Whether it’s payments, banking or treasury, everyone can list a dozen AI use cases they were pitched or watched a demo of. Top-down AI mandates produced impressive and convincing presentations that have attracted billions of dollars in financial support.
European PE and VC firms invested $6.8 billion into AI companies in 2025, up 83% year-on-year, according to S&P. Enthusiasm remains high; 141 deals worth $2.8 billion were closed between January and May 2026.
But identifying a problem on paper or imagining creative solutions was never the challenge. That was the easy part. The hard work has been turning the exciting demo into something teams can trust and use daily.
Has that problem been solved? It’s a mixed story, but I believe there are some underlying factors that can be transformative for AI adoption – if they are addressed early on.
Optimism vs reality
The financial services industry highlights the gap between the enthusiasm about AI and the ability to effectively implement and use it. 79% of banking executives believe Generative AI offers a transformational opportunity, according to Personetics. However, only 18% have fully integrated it into day-to-day operations.
We see the same situation in payments. The Payments Association found that 99% of organisations use AI, but only one in five have modernised their data systems to fully power AI innovation.
All of these findings point to one common theme: when it comes to AI implementation and execution, the gap between ambition and reality remains stark. The problem isn’t interest or intention; it’s internal culture and legacy architecture.
AI in practice – capturing the benefits
There’s no question that AI is a fundamental shift in how we work. But anxiety about AI runs through virtually every industry. Worries range from fears about job loss to concerns about operational disruptions and a long wait for ROI.
Yet these concerns are often no longer based in reality. The Financial Services Skills Commission reports that while almost every role in financial services is likely to be changed by AI, only around 1.5% of workers may require ‘expert’ AI skills. The vast majority would likely need only foundational digital literacy and the ability to work alongside AI tools.
That should be welcome news, and it will make the world of work more efficient. For example, today’s graduates don’t want to work in organisations where “modern finance” means reconciling 15 different CSVs or hours of mundane database analysis. They expect to use AI in their work, just as they see it in daily life, to work better and smarter.
Consider an AI-powered assistant that’s been trained with 10 million-plus transactions. The system doesn’t wait for the month-end to reconcile accounts. It continuously processes bank transactions, identifies patterns in payment behaviour, automatically categorises entries, and flags discrepancies in real time. In this case, AI can dramatically reduce the time and errors associated with high-frequency, high-pain tasks.
That frees up staff for more important and nuanced work. And for companies managing multiple entities, currencies, and banking relationships, this can also help measurably shift them towards a more proactive risk management approach.
And while implementation timelines vary significantly, modern, API-connected platforms can compress the schedule dramatically. Some organisations report measurable impact relatively quickly.
Back to reality
However, bridging that gap between ambition and implementation requires the AI knowledge to distinguish hype from scalable operations. Yet according to Gartner, only 21% of C-suite executives are truly AI-savvy. CFO Connect found that 68% of CFOs say they’ve been slow to adopt because they don’t know where to start. Having the right expertise at board level will make successful AI implementation a company-wide priority.
My advice? First, identify the business problem. AI delivers value only when it’s tightly scoped, with defined users, datasets, and measurable outcomes. AI adoption shouldn’t be a cosmetic upgrade atop legacy systems.
Second, leaders must look at both time-to-value and strategic impact to measure the ROI of advanced AI. As mentioned above, unlike legacy systems, AI-native solutions can – or even must – show measurable results rapidly. The key is determining contained value: specific, auditable use cases where you know what AI will do, who it serves, and how success will be measured.
Third, don’t make AI governance and security a separate workstream. It must be baked into any vendor or project evaluation, complete with assessments of:
- Key standards and certifications, including general security, AI governance and data encryption
- Data usage and third-party model training, complete with written commitments on data ownership and usage rights, third-party access and sub-processor arrangements, model training practices and data retention policies, and geographic data residency and cross-border transfer mechanisms
- Meaningful oversight of AI-generated recommendations
- A clear, auditable trail with context
What comes next
Two years ago, AI was a conference buzzword. Today, it is increasingly viewed as a competitive necessity.
AI front-runners aren’t waiting for perfect clarity or comprehensive frameworks. They’re adopting an enterprise-wide strategy that’s centred on a top-down program. They’re starting with focused use cases: key workflows or business processes where the payoff from AI can be substantial. And they’re embedding governance from day one and building organisational muscle around AI-augmented work.
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.
Business leaders who translate their AI vision from defined pilots to auditable outcomes and measurable ROI will redefine how their organisations create value, transforming AI from an experimental cost centre into a strategic engine of insight, productivity, and growth.
