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Navigating the challenges of AI adoption
But despite the enormous potential of AI in finance, its adoption is not without challenges.
One of the primary concerns is around ethical and responsible AI use. As Pradeep points out: “BFSI firms must establish guardrails to mitigate biases and ensure transparency in AI-driven outcomes”: particularly important in an industry where decisions can have significant financial and personal impacts on customers.
Security and privacy represent another major challenge. The BFSI sector deals with highly sensitive financial and personal data, making data protection particularly critical. “It is important that solutions preserve the data protection that is already built into the underpinning information management systems,” Lars comments. “This is one of the reasons we have the mantra of bringing the AI to the data and not the other way around. That way you do not lose control.”
Unnikrishnan, meanwhile, highlights the need for specific controls to secure customers’ personally identifiable information (PII) based on geography-specific regulatory mandates. He also emphasises the importance of capabilities such as data traceability, real-time threat detection and response including preventing prompt injection attacks, role-based access controls, and appropriate guardrails throughout the value chain, in addition to the existing traditional security controls.
Beyond these technical challenges, BFSI firms also face organisational hurdles, from a lack of IT readiness and the need for talent development and training to cultural shifts required within organisations to fully embrace AI. As Pradeep notes: “Scaling adoption and consistently leveraging the capabilities to build enterprise-grade solutions will mandate changes to IT infrastructure to ensure readiness.”
The human element in an AI-driven future
As AI capabilities continue to advance, much has been said about the future role of human workers in the BFSI sector. However, as the executives highlight, AI is set to augment, rather than replace, human capabilities.
Unnikrishnan envisions a shift in the nature of work: “In the short term, firms will need to equip employees with LLM-specific techniques like prompt engineering, advanced retrieval-augmented generation (RAG) approaches, and contextual fine-tuning. In the longer term, people will fundamentally shift from doing work to training intelligent machines and reviewing the work done by machines. This will ultimately free up time for higher value-adding activities that demand creativity, empathy and critical thinking, which will create new roles and opportunities.”
This shift towards a hybrid workforce – where humans and machines constantly improve each other – will require continuous retraining and upskilling. “Upskilling in data science is key,” Lars asserts. “Gen AI lives on data, so you need people who understand what data you have and what quality it has. This is a business problem, not an IT problem, so you need to upskill the business as much as IT.”
Pradeep reinforces this point, citing findings from a recent TCS study: “Even in this era of AI, there’s no substitute for human creativity, empathy and strategic thinking. Our study found that 63% of BFSI companies think human creativity and strategic thinking will remain essential to their competitive advantage.”
The path to AI implementation
For BFSI firms looking to implement AI solutions, the executives offer several key pieces of advice. First and foremost, they emphasise the importance of getting started. As Lars puts it: “The technology is there and your competition will use it, so dive in.”
However, diving in does not mean going it alone. Both Lars and Pradeep stress the importance of finding the right partners. “I would strongly suggest to both get a technology partner like ourselves and an implementation partner,” Lars says. “Firms should look for a partner that understands their business needs and has a broad range of experience in their chosen project,” Pradeep adds.
When it comes to choosing specific AI projects, Lars advises taking a strategic approach. “Brainstorm on where you think AI can help and get a quick validation with some experts,” he suggests, cautioning against expecting AI to magically solve longstanding business problems: “If you have a business problem you are not good at solving, then Gen AI is not going to magically solve it.”
The future of AI in BFSI
Looking ahead, AI is set to become an increasingly integral part of the BFSI landscape. “Given AI’s potential to add complementary value, it will become mainstream in the BFSI industry,” Unnikrishnan predicts. “Going forward, BFSI firms will increasingly leverage composite AI technologies – both predictive and Gen AI – for disruptive transformation.”
This mainstreaming of AI will involve a shift in how the technology is used. As Unnikrishnan explains: “This will entail a shift in how AI is used – from leveraging it tactically to design point solutions for specific use cases to utilising it to drive knowledge-driven decisions and innovation. This, in turn, will reimagine entire value chains and transform the way BFSI firms do business.”
Pradeep adds that future AI implementations will increasingly focus on innovation and revenue growth. “In our recent survey, we found that as many as 88% of BFSI pace setter companies – those that enjoy greater financial success than the others – are more focused on using AI to spur innovation,” he notes.
However, at a time of widespread AI hype, Lars injects a note of realism. “Right now, everyone wants to have an AI strategy, but we are also reaching the point of disillusion; it is not as easy or as magical as it looked like 12 months ago,” he says. “I expect that a lot of realism will happen over the next 18 months. There are already a good set of practical solutions you can buy and implement, but the real job of understanding your data and cleaning your data will lead to a lot of new insight and hopefully cool solutions that create value for the industry.”
By combining OpenText’s advanced information management and AI solutions with TCS’s deep industry knowledge and implementation expertise, the two companies are well-positioned to help financial institutions navigate the complexities of AI adoption and unlock its transformative potential.
The road ahead will present challenges, from ensuring ethical AI use to managing cultural change within organisations. But for those BFSI firms willing to embrace AI and partner with experienced guides like OpenText and TCS, the rewards could be substantial. As Unnikrishnan concludes: “This will reimagine entire value chains and transform the way BFSI firms do business.”
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