Ajay Pasuvula, Senior Vice President and Global SAP Ecosystem Leader at HCLTech, has over nine years of experience at the leading IT services company, including more than three years spearheading SAP ecosystem initiatives.
Under Pasuvula’s leadership, HCLTech has strengthened its long-standing partnership with SAP, marked by initiatives such as launching an AI Lab in Munich, Germany; developing AI training programs with SAP; and executing a comprehensive plan to certify 15,000 to 20,000 SAP consultants in generative AI. These advancements position HCLTech to help enterprises unlock the potential of AI in their SAP environments.
In an interview with ASUG, Pasuvula shared insights on the infusion of generative AI into the SAP ecosystem and discussed how HCLTech is helping businesses establish robust data foundations and optimize process architectures, enabling them to successfully leverage the full potential of AI within the SAP landscape.
This interview, which you can also download, has been edited and condensed.
Q: Why do you believe that generative AI will hold value for IT professionals? What makes it especially vital for the SAP ecosystem?
Over the past few years, we’ve seen a monumental shift in the IT industry with the rise of generative AI. It all started when ChatGPT became accessible to end-users with no IT knowledge. What makes it different, from an SAP perspective, is the ability to integrate processes with data and combining algorithms with large language models (LLMs).
SAP’s unique value comes from its vast customer base of 73,000 customers and its central role in global transactions. SAP’s position is unparalleled, with 450,000 customer data points across SAP S/4HANA, customer experience, HR, and five decades of process expertise. This breadth of data and process knowledge creates a significant advantage from a customer perspective.
Generative AI is reshaping models based on customer processes, data, and best practices as business models evolve and the industry faces disruption. SAP’s leadership is reinforced by its extensive experiences across approximately 23 industries, placing it in a prime position to offer highly relevant, context-driven solutions.
Q: In terms of that specific integration and the relevance of SAP solutions, what problems or hurdles do you see generative AI helping SAP-equipped enterprises overcome?
There are multiple ways SAP’s landscape helps customers transform their existing IT and business processes. Look at how SAP supports essential back-office functions, from order-to-cash, procure-to-pay, and hire-to-retire to comprehensive supply chain management; most of these processes rely on SAP data. Then, on the Operational Technology (OT) side, SAP integrates with systems like the manufacturing cloud and aligns with OT systems; in telecom, you’ve got the BSS and OSS integration. Given SAP’s pivotal role across all these industries, generative AI can significantly enhance process efficiency, identify bottlenecks, and recommend transformative solutions.
Now, all these LLMs in the market are pre-trained in industry best practices — we call them vertical LLMs — and these models must be customized using the company’s actual system of record data. This is where SAP holds a distinctive position, not just providing best-of-breed processes from its 50 years of experience but also integrating algorithms and training models based on real customer data. That’s hugely relevant from a customer perspective, as SAP can think, listen, and act based on their unique data and recommend new processes.
For example, while LLMs excel at cognitive abilities like hearing, visualizing, and summarizing, they often lack business context. How does a company apply AI to improve supply chain efficiency in a customer’s real-world situation? That’s where SAP differentiates itself. By combining LLM capabilities with tabular functionalities and a knowledge graph, SAP ensures that its solutions are not only advanced but highly relevant to each customer’s needs.
Q: What are you seeing in terms of customer perception of generative AI? As we look ahead to 2025, are organizations recognizing its real value or still caught in the hype?
Many customers in the manufacturing and automotive sectors often ask us how to effectively adopt business AI, especially since SAP is at the core of their digital transformation strategy. When they approach us, my first question is, “Is your SAP landscape aligned with your digital transformation goals?” And 99% of customers say, “Absolutely. We want SAP to be at the heart of our large digital transformation projects.”
My second question concerns how stable their SAP environment is in terms of standardization, fit-to-standard, clean core, and interface strategy. Adopting new technologies like AI requires a solid infrastructure, including a unified data model. You also need a clean, lean core that seamlessly integrates your SAP and non-SAP landscape.
If you’ve got these three layers structured and scalable, infusing AI into your data makes sense. But if you’ve poor data quality and a weak strategy, even the best LLMs and pre-trained models won’t help. In such a case, you’ll experience hallucinations — where the quality of output will not match the quality of the input. This is why SAP’s standard reference architecture is essential for helping customers infuse AI properly.
SAP’s AI strategy is particularly intriguing because it does not limit itself to a single LLM provider, such as OpenAI or Llama. Instead, it selects the best LLM for each specific process. For example, infusing AI in SAP SuccessFactors differs completely from applying it in the supply chain. This flexibility helps customers extend their out-of-the-box processes. SAP’s systems even suggest which LLM best fits a given process, as the company has rigorously tested them all.
Take ABAP chat, for example. While we call it ChatGPT, SAP uses MySQL AI on the backend for code remediations and functionality, much like how HANA GPT is implemented. What’s crucial here is that SAP continuously refines which LLM is best suited for each specific process. Understanding this is key for customers to fully adopt SAP’s offerings.
Q: Could you talk about what advantages the HCLTech and SAP partnership are bringing to clients looking to leverage generative AI in this area?'
