Thehindubusinessline iconThehindubusinesslineSep 13, 2026 ~7 min source read

SAP’s three Rs: Responsible, Relevant, Reliable AI for enterprise scale

Manish Prasad of SAP India explains why enterprises must treat AI as an operational capability built on trusted data, end-to-end processes, and human oversight to move from pilots to production.

SAP’s three Rs: Building responsible, relevant and reliable AI for enterprises

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Useful takeaways from this story.

Enterprises need trusted data across end-to-end processes before AI can deliver consistent value at scale.

SAP’s AI approach rests on three principles — relevant, reliable, responsible — applied to both structured and unstructured data.

Scaling AI requires interoperable platforms, agents that communicate, and human judgment in the loop for decisions and governance.

The useful part

Building responsible, relevant and reliable AI for enterprises The adoption and consumption of AI will keep increasing dramatically year on year, says Manish Prasad, President & Managing Director. One is consumer AI, in terms of productivity, point solutions, and human capital in terms of technical work. 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.

How it works

  • At an enterprise level, there are different complexities, like accuracy and end-to-end processes, where how businesses operate is seamless.
  • You look at these processes end-to-end and realise that the data volumes and the data sets are the fundamental inputs.
  • 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.
  • 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...
  • One, the knowledge of business processes, the industry differentiation a process may have, and the underlying data quality.

What to take from it

We have to keep re-skilling ourselves because the pace of change will be higher than today. Because we were over capacity in Whitefield, we came back with this center, with plans to keep expanding it. We don't need to make comparisons, but instead leverage our strengths.

Example or evidence

  • You can take the external data and build intelligence in the context of business.
  • Suddenly, many nations are also seeing what India is doing and how we are building this model with a frugal mindset.
  • We then build on these with relevant global best practices to deliver the strongest possible solution.
  • Comments Published on September 13, 2026 READ MORE THIS AD SUPPORTS OUR JOURNALISM.

Details worth keeping

Building responsible, relevant and reliable AI for enterprises. The HinduBusinessLine SENSEX 72,480.29 -48.78 NIFTY 22,620.45 -95.75 CRUDEOIL 8,735.00 + 57.00 GOLD 147,900.00 + 1,668.00 SILVER 225,610.00 + 160.00 SENSEX 72,480.29 -48.78 NIFTY 22,620.45 -95.75 NIFTY 22,620.45 -95.75 CRUDEOIL 8,735.00 + 57.00 CRUDEOIL 8,735.00 + 57.00 GOLD 147,900.00 + 1,668.00 THIS AD SUPPORTS OUR JOURNALISM. SAP Indian Subcontinent, SAP India By Sanjana B Venkatesha Babu Updated.

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  • Projectmanagertemplate: Responsible AI becomes meaningful when principles are converted into operational capability.
  • Sap: The two companies are helping shape and strengthen the execution layer for enterprise agentic AI.
  • Sap: Enterprises must know whether they can trust the environment in which an agent operates.

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