Enterprises must move beyond AI experimentation and focus on solving real business problems, building domain expertise and empowering their workforce, said Sindhu Gangadharan, Managing Director of SAP Labs India.

Speaking at a fireside chat on “Leadership in the AI Era” at the India Advantage Summit 2026 on Wednesday, Ms. Gangadharan said organisations should stop viewing artificial intelligence (AI) merely as a technology upgrade and instead treat it as a transformation involving leadership, business processes and people.

She said the rapid advancement of AI has made technology development faster than ever, but the bigger challenge for companies is identifying the right problems to solve and deciding what solutions need to be built.

“AI is actually a leadership transformation that we need to look at,” she said, stressing that companies should begin with the outcomes they want to achieve rather than searching for problems where technology can be applied.

While AI has significantly reduced the time required to build solutions, she said, understanding business challenges, customer needs and industry-specific complexities remains essential. Deep domain knowledge and close engagement with customers are critical to identifying meaningful use cases, she added.

Ms. Gangadharan also highlighted the challenge businesses face in scaling AI initiatives beyond proof-of-concepts. She cited the example of an AI-powered billing agent developed for a global professional services company, which handles complex processes involving workforce deployment, regulatory requirements, legal considerations and pricing. The solution has helped reduce work previously managed by more than 1,000 executives, she added.

She pointed to other applications, including the use of multiple AI agents to transform vaccine manufacturing processes and AI-driven systems deployed across manufacturing plants to improve traditionally manual quality checks.

According to Ms. Gangadharan, organisations that successfully move from AI pilots to large-scale deployment are those that combine business understanding, domain expertise, technology capabilities and strong data foundations.

Stressing that “AI is only as successful as the quality of data available,” she said underlining the importance of access to reliable datasets and a robust technology architecture for enterprises seeking to harness AI effectively.