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How organizations are unlocking business transformation through AI
Listen to our podcast on how AI is reshaping business transformation across Indian enterprises with AI‑ready data, governance & clear transformation roadmaps.
In this episode of EY.AI Unplugged Season 2: AI Agents of Impact, we explore how artificial intelligence is reshaping business transformation across Indian enterprises. Anurag Gupta, Leader, EY‑Parthenon India, shares insights on how organizations at different maturity levels can unlock value from AI to enable an enterprise‑wide impact. The discussion also highlights the importance of AI‑ready data, governance, and clear transformation roadmaps, alongside critical decisions on building versus buying AI solutions. From productivity gains to new business models, this episode examines why AI is a unique opportunity to tap sustainable, scalable future growth.
Key takeaways:
Early adopters use standalone GenAI tools, while mature organizations embed AI to drive enterprise‑wide value.
AI initiatives fail when organizations try to embed AI without data readiness, governance, leadership buy in or clear value definition.
Building an AI-ready data practice requires enterprise data strategy, strong governance and purpose-fit data for each model and use case.
Organizations should buy AI solutions for speed and low risk use cases, while building in-house AI for mission critical, high value capabilities.
Successful AI transformation depends on maturity linked roadmaps, scaling ambition progressively as data, governance and organizational readiness improve.
Organizations should first understand AI’s potential, define a roadmap basis their maturity level, and then progressively scale AI‑led interventions as organizational readiness improves.
Anurag Gupta
Leader, EY-Parthenon India
For your convenience, a full text transcript of this podcast is available on the link below:
Welcome to EY.AI Unplugged Season 2: AI Agents of Impact, a special series by EY India Insights. In this episode, ‘Business Transformation through AI,’ we explore how Indian organizations are using AI to reimagine strategy, transform operations and create enterprise-wide value at scale. Our guest for today is Anurag Gupta, Leader, EY-Parthenon India, who works closely with CXOs on strategy-led, AI-enabled transformation programs across industries. Anurag, a very warm welcome to you, and thank you for joining us today.
Anurag
Thank you, Pallavi. Happy to be here. AI is an interesting topic. We are all busy thinking, working with clients on AI, so happy to talk about it.
Pallavi
Thank you, Anurag. How have organizations implemented Gen AI initiatives at different stages of maturity? What key considerations should organizations at varying maturity levels keep in mind when investing in AI? What common challenges do organizations face across these maturity levels?
Anurag
Basis all the interactions that we have had with various clients across sectors, what we find is that different organizations have different levels of understanding and buy in into what AI can deliver for their business and their organizations. To that extent, AI literacy across organizations, across hierarchy in those organizations, and understanding of the value that it can generate varies significantly. Also, many of these organizations are not yet ready, not mature enough in terms of having the right data, to be able to leverage AI tools, having the right data security to ensure that all compliance aspects are taken care of. And having buy-in and the excitement and enthusiasm among people in their organizations to leverage and embed AI.
Hence, everybody is at different stages of maturity. Those who are relatively low on the scale of maturity have been using some standalone GenAI tools - it could be for research, transaction processing, or some standalone use cases. But others who are far more mature are the ones who have started thinking about what value they can generate out of AI. They have started thinking about new business models; they are reimagining their business processes across different functions and embedding AI into it.
I would urge organizations to learn more about what AI can do and then define an AI journey for their organization – a roadmap which keeps in mind their current maturity level. And as the maturity levels go up across different vectors, they can start thinking of bigger AI-led interventions in the transformation journey. Some of the challenges that we have seen organizations face include lack of clarity of what value will AI deliver, lack of readiness on AI governance, data security, acceptance among their people at the leadership level as well as across the organization, lack of readiness to accept AI use cases or AI agents of what they do on a day-to-day basis.
So, we have seen organizations which have taken a leap, when they were not ready, trying to do bigger proofs of concept (POCs), bigger AI interventions have not succeeded. They have only ended up doing those POCs but have not managed to scale them up. So, it is important that organizations chart out a clear roadmap, based on the maturity of their current state of AI readiness.
Pallavi
Why is AI-ready data critical for the success of AI and GenAI initiatives? What misconceptions do boards and executives often have about AI data readiness? What steps should organizations take to build an AI-ready data practice?
