Archive · 19 Aug 2026
Why Indian Enterprises Struggle to Scale AI Beyond Pilots
Many companies try small AI experiments but struggle to use AI across their whole business.
Rishi Aurora says the main challenge is usually not the AI model itself.
Companies also need to redesign how their workers and systems operate.
Banks are using AI in areas such as fraud detection, cybersecurity, customer checks and anti-money-laundering work.
New AI agents can do more than make predictions because they can help manage tasks and workflows.
IBM is training consultants to understand both business problems and technology.
IBM says its own use of AI has produced more than $4.5 billion in productivity benefits.
Indian companies are also looking for AI systems that give them more control over data, governance and model choices.
Rishi Aurora says successful BFSI companies treat AI as a business transformation agenda, not just a technology initiative.
Banks that scale AI redesign end-to-end workflows in servicing, risk, compliance, fraud management and technology modernization.
IBM is training consultants to combine industry expertise, business understanding and technology fluency.
Aurora says AI agents are creating value in fraud detection, cybersecurity, KYC and AML by managing workflows and exceptions.
Indian enterprises are increasingly prioritizing data control, governance and flexible AI architectures over choosing a single large language model.
- Who
- Rishi Aurora, Managing Partner at IBM Consulting India & South Asia, discusses enterprise AI adoption and consulting.
- What
- The interview examines why Indian enterprises struggle to scale AI beyond pilots, how AI agents are being used in banking, and why AI sovereignty matters.
- When
- The interview does not provide a specific publication date; Aurora refers to a shift occurring over the past year.
- Where
- The discussion focuses on Indian enterprises and BFSI companies, with reference to IBM Consulting India & South Asia.
- Why
- Enterprises need stronger operating models, redesigned workflows and flexible governance foundations to turn AI investments into lasting business value.
- Interviewee
- Rishi Aurora, Managing Partner, IBM Consulting India & South Asia
- Main scaling obstacle
- Weak operating models and foundations around AI, rather than inadequate AI models
- Key banking use cases
- Fraud detection, cybersecurity, KYC and AML
- IBM productivity benefit
- More than $4.5 billion through its Client Zero journey, according to Aurora
- Consultant training focus
- Industry expertise, business acumen and technology fluency
- Sovereignty priorities
- Data location, AI governance, model choice and reduced vendor lock-in
- Preferred architecture
- Open and flexible AI architectures that preserve choice and support changing regulations