Generative AI is making significant strides across various sectors, particularly in enhancing productivity, efficiency, and customer experience. In this interview with e27, Dipen Mehta, APAC Head of Banking, Financial Services, and Insurance (BFSI), SoftServe, speaks about the common use cases of AI in Singapore today and the challenges that businesses face in adopting the technology and maximising its potential.

In Singapore and globally, businesses are leveraging this technology to streamline operations and improve service delivery. The first major application is increasing productivity by automating tasks and processes, allowing businesses to achieve more with less.

Secondly, AI drives efficiency by reducing customer service costs and optimising business processes. Lastly, AI enhances customer experiences by offering more personalised and responsive interactions.

“You may have heard this term in marketing, but this is used broadly in most industries around hyper-segmentation. Generative AI is one of the technologies that really allow us to do micro-segmentation [of our users and customers],” Mehta says.

“I can now say, here are the characteristics we know about our customers and what might drive a certain behaviour. So you can generate more than just customised text; you can also generate imagery, colour, and the whole template. You can evoke an emotion. This space has a lot of experimentation, but we are seeing that across industries.”

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Remaining challenges in AI adoption for businesses

However, adopting AI technology in businesses presents several challenges. The first of which is the rapid pace of technological advancement. As generative AI evolves quickly, keeping up with the necessary skills within an organisation becomes difficult.

“It is not because the technology is too hard for anyone to understand. But it is moving at a rapid pace; the pace of innovation in Generative AI is very, very fast. There are always new models coming out. The way you interact with the models is changing.”

The second challenge revolves around data readiness. While businesses can quickly develop a proof of concept using AI with their own data, moving beyond this stage to full production is challenging.

“We could work with the customer and get that done in days, showing them a proof of concept with their real data and its use case, but to get them into production and have it integrated across the entire organisation–with production data and all that–is a very difficult proposition,” Mehta says.

Unlike generic consumer AI applications, businesses require AI to work with their specific data, which must be well-managed, up-to-date, and compliant with industry regulations. Many organisations lack the data infrastructure and maturity needed to ensure their data is consistently accessible and secure, which poses a substantial obstacle to AI adoption.

The third barrier is organisational readiness and acceptance of AI technology. Even though AI tools are often user-friendly, they can significantly alter existing workflows, leading to employee resistance.

Consequently, businesses often underestimate the importance of change management in AI adoption. Ensuring the organisation is prepared for these changes and addressing concerns about job security are crucial steps in successfully integrating AI into business operations.

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Tackling barriers to adoption

So what can businesses do to tackle these barriers to AI adoption?

Education is certainly a key part of it, but Mehta stresses that businesses must take a specific approach. According to him, companies that have successfully adopted AI technologies educate their employees about its usage in a way that fosters innovation instead of simply saying that this technology will make them better employees.

“Some people might feel reluctant and say, ‘Am I not a good employee now?’” he says. “You can educate [them about the] better use of AI. It takes away 30-40 per cent of the stuff you do not want to do anyway, the low-value stuff. You can focus on the higher value things.”

Businesses can also encourage their employees to be creative about it. “Instead of releasing the technology into the operations team of an organisation and giving them some instructional videos and training, we can make it more like a hackathon. Come back in a month’s time with some ideas [about how you want to use the technology].”

Other ways to encourage the use of AI, which is already commonly used by global tech giants, are to integrate the technology into existing products, services, or systems. A well-known example of this approach is Microsoft Copilot.

“Most people already use Microsoft Office. Releasing Copilot was a very innovative way to get people’s exposure to Generative AI without attacking everything you do,” Mehta says.

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“It is a good example of exposing the technology as an innovation tool rather than a prescriptive of ‘This is going to do your task or your job today.’”

Last but not least, businesses can encourage the adoption of AI by learning from existing use cases.

“We are happy to spend an hour showcasing some demos and things we have seen for your team to get inspired. Then we can go down the route that I said, which is to ask the teams to come up with ideas [of how they want to use this technology].”

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Image Credit: SoftServe

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