Anurag
This is an important aspect of getting success from various AI initiatives because the AI algorithms, whether LLMs or SLMs, are going to take actions, perform tasks, and come up with outputs based on the inputs provided to them. And when we talk about enterprises, if the enterprises have not provided the right quality, width, and depth of data to those AI algorithms, we are likely to see wrong outputs, wrong conclusions, and wrong transactions and tasks performed in those use cases. That will obviously bring the organization back to square one. As a result, they are likely to stop believing in the power of AI, feeling that it is not for them. So, it is important to have the right quality and quantity of data, which feeds into enterprise-level, AI algorithms and initiatives which leverage that.
The misconceptions that many boards and executives have is that they have AI-ready data. Since most organizations have been structuring their data, they believe they are data ready for AI. But data readiness for AI is at a different level in terms of how we need to organize our enterprise data and make it available at the right time, for the AI algorithms and models to leverage.
So to do that, organizations will need to clearly (as they think through their AI roadmap), look at the enterprise-wide data and put it in the right plumbing, if I may use the term, to make the right enterprise data available for each of the algorithms and use cases that are being put into practice. This is to be coupled with validating the fact that they are inputting the right data into each of those algorithms, so that then the trust in the output and the actions that the that the AI models take, goes up.
Hence, it is extremely critical for all organizations to get their data strategy and data governance right, so that the outcomes are as per expectation and they can generate value out of it.
Pallavi
How are organizations approaching the decision between buying AI solutions versus building them in-house? What are the advantages and disadvantages of each strategy?
Anurag
It is a decision tree that organizations will have to go through for various AI solutions as they go through this journey. There are various startups or AI service providers that offer ready AI platforms or solutions, which organizations can use in case they are looking for speedy implementation. If there are ready AI models available and they are not mission critical, that is a good criterion to use such AI solutions, which are bought from external entities. These versus building them in-house depends on what is mission critical to the business; what is proprietary or unique to the business. What is going to be at enterprise-level, large-scale AI intervention or transformation needs to be built in-house.
Now, building such solutions in-house has its own challenges – having the right AI architects and engineers within the team, who can build it, implement, scale up as well as maintain it. That is an advantage: If you are building it yourself, it allows you to not just design and build, but also implement it, scale it up and then maintain it.
Also, through an in-house setup, you protect your enterprise data; it may not go out of your enterprise architecture, enterprise boundaries, versus when you go out and leverage external solutions, where you will be dependent on those AI solutions and platforms and the way they are designed. I would say, where speed of implementation is important or the risk is low, one should use the readily available AI solutions, and those which are core to the enterprise, mission critical and are likely to have a high value over a period of time, should be built in-house.
Each of the organizations are at different stages of maturity, taking their calls between buying or building in-house.
Pallavi
How does EY help clients and log business value to AI, and what is your perspective on the transformative potential of AI across industries?
Anurag
At EY, we strongly believe that AI has a massive transformation potential for clients across industries. Technology like this comes along only once in a few decades. We have the mobile phone; the internet; the computer, which came in over the last few decades, but AI is far more transformative than them. So, in terms of the potential that AI has to drive greater productivity across industries and organizations, its ability to disrupt existing business models and operating models, its ability to add value to a lot of business and operating models, is massive.
If we see the IT/ITeS sector today, it is the most disrupted because AI is now driving significant productivity and revenue compression, but at the same time, it is also offering new opportunities for clients in those industries. So, across sectors, whether it is healthcare, technology, consumer or industrial, there is a lot of transformative potential that AI offers.
We, at EY, are working with our clients to define their AI roadmap to be able to bring sources of AI – generated and led – deliver value across functions such as supply chain. We are doing a massive program with a global FMCG major to make their supply chain autonomous.
Similarly, in India, when we look at procurement as a function, we are working with a large conglomerate to completely automate their procurement function and various transactions that the function adopts. So, across sectors and sector value chains, across functions, on the people side (human resources), with the ability to train people, to assess competencies, to provide customized training to people in clients’ organizations – those are some other use cases that we are working on. And we clearly see the transformative potential there. So, EY is completely lock, stock and barrel, working with our clients to be able to unlock their business value, across these functions and in their respective sectors.
Pallavi
Thank you, Anurag. That brings us to the end of this episode. Thank you so much once again for joining us and sharing all your perspectives on how AI is transforming businesses.
Anurag
Thank you for having me.
Pallavi
Thank you to all our listeners. You were listening to EY.AI Unplugged Season 2: AI Agents of Impact, a special series by EY India Insights. To explore more perspectives on how AI is driving business transformation across industries, visit ey.com/in. Stay tuned as we continue to spotlight leaders shaping the future of transformation through AI.
