at making final calls, unless organisations are willing to accept mistakes. Used poorly, AI behaves like a team of interns: fast and prolific, but still requiring oversight and double-checking. Used well, it becomes a force multiplier for simpler applications, especially when fuelled with the right data and guardrails. Trust doesn't come from autonomy alone. It comes from knowing when AI should assist, when humans should decide, and how the two work together," said Blando.

Malte Ubl, Chief Technology Officer at Vercel, said enterprises are increasingly mixing AI models to balance quality and cost.

"Days like today usually surface one of two conversations: how fast the models are improving or what could go wrong. What we see in the data is more practical. Teams are getting deliberate about where they spend their AI budget, and where they don't. Traffic through the Vercel AI Gateway keeps climbing, even as costs rise. But how teams spend is changing. Cheaper models handle high-volume work; the most capable models are reserved for tasks where quality, reliability and accuracy actually matter. No single model wins every job, so the work is routing each task to the right one. The teams getting the most out of AI are the ones that know which model fits which task and balance quality, speed and cost accordingly," said Ubl.

Alongside AI, European executives are also reassessing climate strategies in the electronics sector. Net Zero Awareness Week has prompted debate over whether current approaches to recycling and offsetting go far enough across product lifecycles.

Arjen Steenbergen, ESG Manager at peripherals maker Trust International, said emissions reduction must start in design and manufacturing rather than at end of life.

"Net Zero Awareness Week is a reminder that recycling alone won't get the electronics industry to net zero. Emissions reductions have to happen across a product's entire lifecycle, not just at the very end. More than 80% of a headset's climate footprint is generated during manufacturing, while global e-waste is expected to exceed 80 million tonnes by 2030. Together, these figures show why reducing environmental impact has to begin long before a product reaches consumers, through better design and more responsible manufacturing. That principle shapes how we approach sustainability. At Trust, our ambition is to achieve carbon-neutral operations by 2030, supported by practical actions such as increasing the use of recycled plastics recovered from consumer products. Maintaining EcoVadis Gold status for five consecutive years also provides independent validation that we are making measurable progress rather than simply setting ambitious targets. Ultimately, consumers, customers and regulators are looking for evidence, not promises. Businesses will be judged less on the targets they announce and more on the progress they can prove. The transition to a lower-carbon economy will come from consistent improvements across the industry, not a single breakthrough," said Steenbergen.