What is holding Indian enterprises back from moving beyond AI pilots and experiments to scaling AI across their businesses, and what are the most urgent barriers to that scale?

There are two differentiations. One is consumer AI, in terms of productivity, point solutions, and human capital in terms of technical work. We are seeing a huge uptake of consumer AI. Even the IT services industry is going through a major transformation: if you could do a development task in X days, we can bring it down significantly. In an end-to-end organisation, within a business process, pilots are being done, which is giving incremental value. At an enterprise level, there are different complexities, like accuracy and end-to-end processes, where how businesses operate is seamless. An organisation has four major end-to-end processes: Procure-to-pay process, hire-to-retire process, design-to-operate process, report-to-report process. You look at these processes end-to-end and realise that the data volumes and the data sets are the fundamental inputs.

For any data or AI model to function at the highest level of efficiency and accuracy, coupled with reliability and compliance, this piece is humongous. If you have given 2-3 decades for automation of processes, we probably need to give ourselves more time to see how AI will impact the business value. Nobody’s questioning AI’s impact, but in the context of evolving challenges and opportunities, we need to be a little patient. We reiterated that message today in our SAP Now event in Delhi about creating an autonomous enterprise, which is about looking at the function end-to-end, processes within it, and embedding AI in that process.

How can enterprises overcome fragmented and siloed data without replacing legacy systems, and how will SAP’s new launch help turn that data into measurable outcomes such as revenue growth, productivity, and faster decision-making?

SAP has always looked at technology in the context of business. SAP has been adopted significantly across enterprises cutting their sizes and across all industry verticals for two reasons. One, the knowledge of business processes, the industry differentiation a process may have, and the underlying data quality. Any financial data that comes from our systems is authentic and reliable. The fundamental principle of bringing a business AI to life is that it has to be relevant, reliable, and acted with a lot of responsibility. These are the three fundamental principles on which SAP’s entire business AI framework is based. It should take care of both structured and unstructured data.

We understand that there are data silos and tell our customers that if their processes are homogeneous, their underlying technology platform cannot be too heterogeneous or have too many silos. Those foundational principles are why, in consumer AI, which has structured data, you’re creating the right models that have shown efficiency.

But if the underlying business process is end-to-end, and these business processes and underlying data sets are well-defined, you start reducing the silos. You can take the external data and build intelligence in the context of business. This is where you’ll probably see almost every KPI getting impacted — KPI with respect to your sourcing parameters, supply chain efficiency, asset performance in asset-intensive industries, financial functions, and cash flow management. It has to be built brick by brick. Agents have to be deployed and communicate with each other. There has to be human capital in the loop to take care of functions, decisions, and judgments in consultation or in collaboration with the agents to create the right impact.

How does SAP view India as both an AI adoption market and an innovation and talent hub, and what does this mean for the future of jobs?

This is India’s moment on four counts — we have a lot of young talent who are hungry to perform and are passionate. That is an intrinsic advantage. GCCs are investing in India to tap into the talent pool. SAP India, or SAP Labs in India, is the second largest overall, or the largest engineering hub outside of our headquarters. Every organization, and SAP more so than ever, is heavily invested in tapping into the talent.

Look at the data volume and data explosion. AI engines and tools will only work when you have more credible data available. Suddenly, many nations are also seeing what India is doing and how we are building this model with a frugal mindset. We were mostly consumers of technology and services. Now, we are also creating, which is a fundamental shift. We don’t need to make comparisons, but instead leverage our strengths.

We have to keep re-skilling ourselves because the pace of change will be higher than today. For certain business functions that are more repetitive or need a more deterministic nature, AI will take over. The good part is, growth engines are coming in. It is about bringing together human talent and technology, including AI, to create new value and drive greater efficiency and effectiveness across businesses and communities. As long as we are open to reskilling ourselves, new roles — like forward deployment engineers — will keep emerging. If I can manage a contact center and take on more workloads in consultation with agents and human capital, that’s efficiency. It’s about moving up the value chain and bringing in intelligence and intuitive experience to work in conjunction.

Is SAP’s new Innovation Campus in Devanahalli driving new hiring, and how will its work differ from SAP’s existing campuses—particularly in terms of product development and AI?

It’s an extension of what we have in the country and globally as well. It’s all about engineering and support services. India has a large talent pool, which we are tapping into. We have kept on re-skilling our people. Every product that goes to the market, our engineers from India are also a part of it. And because we were over capacity in Whitefield, we came back with this center, with plans to keep expanding it.

When can an enterprise be considered truly AI-native rather than simply using AI, and is there a clear point at which AI becomes embedded across its core functions?

The adoption and consumption of AI will keep increasing dramatically year on year. There’s no end state to the challenges and opportunities coming our way. We need to see this like any other technology. Even today, customers typically use only 60–70 per cent of the capabilities offered by a technology platform, regardless of the organisation. From an AI standpoint, it is all about embracing more agents and creating more efficiency in business. This will be an incremental adoption and consumption of platforms. And the way we should measure ourselves is what kind of adoption and consumption is coming in with respect to technology and AI-infused technology.

How does SAP approach governance, regulation and cybersecurity around enterprise AI deployments, and how does it work with customers to address these concerns?

This is the fundamental premise. Two years back, when the AI Wave had just started, three philosophies or principles were articulated by SAP — AI has to be responsible, relevant, and reliable. In the last five decades that we have been in this business, we have been running mission-critical applications, infrastructures, and even important business and government functions. It is important that when we launch, we have taken care of all the areas around cybersecurity, physical security, reliability, and governance. In various countries, AI innovation and regulation will go hand in hand. SAP keeps an eye on everything happening across the globe. We are building systems based on the best practices from around the world, while meeting the standards and regulatory requirements of each country. We then build on these with relevant global best practices to deliver the strongest possible solution.