Debojyoti ‘Debo’ Dutta admits that his deep domain experience means he’s only “a little biased” when it comes to the big topic of the day: the potential of artificial intelligence (AI) in the enterprise. However, his understandable amplification is balanced by a commitment to PPT – not PowerPoint, but the timeless triangle that encompasses people, process and technology as the three-pronged foundations for organizational success.
A computer science graduate and PhD holder, Dutta has amassed valuable experience in neural networks, expert systems, and other AI-related fields through research spanning India and California, with particular reference to its applicability to biological research. His exposure to the field has been ongoing for a long time and came to fruition earlier this year with his appointment as Chief AI Officer at Nutanix, where he is responsible for both making the cloud company a smarter consumer of technology and helping customers navigate this “ brave new world .”
Nutanix is a California-based software maker that enables organizations to manage and run their applications and services “anywhere”: on-premises, in the cloud, or at the edge of the network. It has grown rapidly since its founding in 2009 and is still afloat six years later. At the time of writing, it boasts a market cap of over $18 billion.
Nutanix has also carved out something of a reputation as a bellwether and barometer for change, leading the way in various trends like hyperconvergence (the flattening of data center storage, networking, and compute), virtualization, and ransomware protection. So Dutta is as good a source as anyone to talk about the real-world opportunities and challenges organizations face as they embrace AI.
Promise and reality
As a veteran of many tech trends, I asked Dutta if he’s always wondered whether what sometimes seems like the AI mania is real or inflated by industry hype.
“There’s something real about this wave that’s both real and surreal,” he says. “I wouldn’t have imagined two or three years ago [that things would grow so quickly].
For example, he cites the stunning progress in reasoning models—essentially large language models (LLMS) augmented by reinforcement learning, most famously in the example of Openai’s GPT series. Dutta says that rapid adoption of reasoning models can “override” change, but he adheres to this PPT principle, saying that opportunities can be wasted if the right workflows, skills, and cultures are not in place.
“The promise of technology is there, as seen in agents, but you need people, processes and technology,” he adds.
This is especially important in enterprise AI, where organizations are trying to combine the power of AI and ML on corporate systems. Agents working on private data will require significant human retraining. “Fine tuning” with “loophole” management will have to be rejected. “IT staff must become AI staff” is a benign refrain.
AI everywhere
Additionally, there will need to be a culture of mass adoption of common tools across all types of employees.
“If they’re not using AI in some way, there’s a lack of understanding, and you have a situation where there’s a lot of euphoria initially and then…[nothing] In the area of manufacturing and data science [to succeed], you have to eat your own dog food.” But in driving adoption, the “Jevons Paradox,” where technology efficiency is being leveraged to drive demand, may strike.
Back to the PPT triangle: as an AI leader himself, where does Dutta believe people like himself should sit on the organizational chart?
“The promise of technology is there, as seen in agents, but you need people, processes and technology”
Debojyoti Dutta, Nutanix
He says he believes in a matrix where the AI leader works closely with the CIO and CISO, but also with departments like legal, product development, and engineering. “You don’t want to reinvent the wheel, but this is an early stage of enterprise AI, and it lends itself to a virtual team,” he says. “The market is moving so fast that if you’re not skilled, you might not see the tectonic plates shifting, so AI leadership needs to rely on other advisors.”
Despite the early AI diagnosis, Dutta says Nutanix has seen real results internally, pointing to improved site reliability engineering systems. Lessons learned and shared with customers: Private data in particular needs to be clean, and governance guardrails are non-negotiable. A proof of concept can be built quickly with Openai or Gemini, but once the application is back on-premises, a governance structure needs to be in place. The real challenge isn’t finding the right tools, but aligning them with clean enterprise data, working hard with data science teams, and evolving accuracy over time.
Not everything is ready, he says, noting that AI-superpowered software development still requires human supervision and a deep understanding of prompts and complex domain logic design. But agents (and increasingly agent-based AI) are moving forward, and the only sensible approach is to encourage new thinking across colleges and the current workforce. Storage, networking, and other infrastructure will also need to be optimized, while quantum computing could be a powerful ally for the future.
In the meantime, Dutta says we can all benefit from having “digital minions” as assistants who can help with rote tasks, spot anomalies, and at least parse, compile, and create the first draft of complex documents. If we combine an enthusiasm for learning with this eternal need for human knowledge triangulation, digital innovation, and process engineering, then the future is bright.
