Enterprise AI has moved far beyond being just a buzzword, but for many companies, the real challenge now is figuring out what to actually do with it. During my conversation with Raj Koneru, CEO and Founder of Kore.ai, at the India AI Impact Summit 2026 in New Delhi, the discussion wasn’t just about flashy AI demos or futuristic promises. Instead, it focused on how enterprises are trying to turn AI into something practical and useful. Kore.ai has quietly built its reputation in this space by helping large organisations adopt AI at scale, and Raj’s insights gave a clear picture of why enterprise AI adoption is both exciting and messy at the same time.
Kore.ai, according to Raj, has been working on enterprise AI long before the recent generative AI boom. ‘Kore.ai is a 12-year-old company with an enterprise AI application platform. We built our platform and applications out of India and provide them to large enterprises in the US and globally,’ he explained. What stood out in the conversation was how the company sees itself not just as a chatbot maker or a tools provider, but as a platform that sits at the centre of enterprise workflows.
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In simpler terms, Kore.ai is trying to become the layer that helps businesses build AI-powered assistants, automate processes, and improve how employees and customers interact with systems. Enterprise AI, as Raj described it, is already one of the largest areas of AI adoption today, but that doesn’t mean companies have figured everything out.
One of the biggest problems enterprises face, he said, is not the lack of technology but the lack of clarity. ‘AI is available to everybody, so everyone is trying something. But not everyone is choosing the right use case,’ Raj said. That single line sums up a lot of what’s happening right now. Companies are rushing to adopt AI because they feel they need to, but many are still experimenting without a clear roadmap.
He pointed out that organisations are slowly starting to understand where AI actually adds value. Customer service automation, employee productivity tools, and certain process automation tasks are becoming early focus areas. However, the journey isn’t straightforward because the knowledge gap is real. ‘Nobody learned AI in college or school. People are learning it on the job, and the technology itself keeps changing,’ he added.
What this means in practical terms is that enterprises are learning while building. Teams are experimenting with models, tools and workflows at the same time, which often leads to confusion. Raj compared the current state of AI adoption to a phase where everything is happening at once, and businesses are still figuring out what works and what doesn’t. Over time, he believes companies will become more disciplined about choosing the right problems to solve with AI.
The conversation naturally shifted to agentic AI, a term that has been gaining attention lately. Many people assume AI agents will soon operate completely on their own, making decisions without human involvement. Raj pushed back against that idea. ‘There is this belief that AI agents will make decisions autonomously and humans won’t be involved. It’s going to be a combination,’ he said.
According to him, agentic AI does introduce reasoning and decision-making abilities, but that doesn’t mean businesses can remove humans from the loop. Some decisions can be automated, especially repetitive or low-risk ones, while others will still require human judgement. The real challenge for enterprises is identifying where that boundary lies. In simple words, the future is not about AI replacing people, but about figuring out which tasks humans no longer need to handle manually.
Another interesting part of the discussion was how enterprises are measuring the value of AI. Right now, many AI projects are still seen as cost centres. Companies invest in automation to reduce expenses, improve efficiency or handle large volumes of work. But Raj believes the long-term value of AI will come from revenue generation rather than just cost savings.
‘Revenue creation is starting to happen in areas like customer acquisition and cross-selling,’ he explained. Instead of just reducing operational costs, AI can help companies identify new customers, personalise recommendations and suggest additional products or services. However, he was clear that this shift is still in its early stages. Most organisations are only beginning to see how AI can directly contribute to growth.
From a broader perspective, the conversation highlighted a more realistic view of enterprise AI. Despite the hype around generative AI and agents, many businesses are still working through the basics. They are learning how to manage data, integrate AI into existing systems, and train teams to use new tools effectively. Kore.ai’s approach seems to focus on building structured platforms that bring order to this experimentation phase.
What became clear is that enterprise AI adoption is less about sudden transformation and more about gradual evolution. Companies start with automation projects, learn from small wins, and slowly expand into more complex use cases. Raj’s perspective suggests that the next few years will be less about flashy announcements and more about refining how AI fits into everyday business operations.
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