Global digital consulting firm Infosys is on a mission to bring an AI-first slate of solutions to companies so they can work more efficiently and effectively. CTO Rafee Tarafdar, a company veteran, is one of the driving forces behind this transformation. I talked to him about how he’s led both Infosys and its client base to better and more fully embrace AI.
This conversation has been edited for length, clarity and continuity. It was excerpted in the Forbes CIO newsletter.
You’ve been at Infosys a long time. Tell me about your years there and how both you and the company have evolved.
Tarafdar: I joined Infosys in 2004, and I’ve been with the company now for 20 years. At Infosys, my stint has been in three waves. The first wave, I was largely involved in working with our global clients in their digital and cloud transformation journeys as an architect, which is what I was doing in the first part of it. I’ve built a lot of systems, implemented a lot of digital solutions and all that.
The second wave was leading a lot of our technology consulting. I was in London for a few years where I was driving it for Europe. This is where I set up a lot of tech consulting capabilities.
In the last few years, I have focused on a few things. One is building new technology capabilities within the company. As I saw that most of our clients, the Fortune 500 companies, were becoming technology companies, they required a new kind of talent: deep tech, which is full stack, who understand new ways of working, building platforms, being agile and all that. I created new technology streams within Infosys to build this new kind of talent model within the organization.
I also worked on population-scale platforms. In India, we have this tax platform that is used by millions.
I’ve also been driving our own internal transformation to become a digital-first company, a cloud-first company. In the last three years, I’ve been working on making Infosys an AI-first company. I’ve been taking a lot of these learnings to apply to our clients in helping become AI-first companies.
As part of my role, I also now lead the Center for Emerging Technologies, where we focus on technologies that are likely to become mainstream in 12 to 36 months, and invest in those assets.
What does it mean to be an AI-first company?
The first part is: How do we use AI to amplify the potential of all the humans? The idea for us was AI is a tool that we can use to become a lot more efficient, productive and more client relevant, and become better problem finders, solvers and all.
The second part of it is how do you weave AI into the regular ways of working at the company? If we are doing software engineering for our customers, how do we use AI to generate a lot of code? Today, if you look at it on an average, every few weeks, we generate about a million lines of code through AI. How do we make AI integral to the services that we offer to our clients?
The third part is: How do we use these to drive value for businesses? If somebody is a bank, how do we use AI in order to make their customer onboarding process a lot better? How do we make credit decisioning better? If you are a services firm, how do we use this to improve services? If you’re a retailer, how do we use this to drive better customer engagement? We then start thinking about it from an industry perspective.
The fourth is about doing frugal innovations to bring the value of AI. Eventually for businesses, it is about doing it in a trusted and secure manner, doing it at low cost and doing it with very high efficiency. We ended up building our own small language models a few months back, and we are focused on things that will drive value to the enterprise.
How does bringing the AI-first mentality to Infosys translate to working with clients?
[With] AI, the only way you learn is by experimenting and trying out. There’s no other way because the tech is changing so fast. When we did this at Infosys, we were one of the largest users of GitHub Copilot, and we rolled [it] out. Then we built a lot of AI assistance for learning for our salespeople. We built it for our own internal employees.
In the first wave of adoption of generative AI, we helped our clients based on this experience. We said: How can you adopt these code assistants to drive higher productivity in engineering? How can you build your own AI assistant? With a financial services company, we helped them build a wealth management assistant. For one of the banks, we helped create their own version of ChatGPT, which is for them private and secure. We did one for helpdesk. This essentially accelerated the adoption.
The first wave was about using AI to augment what humans are doing, which means it is not replacing anything. It is just letting me do [things] faster, better. We said: How do we now automate the tasks? We started looking at areas like sales and marketing, business operations, customer service. We said: Can we use AI to automate a lot of these tasks completely? Based on our experience, we started doing a lot of work for our clients where we are starting to help them roll out these AI solutions at scale. We are bringing more automation in their core business processes.
Today, as [AI] agent technology is being applied to reimagine or reengineer the business process, we are working with clients to say: Where is the value? Because at the end of the day, clients are saying: Help me find value in AI, either cost, growth, NPS score or risk and protection.
From our experience, we are picking areas where value can be delivered immediately or longer term. We are helping them prioritize, and then we are applying these in process reengineering. We said if you use gen AI, you can increase the number of customers onboarding quickly because instead of taking four to five weeks, you can do it in a week. Credit decisioning, which would take several [weeks of] back and forth, can now be done quickly in a week’s time. We are looking at avenues which will help drive growth.
We realized from our own Infosys experience that you need to have an innovation team that is constantly tracking everything happening in the open source and AI space, then build products that can deliver value that can then be scaled across the company. The same thing, we applied with a few of our clients. We have set up an innovation center where we look at how AI is evolving, but we already were working with a few clients about six to nine months back on applying agentic technology in core business processes. The job of the innovation lab is to incubate new technology at businesses and then help scale.
