{"id":252,"date":"2025-07-25T05:50:36","date_gmt":"2025-07-25T05:50:36","guid":{"rendered":"https:\/\/blogs.isb.edu\/sritnetechtalks\/?p=252"},"modified":"2025-12-31T09:46:17","modified_gmt":"2025-12-31T09:46:17","slug":"the-great-ai-debate-a-transformative-force-or-an-overhyped-trend","status":"publish","type":"post","link":"https:\/\/blogs.isb.edu\/sritnetechtalks\/2025\/07\/25\/the-great-ai-debate-a-transformative-force-or-an-overhyped-trend\/","title":{"rendered":"The Great AI Debate: A Transformative Force or an Overhyped Trend?<\/strong>"},"content":{"rendered":"\n

In an era where terms like \u2018ChatGPT,\u2019 \u2018Gemini,\u2019 \u2018driverless cars,\u2019 and \u2018automated everything\u2019 dominate both headlines and boardroom discussions, the pervasive question remains: Is Artificial Intelligence (AI) the transformative force akin to electricity or the internet – or are we merely witnessing another iteration of a cyclical tech bubble?<\/p>\n\n\n\n

SRITNE, ISB recently convened a panel of distinguished thought leaders\u2014Trishna Shah, Principal PM Lead at Microsoft; Prithvijit Roy, Lead & MD (Data & AI) at Accenture and Himanshu Tambe, Clinical Professor of Strategy at ISB – to dissect this pressing question. Their insightful exchange offers much-needed clarity amidst the fervent optimism and underlying apprehension surrounding AI’s future.<\/p>\n\n\n\n

Moderated by Professor Anand Nandkumar<\/strong>, the discussion also dissected AI\u2019s promise, pitfalls, and pragmatic challenges. Here\u2019s what emerged.<\/p>\n\n\n\n

AI\u2019s Promise: General-Purpose Technology or Overhyped Tool?<\/strong><\/h5>\n\n\n\n

AI\u2019s proponents argue it is a general-purpose technology (GPT) – a foundational innovation like electricity that reshapes economies. Skeptics, however, warn of inflated expectations.<\/p>\n\n\n\n

Trishna Shah offered a compelling case for AI’s transformative capacity:<\/p>\n\n\n\n

“AI is absolutely a general-purpose technology. It’s been embedded in everyday tools\u2014spam filters, recommendation engines\u2014for 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.”<\/p>\n\n\n\n

Prithvijit Roy expanded on this, highlighting how generative AI has brought the technology into mainstream consciousness:<\/p>\n\n\n\n

“What generative AI has done is consumerize AI. It’s become part of everyday life. The potential to reshape industries is undeniable\u2014but we’re still scratching the surface.”<\/p>\n\n\n\n

Himanshu Tambe, however, struck a more cautious note, emphasizing the work still needed:<\/p>\n\n\n\n

“AI has the potential to become a GPT, but it’s not there yet. The barriers are not just technical\u2014they are infrastructural, organizational, and economic. Data is the biggest bottleneck.”<\/p>\n\n\n\n

The Data Dilemma: AI\u2019s Most Persistent Challenge<\/strong><\/h5>\n\n\n\n

While AI models grow increasingly sophisticated, their effectiveness remains heavily dependent on high-quality, accessible data\u2014a hurdle the panelists agreed is far from resolved.<\/p>\n\n\n\n

Trishna Shah pointed to the gap between technological capability and real-world applicability:<\/p>\n\n\n\n

“Data infrastructure has improved, but data sanctity\u2014clean, relevant, usable data\u2014is still a major concern. Especially in enterprise applications, like building reliable chatbots or healthcare tools.”<\/p>\n\n\n\n

Prithvijit Roy noted the expanding definition of data\u2014and the challenges that come with it:<\/p>\n\n\n\n

“Everything is data now\u2014images, audio, video. But, collecting and organizing it at scale is expensive. We are trying to solve these problems with AI itself\u2014AI cleaning data for AI.”<\/p>\n\n\n\n

Himanshu Tambe provided a sobering real-world example of institutional barriers:<\/p>\n\n\n\n

“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.”<\/p>\n\n\n\n

The Productivity Paradox: Why AI\u2019s Impact Isn\u2019t More Visible<\/strong><\/h5>\n\n\n\n

Despite AI\u2019s rapid advancements, its measurable impact on productivity and economic growth remains inconsistent – a phenomenon the panelists unpacked.<\/p>\n\n\n\n

Prithvijit Roy observed that many companies remain stuck in experimentation mode:<\/p>\n\n\n\n

“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.”<\/p>\n\n\n\n

Trishna Shah cited tangible benefits in healthcare:<\/p>\n\n\n\n

“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\u2014it’s not always visible to users.”<\/p>\n\n\n\n

Himanshu Tambe, however, warned of broader socioeconomic disruptions:<\/p>\n\n\n\n

“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.”<\/p>\n\n\n\n

The Road Ahead: How to Realize AI\u2019s Full Potential<\/strong><\/h5>\n\n\n\n

The panelists concluded with key recommendations for ensuring AI\u2019s responsible and effective adoption:<\/p>\n\n\n\n