Will 2025 be the year that AI really takes root across the FS sector? According to a new IBM report on the global banking and finance markets, only 8% of banks were systematically developing generative AI in 2024. This number is set to soar in 2025 as businesses move from pilot stages to scale and, in some cases, execution.
This will undoubtedly result in productivity and efficiency gains but there will also be a number of challenges that FS organisations will have to overcome. Moving too quickly creates a risk of unintentionally bloating your operating expense with too many tools, while excessive time to market risks spiralling implementation costs.
For many tech teams, being able to quickly prove ROI and enabling Chief Technology Officers to align technology investments more closely with business strategy will be the first priority to getting their AI projects to scale. Measuring this value takes a disciplined approach that factors in a holistic view on costs and resources used to deliver results. Let’s dive into these challenges and possible solutions.
CTOs Must Bridge the Gap Between Technology and Business Strategy
With many banks, insurance organisations and investment companies looking to implement AI across many areas of the business, the modern CTO is expected to be as much a business leader as a technical expert: in charge of creating roadmaps, identifying how AI can enhance existing strategies and better the customer journey. Indeed, in many banks the CTOs are based within the business units themselves and plan new AI initiatives that IT will take over the management or stewardship of later in the lifecycle.
Commonly however, they must contend with the priorities of other c-suites whose objectives can be too hyper focused on cost-cutting. Companies are asking, “are we spending a normal amount?” rather than evaluating AI investments from a strategic perspective and this runs the risk of overlooking the value AI can bring.
This means that CTOs will have to find clear ways to justify new investments and communicate the importance of projects to avoid losing out on tangible benefits – the more robust insights into what’s working and why, the better.
The Growing Challenge of Proving ROI
Even though FS organisations are becoming more familiar with AI, as discussed, many still grapple with balancing costs and value. This can partly be because tracking progress can be more difficult in this sector. Data silos for example, can be a barrier to the effectiveness of AI solutions and delay frequent monitoring.
Addressing these concerns is likely to become more challenging too with recent data from Apptio indicating that 91% of UK businesses anticipate larger tech budgets this year and that AI costs are likely to even surpass these funds. This means that there will be more to manage and track. A problem not helped by the fact that many AI initiatives start with business-based CTOs, so the cost is not seen initially as an IT or technology cost, until IT take over these systems.
As we continue to add to the complexities there is little time to wait around and organisations should be looking at implementing processes to ensure better visibility. One way to accomplish this is through a robust technology business management strategy which can introduce standardised processes and tools for capturing, organising and analysing financial, operational and business data. This can help demonstrate the value delivered by services, by using unit costs to compare against legacy services. This approach shows the added value by AI and other newer technologies. Additionally, this can also reveal when an organisation should be reducing run cost from services that are no longer delivering as much business value.
Legacy Technology: A Roadblock to AI Adoption
Another hurdle to AI-driven transformation in the FS sector is the continued reliance on legacy technology. Despite the push for digitalisation, many still depend on older systems due to their reliability and deep integration into critical banking functions. Banks often just layer new tech over old solutions, which can create a complex IT ecosystem and slow down innovation. The value of both the newer and older solutions needs to be monitored and the lifecycle and retirement of unnecessary solutions should be planned and implemented.
Some organisations are maintaining legacy technology as it continues to deliver business value at a cost that can’t be replicated by new innovations. AI can even help maintain these solutions and make them even more efficient to run.
This method can pose challenges in terms of scalability, cost-efficiency and integration with newer technologies. The reluctance to replace them stems from high migration costs, risk aversion and regulatory concerns. While valid, without addressing these outdated infrastructures, organisations risk limiting AI’s full potential. A strategic approach, such as consolidating IT architecture and leveraging containerisation, can help FS firms modernise incrementally while maintaining service stability.
A Path Forward
To navigate these challenges, FS organisations must take a more structured approach to AI adoption. This involves:
- Enhancing Cost Visibility: Businesses should be looking for solutions and processes which can make it easier to track AI’s impact, ensuring spending aligns with business goals.
- Harmonisation: While legacy technology remains crucial, strategic consolidation and migration can free up resources for AI-driven initiatives. Organisations that can identify unnecessary costs can redirect these savings into funding more innovation.
- Focusing on Business Value: Instead of viewing AI purely as a cost-saving tool, CTOs should champion its potential to drive efficiency, improve decision-making, open new revenue streams, and enhance customer experiences. They should also track the actual delivery of this value with business data.
What Next?
As AI continues to reshape the FS industry, the most successful organisations will be those that strike the right balance between innovation and financial discipline. AI offers a wealth of opportunities for financial services firms, but its integration must be managed carefully. With legacy technology still entrenched in the sector and ROI challenges becoming more pronounced, business leaders must lead the charge in ensuring that AI investments deliver tangible value. This requires a shift in mindset – from focusing solely on cost-cutting to embracing AI as a long-term enabler of business growth.
By leveraging structured frameworks, improving visibility into AI’s impact and strategically modernising IT infrastructure, FS organisations can position themselves for a future where AI-driven innovation translates into real business benefits.
