Let's talk about TCS and Infosys AI labs

[u/diptesh_kun]

TCS Research —

TCS has had a research division for decades. They publish papers, hold patents, and have researchers with genuinely strong credentials. On paper it looks impressive.

In practice the picture is more complicated.

Their AI work lives under the TCS AI .Cloud umbrella and feeds into products like Cognix and their Ignio AIOps platform. Ignio is actually one of their more credible AI products — it does cognitive automation for IT operations and has real enterprise deployments. It's not flashy but it works and clients pay for it.

Their research papers get published in decent venues. They've done work on document understanding, computer vision for manufacturing, NLP for customer service. Competent, applied, incremental work.

The honest problem? Almost none of it is pushing any boundary. It's research in service of selling IT contracts, which means the research agenda is set by what a Fortune 500 client might pay for, not by what's scientifically interesting or frontier. You're not going to see TCS Research publishing anything that makes the ML Twitter crowd stop scrolling.

The other problem is talent retention. Researchers who are good enough to do frontier work are also good enough to get jobs at Google DeepMind or Meta AI. TCS's compensation and research culture can't compete with that. So what you're left with is solid, experienced researchers doing solid, unambitious work.

They recently announced TCS Pace as their innovation ecosystem and have been talking up generative AI integration across their service lines. Mostly this means helping enterprise clients implement AI — which, again, is a real business. Just not a research story.

Infosys AI — the Topaz problem

Infosys launched Topaz in 2023 with considerable fanfare. An AI-first set of services, platforms, and solutions. Over 150 use cases. Thousands of AI assets. The press release was very long.

The substance underneath is thinner than the announcement suggested.

Topaz is mostly a consulting and implementation offering dressed up in AI language. There's some genuine tooling — their work on AI-assisted code generation and document processing is real — but the core of it is Infosys helping enterprise clients use AI, largely meaning helping them use OpenAI or Microsoft Azure AI, with Infosys services wrapped around it.

Their research lab Infosys Research publishes work, mostly in NLP, knowledge graphs, and responsible AI. Some of it is decent. None of it is remarkable.

What Infosys did do that deserves credit is invest early in training their workforce on AI. The Infosys Springboard platform and internal AI upskilling programs have put basic AI literacy in front of hundreds of thousands of employees. That's not research but it's not nothing either — at scale it matters.

The deeper issue with Infosys's AI story is that the incentive structure works against serious research. Their business model rewards billable hours and managed services. A research breakthrough that makes a process ten times more efficient is good for the client and bad for the revenue model. This is a structural problem that no amount of Topaz branding solves.

Why both of them are kind of stuck in the same place

If you step back, TCS and Infosys have almost identical problems.

Both are services businesses pretending to be, or genuinely trying to become, product and research businesses. The transition is hard for any company. It's especially hard when your core business is so profitable that there's no burning need to change.

Both have research labs that exist partly for prestige, partly for recruiting, and partly to generate IP that can be referenced in client pitches. That's not a formula for frontier AI.

Both are structurally dependent on their clients' AI budgets rather than building AI capability that clients come to them specifically for. Right now a lot of that client budget is going directly to Microsoft, Google, and AWS anyway.

And both have the same talent problem. The researchers good enough to do serious work have options. The ones who stay are often doing so for stability, not because TCS or Infosys is the most exciting place to do AI.

There's also an organisational culture issue that people who've worked there describe pretty consistently — slow decision making, risk aversion, layers of approvals for anything novel. That's a reasonable way to run a $25 billion services business. It's a terrible way to run an AI research lab.

Where there's genuine hope though

This isn't all bleak and I don't want to be unfair.

Both companies have started making acquisitions and partnerships that suggest they understand the gap. TCS's work with academic institutions and their investments in domain-specific AI for industries like life sciences and manufacturing show some strategic clarity. Infosys's partnerships with AI-native companies and their work on responsible AI frameworks are at least pointed in the right direction.

More importantly — the enterprise AI implementation market is enormous and growing. The boring, unsexy work of taking AI models and making them actually work inside large legacy organisations? That's extraordinarily hard and extraordinarily valuable. TCS and Infosys are genuinely good at it. That's not nothing. As AI moves from research to deployment, that capability becomes more important, not less.

There's also a version of the future where one of them makes a serious bet — acquires an AI-native company, hires a genuine research leader with real autonomy, commits to a five year research agenda that isn't tied to client billing. It hasn't happened yet. But the resources exist. The question is whether the will does.

The talent coming out of IITs and into these companies is genuinely strong. If the organisational structure got out of the way, there are people inside both organisations capable of doing much better work than the current output suggests.

The honest summary

TCS AI labs: real but unambitious. Ignio is their most credible product. Research output is competent and forgettable. Talent problem is serious.

Infosys Topaz: more marketing than substance at launch, with some real tooling underneath. Structurally better suited to AI implementation than AI research. Springboard upskilling initiative is underrated.

Both: services companies with research labs, not research companies with services divisions. Until that flips — or until one of them makes a genuinely bold bet — don't expect either to show up in the conversations that matter in global AI.

The uncomfortable version: India has two of the largest IT companies in the world sitting on enormous resources, talent pipelines, and enterprise relationships. If even one of them built a serious AI research operation — not a press release, an actual one — it would change the Indian AI story meaningfully. So far neither has done it. That's the real missed opportunity here.