‘If You Don't Have the Right Data, Then AI is Meaningless’

SAP’s footprint in India is large enough to support multiple developments across its portfolio, backed by partnerships with Mahindra & Mahindra, Asian Paints, Wipro, Infosys, Vahdam, Ola, Wakefit, DeHaat, Jaquar and others.

In an exclusive interview with AIM, Michael Ameling, president of SAP Business Technology Platform and member of the extended board, says the story of enterprise AI started long before the current hype wave.

Ameling’s two-decade journey at SAP spans radio frequency identification (RFID) experiments, early cloud services and the company’s integration architecture. But the shift he sees today is centred on one idea: AI without the right data is meaningless.

“If you don’t have the right data, then AI is meaningless. Just like machine learning—if you don’t have the right training data, you cannot achieve your goal.”

This belief has shaped SAP’s architectural bets, especially its effort to position HANA Cloud and the Business Data Cloud (BDC) as a unified “database for AI”, a platform where structured, unstructured, vector and graph data co-exist without losing context.

Why Databases, Not Models, Define Enterprise AI

Ameling is direct about why SAP chose not to build its own large language model. The goal was flexibility, not competing in a model arms race. “We said, let’s build an architecture where we can exchange the model, partner with the best vendors, and keep maximum flexibility. That strategy paid off.”

That flexibility sits in SAP’s data layer. According to him, enterprise differentiation comes not from the model but from the context preserved inside business data.

He pointed to the industry’s habit of copying data across lakes and silos: “Companies copied their data into a lake, rebuilt everything, added huge integration costs—sometimes storing the same data three times in different silos.”

BDC was created to counter this, offering a federated layer that avoids copying and preserves semantics across finance, procurement, HR and supply chain data.

SAP has also invested heavily in HANA Cloud. “I would claim this is a database for AI, because in a single database you have so many different engines—vector, graph, document store and relational store.”

This multi-model approach allows retrieval, reasoning, analytics and transactional workloads to run without splitting the data stack. Some SAP customers are already building AI-driven sales order optimisations directly through the integration of HANA and BDC.

Partnerships with Snowflake and Microsoft Fabric are expanding BDC’s connectivity and reinforcing the company’s open-data philosophy.

India: The Engineering Centre Behind SAP’s AI Data Stack

Ameling is unequivocal about India’s importance, considering many of the core capabilities behind SAP’s data and AI architecture are built here. “A large portion—everything I’ve talked about—is delivered out of India.” This includes HANA Cloud enhancements, anomaly-detection features in the integration suite and several of SAP’s new AI agents. The India teams now cover everything from Kubernetes workload distribution to applied AI use cases.

He said, “We have the right talent… We can build full end-to-end solutions, from deep technical work to delivering AI use cases in hackathons.”

India is not just building SAP’s AI stack; it is adopting it faster than global peers. About 93% of Indian enterprises expect AI ROI within three years, and they invest more aggressively than the global average.

"An average business in India is spending 31 million dollars on AI,” he said. From startups to conglomerates, adoption spans procurement, HR, design, operations and sales.

While JK Cement used BDC and SAP’s business AI to cut procurement processing time by 50%, ABB deployed Joule for real-time insights. Meanwhile, Wipro uses it to support consultants in client engagements.

To Ameling, India is both a builder and a beneficiary of SAP’s “database for AI” vision.

A Future Built on Context, Not Just Compute

As the world moves towards agentic systems and autonomous enterprise processes, Ameling believes SAP’s enduring differentiator will be business context.

He highlights SAP’s deep understanding of how enterprise processes interconnect—and how its knowledge-graph layer captures that logic.

This allows customers to use their data with immediate context, tailored to their functional needs. And much of that intelligence is being engineered—and rapidly adopted—in India.

“India has a very bright future… For us, it’s a very, very important market," he said.

In Ameling’s view, SAP’s AI era won’t be defined by a single model or agent but by the database that holds the world’s business logic, and by the countries building it at scale. India, he believes, is already central to both.