We’re entering a new era where hardware matters again. In the 1990s, the competitive advantage came from CPUs, storage and networking. In the 2000s and 2010s, software such as SaaS, cloud, APIs and mobile took centre stage. Now, AI is pulling the industry back towards hardware, with countries racing to secure GPUs, treating them as strategic assets.
Supply chain resilience is rising to board-level importance. Governments are pouring billions into this shift: $52bn (£38bn) through the US Chips Act, $47bn (£35bn) through the EU Chips Act and more than $140bn (£104bn) in semiconductor subsidies in China. Training and running AI models at scale consumes extraordinary power and cooling, and data centre expansion is hitting the limits of regulation, energy supply and physical space.
Companies that can optimise AI use based on cost-aware scheduling will outpace those relying solely on processing capacity. With manufacturing bottlenecks persisting, prioritising practical, scalable solutions over hype will be essential. Success with AI isn’t just about having the biggest machines; it’s about using them intelligently, reducing unnecessary outlay.
Even the most advanced AI models will be constrained by the systems feeding them, making architecture, connectivity and integrated workflows the critical factors that determine which companies achieve lasting transformation and the competitive advantage.
2026 will be a crucial year for embedding AI into business practices. As the technology matures next year, companies and policymakers will not only need to focus on how AI can improve processes, but also on the safety of its application.
Research suggests that security and privacy are the most pressing ethical issues around the use of AI. As business increase their use of AI, they will be more exposed to cybersecurity threats such as hacking and identity theft.
What’s more, some AI deepfakes are nearly impossible to spot. Anthropic, the makers of Claude, admitted earlier this year that its tools were being used by hackers to commit large-scale theft of personal information. AI is also being used to write code that has the potential to hack into large organisations, including government entities. We’ve seen prominent retailers including M&S and The Co-op targeted by cybercriminals this year. Clearly, even the largest firms must prepare their defences.
For UK firms, the average cost of the most disruptive cyber incident in the 12 months to June 2025 was £8,260, according to data from the Department for Science, Innovation and Technology. With businesses more exposed financially than ever thanks to rising business costs, safeguarding digital systems has never been more important to maintain competitiveness and financial resilience.
In the next year, the CIO’s focus will shift from information technology to enterprise technology. Traditional metrics such as ticket counts will still matter, but forward-looking CIOs will adopt a solutions mindset.
Modern CIOs must leverage AI not just to source tools but to engineer outcomes. Instead of recommending SaaS vendors, CIOs will assemble multiple LLMs to build solutions to solve today’s problems while also anticipating what’s next.
The IT function will no longer be about infrastructure alone. It will be expected to deliver corporate intelligence with AI-driven solutions and provide leverage across critical business platforms. AI will redefine the CIO as a business innovator, not just a technology operator
In 2026, CIOs will become the organisation’s primary sustainability steward and they will be expected to own responsibility for tech-driven sustainability. As enterprises face mounting pressure from regulators, investors and customers to meet climate goals, CIOs will be expected to deliver the data, platforms and AI-driven insights that make sustainability measurable and actionable. From optimising cloud workloads for lower energy use to applying advanced analytics that cut supply chain emissions, CIOs will increasingly be at the centre of corporate sustainability strategies.
This isn’t just about compliance reporting – it’s about leveraging technology to transform sustainability into a source of efficiency and growth.
When I think about the past year, what stands out most is how quickly AI has moved from hype to something everyone in financial services is actually using. A year ago most teams were still experimenting. Now we’re seeing real deployment in underwriting, fraud, customer support and even portfolio tools. It’s no surprise that analysts are estimating over $1tn (£750bn) of annual value from AI in banking alone. The productivity gains are finally showing up in day-to-day operations.
Quantum is also starting to creep into boardroom conversations. It’s still early, but the fact that regulators have started pushing for post-quantum security standards suggests that the timeline is shifting. Financial institutions are running small pilots in areas such as portfolio simulation and risk modelling, mostly to make sure they aren’t caught flat-footed when the tech matures.
One of the most interesting shifts this year has been the way stablecoins have stepped into the mainstream. Klarna launching its own token was a real signal that this is no longer a crypto side-story. The appeal is pretty simple: instant settlements, cheaper payments and quick and easy transfers. Transaction volumes for stablecoins passed $2tn (£1.5tn) recently, and with big consumer apps now adopting them, I think we’re just at the start.
We’re entering a new era where hardware matters again. In the 1990s, the competitive advantage came from CPUs, storage and networking. In the 2000s and 2010s, software such as SaaS, cloud, APIs and mobile took centre stage. Now, AI is pulling the industry back towards hardware, with countries racing to secure GPUs, treating them as strategic assets.
Supply chain resilience is rising to board-level importance. Governments are pouring billions into this shift: $52bn (£38bn) through the US Chips Act, $47bn (£35bn) through the EU Chips Act and more than $140bn (£104bn) in semiconductor subsidies in China. Training and running AI models at scale consumes extraordinary power and cooling, and data centre expansion is hitting the limits of regulation, energy supply and physical space.
Companies that can optimise AI use based on cost-aware scheduling will outpace those relying solely on processing capacity. With manufacturing bottlenecks persisting, prioritising practical, scalable solutions over hype will be essential. Success with AI isn’t just about having the biggest machines; it’s about using them intelligently, reducing unnecessary outlay.
