AI dreams, data nightmares: The finance sector’s tech data dilemma

By Kuljit Bawa, Managing Director, EMEIA at ActiveOps

The majority of UK financial services operations leaders are still in the early stages of their AI journeys. There’s no doubt a lot of work still to be done, starting with the quality and availability of data.

While AI does have the potential to transform the industry, recent research has shown that at present, businesses are being held back by a reliance on data that is days, weeks or even months out of date. Many businesses face an uphill battle to meet their priorities in 2024, and the market won’t wait for them to catch up.

Organisations that can adapt and flex to suit new technology will be the ones that dominate. Data holds the key to not only automating processes but to supporting human decision making too – via predictive and prescriptive insight in real-time.

‘Decision paralysis’

UK businesses are currently facing a phenomenon known as ‘decision paralysis’, a feeling akin to driving without a sat nav, perhaps commonplace in previous decades, but unthinkable if you’re going to beat traffic in this day and age.

Leaders are being held back by either having too much data, a lack of trust in their data or simply not having the tools at hand to glean the right insights, it’s clear that many have been trying to run before they can walk when it comes to AI.

Up until now, many firms will have invested heavily in automating some of their existing processes to support the push towards AI, but they fail to address the fundamental data problem first. Working in an environment of spreadsheets, data siloes and decision making by hunch is not tenable, and operations teams can’t achieve their goals if these challenges and barriers remain.

Instead, work needs to start fast to review the data being collected, where it is coming from and how quickly they can access it to analyse what is happening in the businesses. All of these questions are important starting points and will have a direct impact on the trust that leaders and their teams are able to place in their data.

Basing critical decisions on insights that are incomplete, inaccurate and weeks old puts operations on the back foot. Ultimately, the impact of this misplaced investment is being felt most by customers through continued high costs, slow delivery and poor customer engagement.

Tackling your data problems

In today’s world, where customer demands are so high, speed of decision making to drive productivity and efficiencies is a competitive advantage. Leading to faster turnaround times, reducing overtime, limited staff burnout and avoiding costly SLA breaches.

Despite the challenges, operations leaders are largely recognising the potential power of data if it was accessible, relevant and available in real-time. The key to this is AI and financial services companies will need to prepare themselves to embrace this technology so they can realise their potential through decisive decision-making.

The first step in tackling your data problems is recognition and acceptance that what you have available to you isn’t working in your best interests. For the financial services industry in the UK, acceptance can be the difference between data drudgery and a leaner, transformed organisation.

Next is the need to identify and expose where the most pressing data challenges exist and map out a strategy to address them. The key to a successful data strategy is to agree metrics and terms from the outset to serve as benchmarks, with the ultimate goal being a point where data is no longer siloed.

Once the groundwork is complete, the full potential of AI and real-time data can be unleashed, transforming operations leaders and their businesses from a state of disrepair and into well-oiled machines.

However, within most industries, particularly financial services, time is short and valuable and those that don’t act fast will quickly fall behind. With AI journeys gathering pace, it’s critical that leaders get their data in order before it’s too late.

Unleashing AI

In the long run, AI will uncover deeper, more meaningful insights and improve multiple aspects of business. It can also play a crucial role in supporting businesses to identify and collect the right data.

But those that take steps to uncover and address their data challenges, ensuring it is relevant and real-time will ultimately see better reward.

To find out more, read the full whitepaper here.

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