HCLTech and SAP have a longstanding and deeply collaborative partnership. We’re one of the largest Global Strategic Services Partner (GSSP) partners and the first to embrace RISE with SAP as a reference architecture. Our entire landscape runs on SAP, including Ariba, SuccessFactors, Concur, Fieldglass, RISE with SAP, and BRIM. We’re launching a strategic initiative on total workforce management, where we are building a skill development module with SAP. With 225,000 employees, we will explore generative AI use cases firsthand for workforce management.
Our unique position as both a customer and a partner sets us apart. Based on our experiences, we offer genuine, real-time feedback to SAP and other customers. As a reference customer for RISE with SAP, we can speak to customers directly and transparently about what works and what doesn’t, providing unbiased insights because we’re actively using the solutions ourselves.
At a corporate level, HCLTech is investing in three key areas: data, AI and SAP. Our CEO announced on Investor Day that SAP will be a significant strategic focus for us over the next five years. One of our first significant investments is the AI Lab in Munich, in collaboration with SAP Labs and SAP AppHaus. The lab enables customers to explore their generative AI roadmap, engage in design thinking, build MVPs, and assess the potential for their organizations. We’re also ensuring that all our SAP consultants — 15,000 to 20,000 professionals — are certified in SAP generative AI. We’ve developed a comprehensive plan with SAP, focusing on industries where we have a strong presence, such as aerospace, utilities, manufacturing, life sciences, and retail. We’re looking at each customer’s needs, finding gaps in SAP’s roadmap and bringing innovative solutions to the edges of their business processes.
With RISE, the coming years are set to be transformative. We’ve signed a strategic Migration Factory deal with SAP’s customer services team, recognizing the need for 625,000 SAP consultants for migration over the next three years — an impossible demand. We’re integrating human-bot interactions into RISE with SAP methodology to address this. We’re reducing human dependency while keeping humans in the loop, helping our customers accelerate their migrations and business transformations.
Connect with Ajay Pasuvula on LinkedIn.
Q: What’s your assessment of SAP’s current generative AI strategy? What’s working, what isn’t, and how is HCLTech helping to shape their approach?
SAP’s AI strategy needs clearer, simpler messaging to help customers understand its value in a rapidly evolving tech landscape. While SAP highlights impressive figures, such as 23,000 customer consensus points and 450,000 plus customers’ data and processes, the challenge lies in translating these complex capabilities into something tangible for customers. SAP must take this down to the basics, showing what infused AI will look like and outlining a clear, actionable roadmap for the future.
The generative AI roadmap cannot be treated like a regular product roadmap. Customers won’t wait two or three years for AI features to be enabled in SuccessFactors or Ariba. They need AI to move faster and be integrated now — not as another distant feature but as a core component of SAP’s offerings. The market is evolving rapidly, and SAP must accelerate its AI initiatives or risk falling behind, especially with the rise of vertical solutions from hyperscalers who see data as a universal resource, with SAP as just one piece of the puzzle.
SAP can’t afford to lose its advantage by moving slowly. It needs to get AI into its product roadmap quickly, or it will lose the battle to the bigger hyperscalers.
However, SAP has a distinct advantage. They aren’t just relying on LLMs; they’re building their knowledge graph and large tabular models (LTMs) using their vast data. All these investments combine unstructured and structured data, making them unique in their business context.
A great example is the travel expense management demo, where everything flows seamlessly from email generation to booking portals. With this seamless, infused AI strategy, SAP is headed in the right direction, but SAP also needs to accelerate its efforts to stay ahead in the competitive AI landscape.
Q: Can you explain the AI Force Initiative and its relevance to this overall HCLTech strategy around AI?
AI Force is how we drive AI innovation into our customers’ businesses, focusing on three key areas. The first is service transformation, which accounts for 80-85% of our business. This includes service transformations and operations across various regions — SAP S/4HANA migrations, cloud migration, application maintenance, engineering services, and infrastructure operations. We aim to embed AI into everything, making our consultants more productive and smartly reducing manual tasks.
The second area is industry-specific, repeatable solutions. With the rise of vertical and industry-specific LLMs, we’re identifying gaps where existing LLMs or even SAP haven’t fully addressed the entire process chain. We’re developing solutions that can work across multiple industries, not just isolated cases, creating scalable, repeatable value for our customers.
The third area is cloud-native applications. We’re integrating generative AI into all our infrastructure services and cloud-native applications, with support from AI Foundry and Cloud Native Labs. Among all our labs, our SAP AI lab stands out as the only independent-software-vendor (ISV) lab, highlighting our strategic priorities.
Q: Any parting thoughts for the SAP community?
Before diving into new technologies, make sure your data strategy is solid. Data is essential for driving innovation at the edge. Take RISE with SAP, for example — it has worked for us because we’ve laid the right foundations: a strong data strategy, a robust infrastructure strategy on Azure, and a clear integration strategy.
"It’s simple — get your data strategy right, establish your reference architecture, and then integrate AI. That’s how you achieve meaningful results."
With these foundational pieces and a clean core, we can better predict outcomes when we extend our systems. It’s simple — get your data strategy right, establish your reference architecture, and then integrate AI. That’s how you achieve meaningful results.
Visit the HCLTech website.