Mphasis is aiming to grow at twice the pace of the IT industry, driven by a record $760 million in net new deals in Q1 FY26 and a strong pipeline, CEO Nitin Rakesh told CNBC-TV18.
“We are pegging it to 2x at this point,” he said, highlighting that the company’s confidence comes from the visibility offered by the order book and recent deal conversions. The deal wins in Q1 were over 100% higher year-on-year, and significantly up from $390 million in the previous quarter.
While the macroeconomic environment remains uncertain, Rakesh said the company is focused on execution at the micro level. “If we have the right propositions, the right client needs identified, and take a very account-specific, deal-by-deal approach, we should be able to grow despite the macro,” he said.
A key engine of growth is artificial intelligence. Around 68% of Mphasis’ deal pipeline is AI-related, spanning areas such as developer productivity, contact center transformation, claims, underwriting, and IT support. “It’s more about applied tech,” Rakesh said. “We are not going out there and building new LLMs… this is really more about infusing tech orchestration with people to deliver value.”
Large deals remain central to Mphasis’ strategy. The company closed four large deals in the June quarter, three of which exceeded $100 million. “For us, a large deal is $20 million or above,” Rakesh clarified, adding that the $50–$200 million range is a sweet spot.
Despite investments in AI and client-specific initiatives, margins held steady at 15.3% in Q1, said CFO Aravind Viswanathan. “We kept it flat, largely driven by improved utilisation, which compensated for some of the investment spend,” he noted. The company continues to operate within its guided range of 14.75% to 15.75%.
On the impact of AI on margins, Rakesh said its early days but expects operating leverage as solutions become more repeatable and scalable. “That gives us the ability to either pass back to the customer or get more competitive,” he explained.
Mphasis also sees generative and agentic AI as an opportunity rather than a threat. While applying AI to existing work can be deflationary, it is also making new business cases viable. “It’s unlocking deals that were too expensive, took too long, or had too much risk,” said Rakesh.
Mphasis reported Q1 FY26 revenue of ₹3,732.4 crore, up 1% sequentially, while Profit After Tax came in at ₹441.7 crore. EBITDA margin remained flat at 15.3%, and constant currency revenue also grew by 1% over the previous quarter.
Below is the verbatim transcript of the interview.
Q: The Q1 FY26 performance itself was good. It was better than expected. You’re talking about outpacing industry growth as well, and also steady conversion of TCV to revenue. You’re saying that you’ll outpace industry growth. Do you want to put a number to that?
Rakesh: We called out kind of a 2x-to-industry growth number right now, and it’s driven mostly by our own order book as well as the pipeline. I mean, we had a record order book quarter in Q1 at $760 million in net new deals, which is over 100% YoY, and we had $390 million last quarter. So, I think the pipeline had been building up for the last two quarters, and we are glad that we were able to convert that into actual deals. Some of that translated into revenue growth within the quarter as well, and it gives us a little bit more visibility over the next couple of quarters. So, I think that’s what’s driving it. We are pegging it to 2x at this point. Last quarter, we had called for above industry. So, I think the confidence really comes from the ability to look at what the order book holds and what the pipeline holds.
Q: What about the macros? Are they improving on the ground?
Rakesh: About a year ago, we decided that in an uncertain macro, we have to focus on the micro. What that really means is, if we have the right propositions, if we have the right client needs identified, and if we take a very inclined, account-specific, deal-by-deal approach, we should be able to find a way to at least continue to grow the business, despite the macro being what it is, because we can’t control the macro.
Q: Last time you said that you’re focusing on smaller deals, not going after the big ones. And there is enough action happening. Maybe I’m not characterising it correctly, but my point is, you’re kind of looking at not the mega billion-dollar multi-year kind of things, but there’s enough happening in terms of smaller-sized deals. And we have some numbers — that in the pipeline, 68% of the deals are AI-related. Is this about applying AI, implementing AI like generative AI across clients, or something else?
