Claire Nouet, COO and Co-founder, Pathway
Artificial intelligence’s (AI) progress has made its mark in the financial services sector. Recent research has found that the majority of financial services firms (75%) are currently using AI, and an additional 10% are expecting to use it over the next three years.
Yet in reality, not all business leaders are ready to unlock the benefits of AI and its transformation capabilities. Often during AI adoption, businesses tend to take a narrow approach by only focusing on improving data practices.
However, ‘data-savviness’ is merely a component of what it means to implement AI with maximum impact. Businesses that believe the final stage of preparing for AI adoption is to become data-ready are jeopardising their chances of success.
A comprehensive understanding of AI, especially regarding its limitations and its true abilities, is what separates the businesses that can harness the full potential of AI from the ones that are not ready yet. I define this as being ‘AI-savvy’.
For financial services organisations to reach this level, leaders must critically assess tools and solutions, promote a culture of continuous learning and enable experimentation throughout the entire workforce.
The steps towards AI-savviness
Within financial service organisations, there is a clear distinction between those that are data-savvy and those that are AI-savvy. To leverage AI within an organisation, CIOs and CDOs must ensure rigorous data frameworks are in place. These data practices must prioritise strict governance, accuracy and reliability.
Financial services organisations face a particular challenge in that many need to operate in real-time. This means having the capability to continuously learn and adapt and provide the most accurate outcomes. However, many AI applications are trained on batch data uploads, which results in information lags.
Even for those businesses that don’t require real-time AI applications today, in the future, they will become the standard. So, data leaders must start transitioning today to live data infrastructures that support models that can process data in real-time. This will critically avoid the need for replatforming applications in the future.
Recognising the point when AI is no longer effective
The pace of new AI solutions coming to market leaves some organisations feeling the pressure to adopt the latest AI advancements. However, an AI strategy built on “keeping up with the Joneses” risks prioritising innovation over their own operational needs. And where AI applications are brought in for the sake of innovation and not for the sake of improving workflows and the employee experience, it can lead to disappointment, disillusionment and tool abandonment. That’s why, at its core, true AI-savviness requires discernment and an understanding of the most suitable tool for organisational needs.
Organisations should also carefully evaluate whether to build or buy AI applications. While many firms may lean towards bespoke systems, with hopes of more control over business needs, it comes at a high cost to both build and maintain. Being AI-savvy is also the ability to recognise when specialised capabilities are essential or when off-the-shelf products can deliver similar results at better value – and without putting additional strain on the business.
Incorporating AI throughout the workforce
AI innovation within organisations typically stems from a top-down approach, strengthened by cultural and leadership frameworks to encourage adoption among employees. Successful adoption thrives when employees feel confident to use AI in their day-to-day work and when they see leadership do the same.
Progressive organisations need progressive leaders, specifically leaders who can make proactive and decisive decisions. In the past, the biggest threat to C-suite was making incorrect decisions, but now passiveness is an even bigger threat.
When leaders show how AI provides value in their organisation and how the technology can be trusted, it leads to a shared consensus between executives and employees. However, to attain employee buy-in, employees must be provided with tools that are regularly maintained with up-to-date information. Inaccurate information can have a detrimental impact on employees as distrust and scepticism can lead to disenchantment. However, systems that are built with accurate and real-time data improve employees’ trust in AI.
Upskilling to prevent mistrust
In financial services, AI investments that are not reinforced by comprehensive education programmes may lead to losing out on the return on investment. The reality is that a shared understanding of AI tools creates a bigger impact than how advanced an AI tool is. For this to happen, employees need to have access to the latest information and training on an ongoing basis. This allows employees to develop their confidence in AI and redefine what AI-savviness means to their role.
Adopting AI-savviness
AI-savviness is the first step in the AI adoption cycle. Now, firms must strive to become AI-savvy to be better positioned to navigate the next wave of technological innovation.
At the core of being AI-savvy, it’s about resisting the temptation to jump on the newest and most-hyped tools and focusing on cultivating a workforce environment that combines strategic foresight, robust and real-time data practices, and promotes an AI-centric culture that leads to innovation and creativity.
