ServiceNow’s India MD Kulmeet Bawa cautions that legacy IT infrastructure, often plagued by siloed data, risks failure in AI deployment. To address this, the firm launched new AI-governance tools, highlighting a shift toward mandatory data infrastructure over rapid, unchecked automation.
ServiceNow’s India Managing Director Kulmeet Bawa has issued a caution for legacy information technology firms, suggesting that many are heading toward operational chaos as they attempt to integrate artificial intelligence into outdated infrastructure. The warning comes as businesses race to adopt AI, only to find that their existing, siloed systems—built over decades—are unable to support the complex data needs of modern models.
The problem often stems from the fragmented nature of legacy enterprises. For many large firms, years of mergers, acquisitions, and disconnected software rollouts have created deep data silos. When companies try to layer advanced AI agents on top of these unorganised foundations, the results are often dysfunctional rather than innovative. Bawa explains that this rush to deploy technology without first cleaning up the underlying data architecture is a significant misstep that can lead to systemic failure in IT operations.
To address these gaps, ServiceNow recently introduced new solutions, including the AI Workflow Factory and the Autonomous Engineer, launched on October 6, 2026. The company is positioning itself as an AI control tower, arguing that effective deployment is impossible without centralized governance. The strategy marks a shift in the corporate narrative: companies are being urged to move away from rapid, unchecked scaling of AI agents and toward building robust, compliant data frameworks first.
Market data indicates that the challenge is widespread. Currently, only about 22 percent of Indian enterprises possess the governance frameworks necessary to match their ambitions for AI investment. Without this foundational structure, firms risk wasting significant capital on AI projects that fail to deliver a measurable return or, worse, violate data sovereignty and regulatory requirements set by bodies like the Reserve Bank of India.
For investors and industry observers, the focus is shifting toward how quickly companies can transition from AI experimentation to building disciplined, AI-ready infrastructure. The primary monitorable in the coming quarters will be whether enterprises prioritize heavy investment in data governance and workflow integration as a precursor to large-scale AI automation, or if they continue to face execution delays and efficiency bottlenecks.