How AI-ready are Enterprise data sets currently?

One of the biggest challenges right now for most organisations is AI-ready data. Machines need to look at the data and identify it and its various components. For that, you need ‘metadata,’ which is data which describes data. We invest a lot of money in building out metadata for all our data sets.

In the past, we did not have a good reason to do so but a lot of our client organisations are struggling with metadata to help AI models. Further, there is the challenge of connecting data in different places in the organisation or within the same department.

Organisations generally speaking are far behind on what needs to be done to build out these capabilities. We are building out a connected data fabric, which makes distribution and AI-driven use cases easier, and new tools like MCP Connectors to help AI models read from our data with a high level of certainty. So, there’s a lot of work organisations need to do to catch up.

Do you feel that this is going to lead to newer jobs?

Absolutely. 10-15 years ago, there weren’t really any meaningful data engineer roles. Those are new roles that have come up and we go through a similar cycle where new roles emerge. Some of those roles will be around people who can prompt engineer at scale, people who can code with these systems.

There will be new roles around a lot of other fundamental roles; how researchers and software engineers work will change. Rather than doing solo work, AI tools will help them be more productive. It’s going to be a bit of a change management exercise as well because people who can’t keep up or embrace these new tools might suddenly find themselves much less productive than their counterparts and colleagues.

These are changes that happen. What’s different this time is that the timelines to adapt are far more compressed. So, it’ll be a significant expectation for people to adapt to these new tools and technologies or be left behind in the workforce.

Everyone is talking about a Human+AI model. How does that harmonisation work?

There are two parts: how people use AI to become more productive and Agentic workflows. We are empowering our researchers, data scientists, data engineers, data analysts, all major personas with these new tools to be more productive within limited time, cover more topics, have higher quality output and do research that would be hard for a human due to consumption of huge data.

There’s also a focus on responsible use of AI and ensuring customers understand AI deployment. We label our products very clearly because ours is a business of selling trusted data and research and we want to retain trust.

In the future, we can see even in our own workflows potential application of agents to do straight-through processing of simple workloads.

Are there any particular roles that agents can completely take over?

A big factor in answering that question is time. Potentially, agents can run some workflows in data extraction from structured financial filings. Very structured, regimented agents can do that within reason.

Those are some areas where agents can run within the next 12 to 18 months. There are much more complex workflows like risk management, investment decisions, etc, for which the timelines are unclear.

What skill sets should employees or applicants develop to prepare for the AI role?

The best skill set they have is a high level of fluency and proficiency in all of these new tools and technologies. Not everybody has to be an expert: can you use X tool to build an agent to simplify three things you do every day?

Can you use X to quickly spot and identify bugs and fix them? Can you use X tool? People who can do that will have more opportunities.

Going forward, how do you see the AI-grade data market evolving?

Both the demand and supply of AI-ready data will grow exponentially. What will also happen simultaneously is that the non-AI-ready data market is going to shrink over time because all customers will become more productive and their primary demand would be AI-ready data.

For large industries, especially the sophisticated ones who work a lot with data, this creates new opportunities to be more productive and serve customers in new ways. Both because of what’s going to happen with proximity to data centres and the raw talent available here, India finds itself in a very promising position to capitalise on this boom.