Infosys has a strong track record of innovation. How did this help you to create an AI-first business strategy?

Infosys – through our Center for Emerging Technologies – has been at the forefront of generative AI before it became mainstream with ChatGPT. In fact, as an early supporter, we were one of the initial donors to OpenAI during the nonprofit phase. Our commitment to generative AI extends back many years, particularly as part of our cloud migration program. Our Center for Emerging Technologies and the Infosys Innovation Network look at the startups and new technologies that are emerging, curating them to use internally for the organisation and also for our customers. This proactive approach has given us a head start in our generative AI journey.

How are your customers using generative AI?

We’re seeing recurring patterns among our clients using generative AI. One key application is customer advisory services, which is about taking organisational knowledge and making it available to front-end customer service or advisory professionals, whether it is event managers or sales personnel
With generative AI, you can train a model with your organisation data, so that everybody in the field can have the latest up-to-date information. In sales and marketing functions, tasks such as newsletter and content generation are also automated. For IT operations, generative AI is a game changer as it helps with self-service assistance and faster resolution. Another area where AI can play a crucial role is in business operations – from automated document processing and document summarisation to enterprise search for specific business functions.

Another use case is AI for legacy systems migration, modernising your current landscape. Then, of course, there is helpdesk and contact center use cases. Learning and enablement is another interesting area as you can generate a lot of learning content much more seamlessly, including creating videos from your content and avatars to deliver the content – making the learning approach much more intuitive and customised with generative AI.

Which industries are adopting AI the fastest?

We see applicability across all industries, and we’re talking to our customers across different sectors. But some are, perhaps, leveraging it a lot more, like healthcare, financial services, telecoms, and media.

What would your advice be to IT leaders approaching the integration of AI across their business?

We see IT leaders jumping straight to proof of concept to test the water. However, if you want to implement at scale and make a difference within an organisation, it requires more deliberate planning and strategy.

Some of the key lessons learned are, first and foremost, it is not just an IT-only initiative. You need to get stakeholders onboard across your business functions and have a strong executive sponsorship. This goes a long way in the success of taking it to scale across an organisation.

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The second piece of advice is to understand there are hundreds of use cases that are actually possible and it is important to prioritise these to figure out which is the best benefit to the business – a focus on the return on investment is key. Without this, you may end up creating use cases that don’t have adequate business impact.
It is also important to develop the architecture and implementation patterns from a technology perspective. Technologies move very quickly, therefore it is important to ensure that what you develop does not become outdated. We would recommend creating a platform to leverage the latest and best models and keep it slightly abstract from your use cases and the data.

Another key lesson or potential pitfall is making sure that we are looking at responsible AI right from the start – this must be free from bias, while taking care of security issues, data privacy issues, and also making sure that it is trustworthy. This aspect is extremely important, it needs to be responsible by design from the design stage.

Talent is also a crucial aspect – it is important to both centralise some of the key capabilities that you actually need and at the same time, democratise AI across the entire organisation. Finding talent gaps and making sure that the entire organisation is skilled to be able to leverage what you can do with AI is very important.

And finally, it is important to build a good partner ecosystem – from product vendors across cloud service providers and specialist AI startups to service integrators, for example.

Do you see AI implementation as amplifying human potential or replacing human roles within an enterprise setting?

As an organisation, we want to become an AI-first enterprise. What this means is that we look at how we can apply AI in everything that we do. One of the things that we are starting to look at is how we could create AI assistants for every role in Infosys. As software developers, we should be able to do the job better with the enhancement of AI. With AI, a recruiter should be able to perform the role better and so should a marketing person or a salesperson in their respective roles.
We looked at use cases and how we can amplify human potential, not replace it. AI technology is well suited to provide outputs that a person needs to review before incorporating into whatever they are actually doing. And that’s the way that we look at it, we call it amplifying human potential to create exponential impact.