Despite billions invested globally, many enterprises are finding it difficult to move beyond AI pilot programs and basic productivity enhancements. While chatbots and AI-powered email drafting are common, achieving substantial operational transformation and a measurable return on investment (ROI) from artificial intelligence remains elusive for most organizations.
The Missing Layer: Operational Context
According to executives at Celonis, a company specializing in process mining, the core challenge lies in a critical missing layer: process intelligence. Malhar Kamdar, Chief Growth Officer at Celonis, explains that AI often fails to deliver on its promise at an operational level because it lacks a fundamental understanding of how a business truly functions. The necessary business context is frequently scattered across various systems, departments, and personnel, creating operational blind spots that hinder AI from scaling effectively across the enterprise.
Fragmented Workflows and Legacy Structures
A significant hurdle, Celonis leaders argue, is that companies often rush into generative AI (GenAI) adoption without first addressing underlying issues such as fragmented workflows and outdated operational structures. While approximately 95% of companies are experimenting with AI, most applications are confined to the productivity layer—automating minor tasks or summarizing documents. True transformation of complex areas like supply chains, finance operations, or core enterprise workflows demands that AI comprehends the intricate operational context.
Process Intelligence as the Solution
Celonis is championing a shift towards building operational visibility across organizations, moving beyond standalone AI tools. Their approach centers on a “context model”—a real-time digital twin of enterprise operations. This model provides AI systems with a comprehensive understanding of workflows, bottlenecks, and interdependencies across different departments and software platforms.
Dilipkumar Khandelwal, Celonis's Chief Customer Officer, emphasizes that enterprises are now transitioning from mere AI experimentation to a focus on quantifiable “return on AI.” By using process intelligence to gain an X-ray view of business operations, companies can pinpoint inefficiencies, automate processes, and deploy AI agents with the precise context needed to make ROI measurable. Celonis claims its global clients have generated over $25 billion in value through process intelligence deployments, with more than $10 billion already validated by customers.
India's AI Opportunity
India is emerging as a significant market for enterprise AI, driven by its robust Global Capability Center (GCC) ecosystem, a proliferation of digitally native startups, and a youthful workforce. Celonis views India as a strategic market alongside North America and Europe, having nearly doubled its headcount in the country recently. Kamdar suggests India may even have an advantage over more mature Western markets, as its enterprises often exhibit a greater willingness to embrace AI-led transformation without the inertia of deeply entrenched legacy systems.
AI as a Force Multiplier
Ultimately, the future enterprise will be AI-driven and composable, but AI cannot operate in isolation. It must seamlessly integrate with existing systems, workflows, and human decision-making processes. As Kamdar concludes, “AI is a force multiplier for humans, not a replacement.” Unlocking AI's full potential requires a foundational understanding of business operations, ensuring that technological advancements translate into tangible, measurable value.