For many financial institutions, artificial intelligence (AI) has become a strategic tool. From fraud detection to risk assessment and customer experience. AI has the potential to re-mould financial services as we know it, streamlining internal processes and unlocking greater resources.
However, despite 61% of executives naming digital transformation as a top priority, according to McKinsey, 70% of projects still fail to meet their objectives. A common misconception is that this is due to technological limitations. In reality, the problem is that many C-suites tend not to be fully aligned with the goals of the digital transformation programme.
Without a clear integration of AI with overarching business goals, businesses risk expensive projects that underdeliver and create scepticism around AI’s true value. Successful digital transformation begins with a shift in mindset to view AI not as a stand-alone technology, but as an enabler of core business objectives.
Why digital transformation and AI in financial services fall short
Back in 2018, Lloyds Banking Group announced its ambitious transformation plan to invest £3 billion to improve its digital capabilities. This commitment reflects a wider industry trend, where financial institutions are allocating significant resources to digital transformation initiatives. But, despite a sentiment shift in favour of AI in financial services, many initiatives are failing to deliver results.
Too often, financial firms rush to implement AI without a clear road map. In fact, just 40% of regional and global banks have a well-defined AI strategy in place. With billions being invested across the sector, the success of these projects is crucial and these firms cannot afford to see such huge investments fail to deliver tangible results.
Remaining at the forefront of AI adoption is no easy task. Many businesses make the mistake of committing to inflexible technologies or outsourcing crucial capabilities, risking a loss of control over their intellectual property which can create even bigger problems.
Alongside this, what’s missing from so many AI road maps is a strategy for stakeholder engagement. Typically, digital transformation projects are driven by IT departments but without securing solid buy-in from leadership, compliance teams and front-line staff. This disconnect can potentially lead to resistance or even the abandonment of the project altogether.
Additionally, failing to define clear success metrics is also a common issue we see, making it tricky to track performance beyond general efficiency gains. Without measurable outcomes that are tied to financial or operational KPIs, AI risks becoming an expensive, directionless investment.
Solidifying stakeholder engagement for successful digital transformation
For AI projects to succeed in financial services, they must align with both leadership goals and operational needs. Educating executives on the role of AI beyond simple automation is key and this starts with translating technical concepts into language that resonates in the boardroom. With 74% of CEOs believing that gaps in their knowledge hinder their ability to ask critical questions, bridging this divide through targeted workshops, clear communication and knowledge sharing can empower leadership to drive AI adoption with confidence.
It is also a good idea to involve a dedicated team member in each area of the business throughout a programme. This is because digital transformation often spans over several years. These dedicated members can help maintain momentum throughout this period, preventing disengagement in each of their departments and ensuring any outcomes align with their department’s needs.
By taking a collaborative approach to digital transformation, the project can remain responsive to the evolving priorities of the business and leadership can remain well informed throughout the process.
Staying in the AI driving seat
Long-term success for AI programmes is centred on maintaining flexibility in technology adoption. Businesses should avoid locking themselves into specific tools or platforms that may become obsolete, instead ensuring they have the agility to adopt emerging innovations without excessive costs or operational disruptions.
At the same time, organisations must retain control over their intellectual property (IP) by embedding AI within bespoke business processes rather than outsourcing core competencies. AI should enhance what makes a business unique – not replace it.
Creating actionable outcomes by measuring success
To help ensure that AI initiatives deliver on their promises, financial institutions need clear definable metrics. Cost savings, fraud prevention and improved customer service are some of the most impactful KPIs for AI projects in financial services. For example, AI’s rapid capabilities can detect fraud much quicker than traditional or manual systems. Solutions such as these, that improve customer experience or automate operational processes, often translate into higher customer satisfaction rates and lower operational costs.
But the key to measuring AI success isn’t just about tracking metrics it’s ensuring that leadership understands how AI contributes to the bottom line. It is essential for digital transformation leads not only to present technical results, but frame them in terms of business value, such as cost reductions, increased revenue and resource savings.
Bridging the gap for long-term success
The financial services sector stands at a critical crossroads where AI and digital transformation are no longer optional as they play an instrumental part in remaining competitive. However, without a clear strategy, stakeholder engagement, and measurable success metrics, even the most ambitious AI initiatives risk failure.
To truly harness AI’s potential, financial institutions must align AI programmes with business objectives, educate leadership, and foster a culture of collaboration across departments. By doing so, organisations can ensure that AI is not just a technological investment but a driver of efficiency, innovation and sustainable business growth.
With digital transformation’s rapid acceleration, with an expected compound annual growth rate (CAGR) of 23.6% from 2022 to 2030, those who don’t want to get left behind must be ready to adapt. AI programmes are no longer a ‘nice to have’, they are a necessity if organisations want to remain innovative in a competitive market.
