A major challenge is beginning to surface for enterprises beneath the AI momentum; despite large-scale investments, many organisations are still struggling to operationalise AI meaningfully and generate measurable business value.
From supply chains and finance to customer onboarding and enterprise operations, businesses are increasingly realising that AI alone cannot solve fragmented operational environments. The bigger challenge lies in giving AI systems the operational context, process visibility, and orchestration intelligence needed to function effectively at enterprise scale.
In exclusive interactions with Express Computer, Malhar Kamdar, Chief Growth Officer, and Gunther Rameseder, SVP Global Value Engineering & Business Apps, Celonis discuss why process intelligence is becoming central to enterprise AI adoption, how India is evolving into a strategic innovation market, and why context-aware orchestration could define the next phase of enterprise transformation.
Enterprises are adopting AI rapidly, but operational deployment remains limited
According to Kamdar, enterprises globally are entering a new phase of AI adoption where the focus is shifting from experimentation towards operational deployment and measurable ROI.
“What we are seeing is every business in every industry is being reshaped by AI,” says Kamdar.
However, he points out that while organisations are aggressively investing in AI initiatives, enterprise-scale deployment still remains limited.
“There’s a big gap between the incredible movement to adopt AI and the actual operational deployment of AI at enterprise scale. And most importantly, the return on investment of AI is yet to be seen,” Kamdar notes.
He also believes the core problem is that AI systems still lack a deep understanding of how businesses actually function.
“AI has operational blind spots. AI does not naturally understand how business truly works,” he says. Also, enterprise knowledge today remains fragmented across departments, systems, databases, and people.
This is where process intelligence becomes important because it provides operational context that helps AI systems reason more effectively across enterprise environments.
Why India has become strategically important
Kamdar makes it clear that India is no longer viewed simply as a delivery market.
Over the last two years, the company has elevated India into an independent strategic market alongside North America, EMEA, and Asia Pacific.
A major reason behind this, according to Kamdar, is India’s rapidly growing GCC ecosystem.
“India is our future. It is home to 2000-plus GCCs. No other country has this. By definition, those GCCs are the epicentre of process and process intelligence,” he adds.
He reveals that the company is already working with more than 260 GCC customers in India, many of whom are now driving global AI and transformation initiatives from India itself.
“All the large technology and services players have some of their largest talent force here in India globally. They are driving AI transformation and innovation from India,” Kamdar adds.
Why enterprises struggle to move AI from pilot mode to production
Kamdar believes the answer lies in missing business context.
“The reason why AI is not being deployed at enterprise scale is because of the missing piece, and the missing piece is context. That’s why our tagline is ‘context matters’, Kamdar says.
According to him, enterprises need a live operational understanding of how processes actually function across departments and systems.
“Imagine a platform for any organisation that provides a live, unbiased, system-agnostic view of how a process really works. That’s what a context model is,” he explains.
He also adds that this gives organisations the ability to understand historical performance, monitor current operations, and predict future outcomes simultaneously.
He also argues that enterprises often overestimate AI’s autonomous reasoning abilities. “The difficulty in moving from pilot to operationalisation is because as soon as you ask the agent to do hard things, it struggles,” Kamdar observes.
However, if AI systems are provided the right operational context, they can reason more effectively and make better decisions, he points out.
Process intelligence is becoming central to enterprise orchestration
According to Rameseder, enterprises are only beginning to understand the true value of AI. He notes that many current AI deployments remain limited to coding assistance, email generation, or basic productivity tasks.
However, the real value emerges only when AI becomes embedded inside operational business processes.
“The true value from GenAI comes when you embed AI into your business processes,” Rameseder says.
This includes workflows such as invoicing, customer onboarding, manufacturing operations, supply chain management, and product development.
“To understand where your business is broken, you need process intelligence,” he adds.
Rameseder also cautions against deploying AI blindly into workflows that are already functioning efficiently.
According to Rameseder, the enterprise AI conversation itself is shifting beyond language models towards operational context and orchestration.
GCCs are evolving into strategic enterprise transformation hubs
Rameseder believes process standardisation has become foundational for resilience, automation, and AI scalability. “If your processes are not standardised, not documented, not orchestrated, and not simplified, you cannot be resilient at all. You’re losing money. It’s like a leaking bucket,” Rameseder adds.
He also points out that GCCs are rapidly evolving beyond traditional back-office functions.
“It’s not only the back office anymore. It’s not only finance or IT operations. It’s R&D. It’s some of the most important, most strategic, value-adding functions,” he notes.
He reminds that GCCs are increasingly becoming responsible for simplifying and standardising enterprise-wide operations globally.
Enterprises are prioritising resilience, agility, and customer outcomes
Rameseder observes that organisations today are moving beyond traditional cost optimisation metrics.
Instead, enterprises are increasingly prioritising resilience, customer satisfaction, agility, innovation, and operational responsiveness. The operational pain points, however, vary across industries.
In banking, customer onboarding speed is becoming a major competitive differentiator.
“There are digital banks where you can open an account in 15 minutes. And traditional banks where it might take days or weeks,” he points out.
In manufacturing and energy sectors, global disruptions are creating large-scale supply chain challenges.
But increasingly, enterprises are also dealing with workforce readiness and talent availability challenges. “Do you have enough skilled workers to produce these very complex machines? You also need to think about talent acquisition,” he adds.
Enterprise architecture itself is being redesigned for the AI era
Kamdar believes CIOs are entering one of the most difficult transformation phases in enterprise technology history.
“They see their business is adopting AI from all over the place,” Kamdar says.
Because AI tools are now accessible even to non-technical users, business teams themselves are increasingly experimenting independently.
“You don’t need to be a technology expert anymore. Every user can do it,” he notes.
This, according to Kamdar, is fundamentally changing the role of enterprise architecture. “I think CIOs are facing the challenge of reinventing the enterprise architecture of the future,” he says.