By Ben Furnival, Digital Operations Director at global marketing services firm APS Group
Financial services has embraced AI with remarkable speed. Banks, insurers and investment firms have invested heavily in pilots and proof-of-concepts, many of which have demonstrated genuine gains in efficiency, decision-making and customer experience. Yet as AI adoption matures, attention is increasingly turning to how organisations embed these technologies into everyday operations in a way that delivers sustainable value.
Many pilots have generated promising results, but the next challenge is turning those isolated successes into repeatable business-as-usual operations. Many organisations are still finding it difficult to embed AI into everyday processes in a way that is scalable, governed and capable of delivering long-term value.
One of the main reasons behind this challenge is that organisations are often trying to fit AI into existing ways of working, rather than redesigning those ways of working around AI. New tools are layered onto legacy processes with the expectation that transformation will naturally follow, but technology alone rarely changes how an organisation operates. Existing workflows need to evolve too and, where they don’t, organisations may find it more difficult to realise the full value of their investment.
Financial services presents a particular challenge because AI rarely sits within a single function. Whether supporting customer communications, onboarding, complaints handling or regulatory reporting, AI’s home is within operational processes that involve multiple teams and governance requirements. Improving one stage of that journey without considering the wider operating model can simply move inefficiencies elsewhere, limiting the overall value AI is able to deliver.
This is where many AI projects can lose momentum. Responsibility often spans technology, operations, customer experience, data, risk and compliance, each with its own priorities, budgets and measures of success. This complex ecosystem is why organisations need to have a shared vision to show how AI can support every single team – often led by a committee representing the various operational stakeholders.
Organisations are faced with a growing choice of platforms, models and specialist solutions, making it tempting to focus on selecting the right technology before addressing the operational foundations needed to support it. In reality, technology itself is rarely the limiting factor. Greater value comes from understanding how AI fits within existing business processes, where responsibility sits, how data flows across the organisation and what success should ultimately look like.
Governance also plays a critical role, yet it is often viewed as something that slows innovation. In highly regulated sectors such as financial services, the opposite is usually true. Strong governance gives organisations the confidence to adopt AI more widely because it provides clear accountability, transparency and oversight from the outset. As regulation such as the EU AI Act raises expectations around transparency and accountability, those foundations become even more important. For financial services organisations, AI is becoming part of the operational and regulatory environment, rather than a standalone innovation initiative. Rather than creating barriers, effective governance establishes the conditions that allow innovation to scale safely and consistently across the business.
This becomes increasingly important as AI moves beyond supporting internal productivity and begins influencing customer-facing processes and business-critical decisions. Organisations need confidence that AI is operating within clearly defined parameters, supported by appropriate testing and human oversight. Without those foundations, even the most promising use cases can struggle to gain the trust needed for wider adoption.
The organisations seeing the greatest return from AI are those investing as much in operational readiness as the technology itself. They are the ones investing equal attention in operational readiness, bringing together people, processes, technology and data before expecting AI to transform outcomes. That may involve redesigning workflows, improving governance, strengthening collaboration between teams or addressing longstanding process inefficiencies that AI has simply brought into sharper focus.
The conversation around AI in financial services is beginning to mature. Attention is increasingly turning to how organisations embed AI into day-to-day operations in a way that is scalable, governed and capable of delivering lasting value.
Ultimately, the organisations that see the greatest value from AI will be those that view it not simply as a technology investment, but as an opportunity to evolve how their operations work.