Lupin’s global CIO Rakesh Bhardwaj discusses Lupin’s AI priorities, its data foundations and why CIOs must help redesign work, the workforce and workflows.
In pharmaceuticals, the evolution of enterprise technology is closely tied to the demands of scientific research, manufacturing quality and patient safety. For Rakesh Bhardwaj, Global CIO, Lupin, AI’s value lies in the decision advantage it can bring across this chain, supported by reliable data and compliance built into processes.
In conversation with DATAQUEST, Bhardwaj explains how Lupin narrowed more than 150 ideas to five priorities with its executive team, why it is building an enterprise-wide data foundation, and how the CIO’s role is expanding from service delivery to business orchestration.
Moving into pharma brought a different industry context, with a strong emphasis on domain expertise and regulatory requirements. Having worked across industries, I have always believed that technology is an enabler. It is the context in which we apply it that acts as a multiplying factor. Some industries are more regulated, while others are regulated differently.
The transition to Lupin has been very positive. The entire leadership, from the promoters onwards, has been supportive, and digital transformation has been a central theme from day one.
Pharma works with data, and compliance needs to be built into everything we do, given the purpose we serve globally through our patients and partners, ultimately benefiting patients.
It has been a rewarding journey so far. The investments we have made and the opportunities we see for technology give me confidence about what lies ahead. With AI becoming available at scale and moving beyond laboratories, it is becoming a catalyst for advancing our digital and data capabilities.
By and large, the structure and operating model of IT organisations across industries are similar. What changes are the industry-specific requirements. In pharma, regulation and compliance are significant factors. Whatever we seek to automate or digitise needs to have compliance by design.
The second distinction is people capability. Every industry has generalist and specialist roles. In our industry, that specialisation comes from experience in life sciences, pharmaceuticals and, in some cases, healthcare.
Being manufacturing-heavy, we also focus on what I call price-to-performance. Factories need instruments, equipment and supporting technology. In that respect, there are similarities with automotive, high-tech manufacturing and FMCG.
The distinction is the emphasis on quality and compliance by design. We embed these requirements into our development and service delivery lifecycles.
R&D is the most important part of the equation because that is where we discover new molecules and products, which then move towards approvals, manufacturing and sales.
It is also a function that is always data-hungry. Research starts with data, whether it is structured information or unstructured material from journals and other sources.
The coming together of automation, digital and AI is particularly exciting. At Lupin, we started this journey around 12–15 months ago, specifically from the standpoint of taking AI in R&D to the next level.
We are looking at areas such as filings and clinical trials. As the journey progresses, you will see these capabilities becoming embedded in more processes, including technology transfers to manufacturing for production at scale.
AI has created an opportunity for the pharmaceutical industry to move forward more broadly and deeply on its digital agenda. But AI is only as good as the data it receives. Digital therefore becomes the bedrock of any AI initiative.
At Lupin, we picked use cases in sales, manufacturing and quality to demonstrate to management and the user community that AI can be embedded into work, improve individual productivity and support decisions.
My simple view is that AI is about decision advantage. Computers gave us a decision advantage, and AI takes that capability much further.
Across the pharma product lifecycle, R&D, manufacturing, quality and sales offer significant opportunities. Functions such as HR, finance and legal also have use cases, but many are common across industries. The differentiation for pharma will come from those four core areas.
That is the direction we are seeing. The number of experiments and proof points, particularly in R&D, has grown significantly. Companies are choosing use cases that matter to them and can help them get to market faster.
It is also encouraging to see Indian companies and our peers investing in the four areas I mentioned: R&D, manufacturing, quality and sales.
Over the next 12–36 months, I expect to see more of these processes being connected end to end, allowing R&D to add greater value.
Creating a digital thread is an important part of that. At Lupin, I describe our operating model as a three-legged stool.
The first leg is the customer value chain, with the patient ultimately being the customer. This includes commercial teams, sales and marketing, R&D, manufacturing and quality. I always put manufacturing and quality together because, with compliance by design, they are two sides of the same coin.
The second leg is the enterprise value chain, covering our corporate functions. The third is digital foundations. Data comes first here, alongside security, privacy and infrastructure, all in the context of Lupin’s global operations.
Within this, I use the principle of “core, common and distinct”. We need a modular strategy. Core capabilities connect the organisation end to end. Distinct capabilities reflect regional requirements. Sales is an example because customers, pricing and competitors are regional.
Common capabilities largely cover manufacturing, quality and processes such as supply chain and logistics, where we want global standards with regional or local adherence.
IT plays an enabling role, but it also leads some of these conversations.
We started our data journey around 18 months ago and decided to move to Databricks on Azure. We are building an enterprise-wide data lakehouse as a foundation.
This is not limited to artificial intelligence. I sometimes describe it as a small “a” and a big “A”: analytics and artificial intelligence. AI for business intelligence is also becoming a reality.
Tools are making it possible to use prompts to create graphs and work with information. I see AI becoming a spectrum, starting with simple reports and dashboards and extending to agentic capabilities.
The important thing is to have a complete roadmap. This cannot be achieved in a quarter or two. As you progress, the value becomes clearer, moving from efficiency and productivity to seamless workflows and better decision-making.
The key is selecting the right use case. By that, I mean looking end to end, rather than only at a point solution.
When we started our journey around two years ago, we worked with the executive team to bring more than 150 ideas down to five. We also defined what value would look like and how that value would develop over time.
Return on investment matters, just as it does with any investment. But there must be a line of sight to achieving it. Some initiatives require groundwork first. In our case, that includes data and workflow automation.
I strongly believe that impact matters. With a new technology, the starting point may be to test a future bet and establish whether it works. As you go deeper, the value begins to take shape.
We keep ourselves informed through partners and developments outside the company, while remaining practical about what we want to achieve.
For the first time, AI is becoming something that an ordinary person can consume directly. Earlier, it was largely associated with data scientists, data engineers and researchers working in laboratories. Today, it is widely available.
That accessibility is driving adoption. People can use it to become more effective and then think about how to apply the capacity it frees up to bigger and better things, both for themselves and for the company.
Having spent around 30 years in the industry, I see the role evolving into that of an orchestrator: someone who brings different aspects of the business together.
CIOs and IT leaders need to move beyond being providers of services to becoming leaders, creators and builders of what the future will look like. It is about having a seat at the table and helping set the agenda in a way that benefits the company.
Every company talks about data, from start-ups to established conglomerates. The question is how many of us really understand what we gather, why we gather it and what value we derive from it.
IT organisations have traditionally described themselves as data custodians. We now need to make that data usable in ways that improve workflows.
Previously, much of the work on business intelligence and dashboards involved converting data into information, which someone would read, analyse and use to make a decision. Now we need to close the loop back into the process and consider how the workflow itself should be redesigned.
At Lupin, I talk about three Ws: work, workforce and workflow. All three are going to be redesigned with AI.
I would answer that slightly differently. The starting point is the value we want to unlock and the business problem we want to solve on a sustainable basis.
Existing technologies, new technologies or a combination of both can be brought into the solution. Investing in technology and modernising existing systems are part of the role.
What matters is the value we deliver to stakeholders and the company. Technology will help. The questions are where we apply it, at what time and what value we deliver.
This interview was published in the Dataquest on October 29, 2026
