2026: the Year of AI Execution
Published: November 20, 2025
If 2023 and 2024 were spent talking about AI in finance, 2025 has been the year of trying to make it real, and discovering just how hard that is. But 2026 will deliver a new approach. Theo Wasserberg, Head of UK & Ireland, Embat, explains.
Across the finance industry, the tension between excitement and exhaustion was palpable. Many leaders wanted to modernise. Many believed AI was the way forward. But when it came to execution, the gap between ambition and reality became obvious.
Most finance teams still found themselves trapped between legacy systems that couldn't deliver and point solutions that didn't talk to each other. The problem wasn't ambition; it was architecture. This infrastructure challenge helps explain why so many teams struggled to move from conversation to implementation.
When I speak to CFOs, there's a consistent undercurrent of caution. Many have endured transformation projects that dragged on for years, went over budget, and failed to deliver expected ROI. They hesitate. They tell themselves they're waiting for the market to mature or for regulation to stabilise. But underneath it all is fear: fear of failure, disruption, and another system overhaul that derails teams for 18 months.
It's understandable. But in 2025, the cost of inaction became clear. Finance leaders began realising that what they thought was ‘best practice’ was often just ‘current practice’.
Why traditional automation hit a ceiling
Understanding this hesitation requires looking at what finance teams have been working with. For 20 years, finance automation followed the same principle: if X, do Y. That approach created value – automating maybe 30% of manual work – but it also created a ceiling.
Real life rarely follows rules perfectly. A customer pays two invoices in one transaction. Someone mistypes a reference number. A file format changes. Suddenly, the system freezes, and a human must step in. The promise of efficiency evaporates in exception handling.
AI, by contrast, doesn't require every rule to be predefined. It understands intent. You tell it the desired outcome, and it figures out how to achieve it. We find that's the difference between 30% automation and 99%. It's also the difference between a system that merely saves time and one that transforms how finance operates.
Human cost of outdated systems
This technological limitation has consequences beyond operational efficiency. Modernisation isn't just about systems. It's about people.
The best talent no longer wants to work in organisations where ‘modern finance’ means reconciling CSVs across 15 spreadsheets. Graduates from the London School of Economics and London Business School and other leading institutions expect AI in their work, just as they see it in daily life. Legacy technology is no longer merely an operational cost, it's a talent risk.
Digital adoption is central to solving this challenge: 80% of CFOs expect digital tools to dominate operations by 2025, while 30% of finance tasks are fully automatable [Deloitte Survey, 2024; McKinsey Report, 2024]. By modernising tools and processes, companies can both attract top talent and unlock productivity gains.
Finance's strategic evolution
This urgency around modernisation reflects a broader shift in how finance is understood within organisations. The role of finance is evolving far beyond operations. Over the next five years, 69% of American middle-market CFOs surveyed expect greater emphasis on data analytics, 60% anticipate more scenario planning, and 55% say finance will become a more embedded strategic partner across the business [Cherry Bekaert CFO Survey, 2025].
In another revealing shift, HSBC found that 64% of global CFOs at large organisations surveyed now consider treasurers part of the C-suite, reflecting a mindset change: finance is no longer viewed purely as a cost centre, but as a catalyst for insight and strategic agility [HSBC Corporate Risk Management Survey, 2024].
This is not just theory – the emphasis on analytics, scenario modelling, and strategic partnership is already shaping investment in digital platforms and AI initiatives. Finance is moving from being transactional to being a strategic adviser to the business.
Treasury's renaissance
Nowhere is this strategic evolution more visible than in treasury. Treasury is emerging as a strategic nerve centre for liquidity, risk, and capital management. The KPMG 2025 Global Treasury Survey confirms that nearly 80% of companies surveyed operate predominantly centralised treasury functions, positioning treasury to guide financial decisions rather than merely processing transactions.
Real-time reporting and dashboarding are the top priorities for treasurers over the next 12-24 months, followed by real-time liquidity and real-time payments and collections [EACT Survey, 2025]. Similarly, 65% of organisations polled plan to expand API use to enable real-time integration across ERPs, TMS platforms, and banking networks [PwC, 2025 Global Treasury Survey].
This transformation in treasury's role points to a critical insight into how AI should be deployed.
Contained value: the path forward
One of the early mistakes in AI adoption was treating it as a cosmetic upgrade atop 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. Finance teams that succeeded in 2025 started small – automating reconciliation first, then forecasting, then cash visibility – building confidence one use case at a time.
Regulatory catalyst ahead
This measured, use-case-driven approach will soon feel a significant tailwind from regulatory changes. Treasury leaders are waiting for regulatory clarity. The upcoming PSD3 and ISO 20022 migrations are more than technical exercises; they are the foundation for the next era of financial infrastructure. Open banking data will become persistent and connectivity standardised, removing one of the biggest barriers to automation: fragile data flows. When that happens, a second AI surge is inevitable.
2026: the year of practical AI
In 2026, AI transformation won't be an abstract goal; it will be practical, embedded, auditable, and measurable. The winners will be companies that understand AI's purpose: removing friction from decision-making, freeing people from low-value work, and giving finance teams the space to think strategically.
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.
The takeaway for 2026 and beyond is straightforward: bold ambition must be paired with disciplined execution. Finance leaders who translate vision into defined pilots, auditable outcomes, and measurable ROI will not only surpass yesterday’s automation ceilings, they will redefine the very role of finance, transforming it from a cost centre into a strategic engine of insight, liquidity, and performance.
In short: 2025 tested ambition; 2026 will reward execution. The gap between those who dream and those who deliver is measurable, and it’s closing fast.