Drawing long-term value from artificial intelligence is not just about deploying more AI models but ensuring the scaffolding underneath is robust by redesigning workflows, according to Rishi Aurora, Managing Partner, IBM Consulting India & South Asia. He tells Poulomi Chatterjee why Indian enterprises struggle to move past pilot programmes. Excerpts:
What separates BFSI firms that are successfully scaling AI adoption from those stuck with pilots?
The difference is rarely the AI model. It’s the operating model around it. BFSI enterprises scaling AI successfully are the ones treating AI as a business transformation agenda rather than a technology initiative. They are moving beyond isolated use cases and redesigning end-to-end workflows across customer servicing, risk operations, compliance, fraud management and technology modernisation. What distinguishes these enterprises is their willingness to invest in the foundations required for scale.
How are consulting roles within IBM being trained and upskilled ?
Clients today want consultants and partners who understand their industry, can navigate complexity and help translate AI investments into tangible business outcomes. At IBM, our focus is on developing consultants who combine industry expertise, business acumen and technology fluency.
Platforms such as IBM Enterprise Advantage give consultants access to AI-powered assets, accelerators and best practices that help them solve client challenges while building practical AI expertise in the flow of work. At the same time, our Pi-shaped skills approach encourages consultants to pair deep domain knowledge.
What gives IBM an advantage over competitors in AI consulting?
IBM’s advantage is that we’ve been on the same transformation journey many of our clients are navigating today. Through our Client Zero journey, we’ve applied AI across our own enterprise to reimagine how work gets done, generating more than $4.5 billion in productivity benefits while transforming key business functions at scale. That experience gives us a practical understanding of what it takes to move from experimentation to enterprise adoption.
Which AI agent use cases are delivering tangible business value for banks today?
The first wave of AI in banking was largely about prediction and insight. The next wave is about execution. Across BFSI, we are seeing AI agents move beyond assisting employees to actively orchestrating workflows, managing exceptions and helping banks handle increasingly complex, high-volume operations. Today, some of the strongest value is emerging in fraud detection, cybersecurity, KYC and AML, where the combination of scale, regulatory intensity and operational complexity makes them ideal candidates for agentic AI.
What’s particularly interesting is that AI is evolving from an analytical tool into an active participant in business processes.
How has the focus on AI sovereignty changed how Indian enterprises choose AI models?
A year ago, most conversations were centred on model capabilities. Today, many enterprise leaders are equally focused on questions of control, trust and long-term flexibility. Increasingly, enterprises are recognising that digital sovereignty is a strategic advantage. The ability to determine where data resides, how AI systems are governed and which models can be deployed gives enterprises greater confidence to scale AI across critical business processes.
As a result, the discussion is shifting away from choosing a single LLM and towards building open, flexible AI architectures that preserve choice, support evolving regulatory requirements and avoid unnecessary lock-in. The organisations that get this right will be better positioned to innovate while maintaining the trust and resilience needed at enterprise scale.