What kind of support have you had in making these changes, both at Infosys and among clients?
At Infosys, this was sponsored right from the board and the leadership. We look at it as a very strategic initiative at Infosys. Everybody from the leadership to the execution team is all-in. In that way, we don’t have a challenge with clients.
We see two types [of clients]. There are clients where this is a boardroom agenda, where the boards are asking them what is the AI strategy. In that case, we are working with the board and with their CEOs and COOs and CXOs. In those cases, it is being done in a more strategic way.
There are other clients where it is a bottom-up exercise, where individual teams are coming up with a lot of these ideas and then they’re implementing [them]. Here, you need to put together a business case and we have to justify the value in doing this, then take a few use cases at a time, demonstrate value and scale. This takes a little longer time, because you first have to win these stakeholders, prove out and do it.
Where we come top-down, it has been phenomenal. The pace has been great, and they’ve already started seeing a lot of value.
What has been the biggest challenge that you have encountered turning a workplace into an AI-first workplace?
The first challenge will be the data. A lot of times, the data that is there is not fully ready to be consumed for either pre-training or building these AI solutions. We end up spending typically 60%, 70% of our time in preparing data. A lot of times the data may not exist, in which case we may have to create synthetic data in order to fix the data gaps. We know how to fix it, but it takes time.
The second part of it is responsible AI because most businesses are regulated industries. Ensuring that we are building AI products that are legally compliant, trusted, secure, there is no bias that is explainable [or] auditable, all those things become important. Sometimes organizations may not be fully ready, either with the processes or the tooling or the risk mitigation strategies to deal with it. That takes a good amount of time. At Infosys, we have launched a solution to help organizations become faster, but we took some time as well. For us, it took about two years to get all of it in place.
The third part of it is the cost of running AI. While the cost has come down over the last two years, it is still significant. It is not as cheap as a normal search application or a transactional application. We are looking at how do we run this with optimal cost, which is where a lot of these innovations are important: If I have to scale it to thousands of users across the company and to their end users, then it also has to be frugal enough that the ROI is justified.
The fourth is talent. If an organization wants to build your own models, you need AI masters. Today, there are only a handful of AI masters who can build models [from the] ground up. Finding that talent is also a little challenging. Most organizations will have to either build talent or hire that kind of talent to do those kinds of activities.
What do you do to find that talent? What, precisely, are companies looking for right now?
We are looking for those with very strong first principles thinking, somebody with good mathematics and computer science background. But more importantly, we look for problem finders and solvers. Anybody given a problem can solve it, but can somebody find problems and innovate on it?
To hire these kinds of people, we do two things. We go to some of the top-tier universities and then we hire graduates who can be part of this. Then we look for contributors to open source and other avenues where a lot of innovation happens.
But for us, the majority of them come from internal skilling. Infosys as a company is 40 years old, and one of our biggest strengths is training people to build skills of [the] future. We picked some of the best people that are within the company, and then we run them through a structured program and mentoring in order to turn them into AI masters or AI builders.
At Infosys, we have a three-tiered training program now. We are saying all employees at Infosys will be AI aware, which means they know gen AI, prompt engineering, everything. Second, we have AI builders who know how to build gen AI applications. Then gen AI masters who are the actual deep experts. Through a structured program, we train everybody and skill them across these three levels. Our biggest strength is internal training, and where we have gaps, we go and hire from market.
What kind of advice would you give to a CIO at a company that is trying to build its own AI-first program?
One, look at the enterprise AI as a strategic driver, because this has already become a general purpose technology, which means it will get embedded into every part of the business. If that is the case, then how do we look at it strategically?
For this, I think there are five key things that they need to get right. First is how do they find value in AI? That’s the first important thing to create a business case. For that, they need to look at strategic business value chains and not use cases. Identify areas where value can be delivered so they can demonstrate the business outcomes, which becomes critical for the success of any AI initiatives.
The second part is set the foundation. You have the data for AI, have the platforms in place, make sure that these systems are talking to each other, the data issues are sorted. If they don’t have the right foundation, they can never scale the AI initiative.
The third is invest in the right operating model. What we have seen is just giving AI tools does not lead to better productivity efficiency or change. You also need to change ways of working. Having a talent reskilling program or the right AI talent with new ways of working is important to get this right.
The fourth is to be responsible by design upfront. This cannot be an afterthought, because in most regulated industries, this will come to bite. That needs to be very clearly defined and done.
And the fifth thing, which is very critical, is to have a foundry and a factory model to scale. Eventually, if you’ve got all of these right, then it is about doing hundreds of AI projects at scale. You need to have the right operating structure to do these programs at scale and democratize AI across the company, so that you have more and more innovators who are using the technology to innovate for their business and customers.