Despite strong enthusiasm for artificial intelligence among Indian businesses, leaders emphasize the need to address critical issues around security, workflow redesign, and measurable returns before scaling AI solutions beyond pilot stages.
- 80% of Indian firms want AI integration this year; fewer than 30% feel ready
- Security and governance challenges are key barriers to wider AI adoption
- Industry calls for principles-based AI regulation rather than rushed rules
What happened
At the Economic Times AI Vantage roundtable in Mumbai, Indian corporate leaders shared insights on the current state of AI adoption. They noted widespread enthusiasm but pointed out that most organizations still face significant hurdles in scaling AI projects securely and profitably.
Speakers from major firms like Cisco, Reliance, and Angel One emphasized that AI implementation requires more than just the latest technology. Instead, successful adoption depends on securing data, redesigning workflows to fit AI tools, and ensuring ongoing governance and employee training.
Why it matters
With over 80% of Indian companies wanting AI agents integrated into their workforce this year yet fewer than 30% prepared in terms of security and infrastructure, the gap highlights vulnerabilities that could expose businesses to risks including data breaches and unreliable AI outputs.
Financial services in particular face heightened risks because a single AI failure can have significant consequences. This underscores the urgency to embed security and safety into AI systems right from the design phase, rather than as an afterthought, to protect customer data and maintain trust.
What to watch next
Industry leaders urge companies to evaluate business value thoroughly, redesign processes around AI capabilities, and train employees before scaling solutions. The growing trend of ‘bring your own AI’ introduces governance challenges as employees use consumer AI tools that may not comply with enterprise standards.
Regulatory experts recommend India adopt a broad, principles-based AI framework co-developed by industry and policymakers instead of rushing into rigid regulations. Accountability for AI failures remains an unresolved issue, making ongoing dialogue critical to foster trustworthy AI adoption.