Rakesh: I think when we said that we are focusing on large deals — for us, a large deal is $20 million or above. Even in Q1, we had four large deals, three of them over $100 million. I think that’s kind of a very good sweet spot for us to operate in — in the $50 to $200 million range. And there is a bunch of deals in the pipeline, even today, that qualify for that. So just to clarify, small deals don’t necessarily mean $1-2 million deals.
On the AI-led pipeline — this is a combination of a few things. As clients have gone through the last two years and looked at the ability to actually use AI to impact their tech and their operations, there is now a pretty strong appetite and a top-down push to make these more enterprise-wide. Whether it’s applying AI to get productivity, contact centre transformation, developer productivity, or applying AI to see how they run their operations — both tech and ops, claims, underwriting, support, production support, maintenance, IT help desk.
So, for us, it’s more about applied tech. We are not going out there and building new LLMs because that’s not what we need to do. There are enough and more available for us to apply. So, this is really more about infusing an element of tech orchestration and bringing them together with people to deliver a solution that provides the value the customer is looking for.
Q: I wanted to come to the margin picture and what this infusion of AI means for margins. Then there are other factors which maybe Aravind can talk about, which are impacting margins. You’ve been holding fairly comfortably around the 15% level, but going forward — and this is not a question only for the next quarter, but say, over the course of the next year or year and a half — what will this greater AI integration mean for your margins?
Rakesh: Again, I think it’s a little bit too early to tell. We are still in the early stages of adopting a lot of these solutions, but there’s definitely some play that gives us the ability to infuse the platform or codify our solution into a set of repeatable tools. That definitely gives us a little bit of operating leverage that we can use to either pass back to the customer or get more competitive, so we don’t need to really drop our margin to win a $150 million deal.
That’s been established over the last two or three quarters, because you just pointed out that we’ve held our margins quite tightly. Whether that gives us the opportunity to expand margins over the medium to long term — I think we’ll address that as we get a little bit more comfortable with scaling these deals across multiple customers.
Q: But on the other factors impacting margins — Aravind, I mean general competitiveness in deals, wage costs, and overall operating costs. Are you comfortable and confident about keeping margins here? Is there any scope for improvement in margins excluding AI?
Viswanathan: We kept our margins flat in the quarter gone by — we kept it at 15.3% — largely driven on the back of significant improvement in utilisation, which kind of compensated for some amount of client-specific investments and AI-specific investments we’ve been making. From a range standpoint, what we’ve said is that we would operate between the 14.75% to 15.75% range, and we are probably right bang in the middle of that.
I don’t see a reason for a big change. Obviously, like Nitin said, as we ramp up these deals and win more, it’ll be important to see how we navigate. I don’t think the traditional levers are going to swing it too much. But obviously, AI would be something interesting to see — how it plays out from a margin standpoint.
Q: There are two parts to AI work: one is orchestrating AI, which is new work, and then there’s using AI to do your existing bread-and-butter work. So, the latter is deflationary; the former, we don’t know yet. But this agentic AI — do you think it’ll impact AI orchestration work itself, because you’ll have these AI agents orchestrating all that? Do you see disruption?
Rakesh: I mean, to us, the value stream of AI actually cuts across. If you look at the stack that enterprises need to build, and there is definitely an AI engineering element to that, we are actually calling it the Mphasis AI Superhighway as a solution. What that really means is you need to create an environment where you have the ability for an enterprise to offer these services to their internal users, for them to build AI applications on top of.
I don’t think that can be disrupted by agentic AI because that’s an engineering exercise. It can be done efficiently using AI agents, but it requires an element of design, governance, and execution that cannot be fully automated using these agents.
Applying agentic AI or generative AI to existing solutions — you can call it deflationary. But the way we look at it is that it’s unlocking deals that were probably not conducive to ROI earlier because they were either too expensive, took too long, or had too much risk. So, it’s actually opening up a new addressable market, even within our existing book of business.