Artificial Intelligence: Sorting Hype from Reality

Is AI the next electricity or just another tech bubble? Experts weigh in.

Insights from Trishna Shah - Principal PM lead Microsoft, Prithvijit Roy - Lead &MD (Data & AI) Accenture, Himanshu Tambe - Clinical Professor Strategy, ISB.

In an era of ChatGPT, driverless cars, and smart everything, artificial intelligence (AI) dominates headlines and boardroom agendas. But is AI truly the next big leap-like electricity or the internet-or are we caught in another hype cycle?

SRITNE spoke to thought leaders from industry and academia explored this pressing question. Their conversation unpacks the promise, paradoxes, and practical hurdles of AI adoption, offering rare clarity in a time of both fervour and fear.

The Big Question: GPT or Gimmick?

Professor Anand Nandkumar: Is AI a general-purpose technology? Or is it simply the latest in a line of overhyped technologies that fade with time?

Trishna Shah: AI is absolutely a general-purpose technology. It's been embedded in everyday tools-spam filters, recommendation engines-for years. What's changed today is accessibility. Thanks to better data, computational power, and user-friendly interfaces, we're seeing a democratization of AI use cases."

Prithvijit Roy: A veteran of two decades in analytics, broke AI's evolution into phases-from predictive analytics to big data to machine learning and now, generative AI.
"What generative AI has done is consumerize AI. It's become part of everyday life. The potential to reshape industries is undeniable-but we're still scratching the surface."

Himanshu Tambe, An academic and AI entrepreneur, was cautiously optimistic.
"AI has the potential to become a GPT, but it's not there yet. The barriers are not just technical-they are  infrastructural, organizational, and economic. Data is the biggest bottleneck."

The Data Dilemma: Infrastructure vs. Integrity

Professor Anand Nandkumar: What is the role of a robust data infrastructure in the future growth of AI?

The panel agreed: the AI engine runs on data-but that fuel needs refining.

Trishna: Highlighted the gap between technological capacity and data quality.
"Data infrastructure has improved, but data sanctity-clean, relevant, usable data-is still a major concern. Especially in enterprise applications, like building reliable chatbots or healthcare tools."

Prithvijit Roy Pointed out how the definition of data has changed.
"Everything is data now-images, audio, video. But, collecting and organizing it at scale is expensive. We are trying to solve these problems with AI itself-AI cleaning data for AI."

Himanshu: Brought a pragmatic, almost provocative view.
"Data sharing is a managerial problem. Organizations hoard data like power. In one project on domestic abuse, we could not even begin modelling because ministries would not share data. Unless institutions evolve, data will remain a constraint."

Applications & Ecosystems: Are We Moving Fast Enough?

Professor Anand Nandkumar: If AI is indeed powerful, why isn't it transforming productivity?

Prithvijit had an answer:
"Most enterprises are still piloting AI. There's a joke that some companies have more AI pilots than Air India. But slowly, we are seeing infusion into sales, business intelligence, anomaly detection, and more."

Trishna offered insights from healthcare:
"AI is already streamlining practitioner workflows, improving diagnostics, and personalizing wellness. Nuance, for instance, automates note-taking for doctors, freeing up time for patient care. But much of this is under the hood-it's not always visible to users."

The Productivity Paradox: Gains, Displacement & the Human Factor

Professor Anand Nandkumar: Why isn't all this innovation showing up in the numbers? It might be because every efficiency gain is offset by disruption.

Himanshu shared:
"There's downward wage pressure on high-skilled jobs and upward pressure on routine roles due to automation. The net effect? A reshaped middle class and compressed wage structures. Inequality might look different in an AI-powered world."

He emphasized the danger of over-trusting predictive analytics, particularly in sensitive areas like parole decisions or medical triage.
"We must be cautious. Prediction is not the same as judgment."

Closing Statements: A Technology Worth Waiting For

Professor Anand Nandkumar: Okay, what is your final take? Is AI a GPT?
Trishna:
"Yes-AI is a GPT, but it needs regulatory scaffolding to diffuse responsibly. We can't rely on natural adoption alone like with electricity."

Prithvijit:
"The short-term hype around generative AI overshoots its current value. But over the long term, the value will surpass the hype. What we need now is patient capital-more Microsoft's, more government-backed investment."

Himanshu:
"The technology is ready, but the infrastructure isn't. AI's success hinges on complementary innovations-especially data sharing-and a culture that embraces augmentation, not just automation."

The Verdict

AI is not just another tech buzzword. It's a powerful, evolving force with the capacity to redefine industries, labour markets, and global productivity. But for AI to cross the chasm from potential to purpose, we need more than innovation-we need patience, collaboration, and systems that reward long-term thinking.
Whether AI ultimately becomes our generation's dynamo or fizzles into over-promised disappointment depends not just on what it can do but on what we do with it.