A guest editorial by Claire Nouet, chief operating officer and co-founder of Pathway
AI has now become a recognised ingredient for efficiency in the financial services sector. Tech UK reports that 75% of institutions are already using AI, with a further 10% planning to implement it in the next three years. However, the reality is that not all businesses are truly ready to benefit from AI.
There can be a tendency for businesses in the sector to restrict their focus to simply improving data practices while preparing for AI implementation. However, this data-savviness alone does not translate to becoming truly ready to deploy AI at a wide scale. When businesses assume that being “data ready” is the final stage in their AI adoption –rather than a stepping stone – they set themselves up for challenges later down the line.
AI technology has its strengths and limitations and a comprehensive understanding of this denotes a business that is ready to harness AI’s full potential. I call this being “AI-savvy”. To reach this point, leaders at financial services firms have to be critical in their assessment of tools and solutions and also foster cultures of learning and experimentation throughout the entire workforce.
The data foundation for AI-savviness
There is a clear distinction between “data savvy” and “AI-savvy” financial service organisations. Data-savvy organisations may be data-literate but not ready for AI, whereas AI-savvy organisations always have strong data management and governance practices. Before financial institutions pursue AI transformation, they must implement robust data principles that ensure high-quality data is being used securely to enable accurate and reliable AI outputs.
Streamlining data practices is especially critical for organisations which need to ensure that the outputs of their AI models consider the latest information. To support this, transitioning towards live data architecture to support more sophisticated models capable of real-time thinking and continuous learning must be considered. Even if financial services companies are not using these kinds of models right now, all AI applications are likely to have a real-time element in the future. Data management must therefore be planned with this in mind to avoid being forced to replatform down the line.
The importance of a balanced AI perspective
The AI hype has exploded. As more products and solutions emerge, organisations can fall into the trap of thinking they need the newest AI advancements without considering how it aligns with operational needs. There is a very fine line between visionary adoption of AI tools to enhance operations and choosing the wrong tools and becoming quickly disillusioned. The rapid rise of AI has left many organisations feeling under pressure to adopt new tools without necessarily knowing if the reality of AI’s capabilities fulfils what organisations want AI to achieve. At times, true AI-savviness lies in having an understanding of when AI is not the right tool to serve organisational needs.
When AI is determined as the answer, leaders face the decision of whether to buy or build. Bespoke systems give organisations more control over their models, but come with a price tag which can be prohibitive. A truly AI-savvy business understands when investments into tailored capabilities are necessary and when an off-the-shelf product will return better value.
Adopting an AI-centric culture
To unlock the full benefits of AI, organisations require cultural and leadership frameworks that encourage innovative AI adoption throughout the whole workforce. AI adoption flourishes when employees feel empowered to use AI in their day-to-day work, and this must be led by example from the C-suite.
Today’s forward-thinking organisations need forward-thinking leaders who are proactive and decisive in their decision-making. Making incorrect decisions used to be the biggest risk for executives, but now passivity is as big of a threat to those in leadership positions.
When leadership is excited and bold with AI decisions, employee buy-in is more attainable. Without the workforce’s support, even the most advanced AI tools risk obsolescence. To achieve and retain this buy-in, it is crucial that the tools provided to employees are always fed with accurate and up-to-date information. When so-called intelligent systems produce inaccurate information, cynicism and mistrust grow within workforces, leading to disenchantment. Systems that are built in a way that prioritise real-time, accurate data will be more widely used, boosting productivity and overall trust in AI.
Education to boost AI confidence
AI investments that aren’t bolstered by comprehensive education programmes are unlikely to create the desired return for financial services organisations. No matter how advanced an AI tool is, employees need to know how to use it for maximum impact. This requires continuous training, alongside up-to-date information. When employees feel confident in their ability to use AI tools, they can experiment in an informed way to define what AI-savviness uniquely means to them in their specific job role
Approaching AI savviness
Being data-savvy is just the beginning. Today, organisations must strive to become AI-savvy to succeed in the next wave of technological innovation.
At the foundation, being AI-savvy is about avoiding the temptation of the next big technology hype and instead promoting a culture that encourages learning, tactical procurement, and considered implementation. This means blending strategic foresight, robust data practices and a pro-AI environment that fosters creativity and innovation.
Image: Pathway
Guest Editorial
This article was produced specially for Fintech Intel by an expert guest contributor.
