Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I have 20+ years of experience at the intersection of AI, Technology Consulting, and Enterprise Transformation. My background spans leadership roles, including VP at Accenture AI and Principal Consultant at PwC, where I drove AI-led transformation programs across Fortune 500 clients. I am also an alumnus of IIM Ahmedabad and currently the Founder & CEO of Genexa.AI, a next-generation consulting and product engineering firm focused on the adoption of Generative and Agentic AI. I have been recognized as a Top Voice in Artificial Intelligence on LinkedIn and frequently speak at CXO forums and industry events on the future of AI in enterprises.

Q2. How is the global AI products and platforms market evolving in terms of size and growth rates across key sectors like financial services, healthcare, manufacturing, and retail?

The global AI products and platforms market is expanding at a 25–30% CAGR, expected to cross $500–600B by 2027.

Financial Services: The sector has seen the strongest adoption in risk modeling, fraud detection, and wealth advisory services, with BFSI contributing ~20–25% of the market share

Healthcare: Growing fastest (~30% CAGR), led by AI in diagnostics, drug discovery, and patient engagement

Manufacturing: Increasingly driven by predictive maintenance, supply chain optimization, and autonomous operations

Retail: AI-powered personalization, demand forecasting, and agent-driven customer engagement are scaling rapidly

Q3. Which AI sub-segments—like generative AI, computer vision, or MLOps platforms—are showing the fastest adoption, and in which geographies?

Generative AI & Agentic AI are getting the fastest traction across geographies.

Agentic AI / Autonomous Agents: Emerging fastest in North America for enterprise workflows and in emerging markets for consumer-facing apps.

Generative AI: The fastest adoption globally, with the US and Western Europe leading; an early surge is also observed in the GCCs and Japan for enterprise use cases

Computer Vision: Strong in manufacturing (quality inspection), logistics, and healthcare imaging; Asia-Pacific leads industrial adoption

LLM Ops / MLOps Platforms: Rapid uptake in regulated sectors (BFSI, healthcare) in the US/EU, as enterprises industrialize AI at scale

Q4. What AI technology breakthroughs (e.g., multimodal AI, autonomous agents, federated learning) are you seeing gain commercial viability in the last 12–18 months?

Multimodal AI: Commercial deployments in healthcare imaging, enterprise knowledge management, and retail cataloging

Autonomous Agents: Moving Beyond Pilots into Production in Enterprise Knowledge Workflows, Customer Operations, and Financial Research

Federated Learning: Gaining traction in healthcare and financial services where data privacy is paramount

Synthetic Data: Rising adoption for model training in regulated industries and low-data scenarios

Q5. Which untapped industry verticals or use cases hold the greatest potential for AI product expansion in the next 3–5 years?

There are various untapped industry domains & use cases across Utilities, Energy, Manufacturing, etc, which have great potential for AI application & adoption.

Energy & Utilities: Grid optimization, demand prediction, and sustainability tracking

Public Sector / Smart Cities: Citizen services automation, infrastructure monitoring

Agriculture: Precision farming, climate-adaptive crop models, and supply chain integration

Manufacturing: Predictive Maintenance, Production Efficiency Optimization, Smart Factory Operations

Q6. Are there emerging companies or lesser-known names you believe could disrupt the market in the next 3–5 years?

There are various platform players, as well as niche vertical players, in insurance tech, healthcare, and industrial AI, poised for breakout growth.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

“How defensible is your moat in terms of data, distribution, and differentiation, given the rapid commoditization of foundational models, and what are you doing about it?”

Because in the next 3–5 years, sustainable advantage will hinge not just on model capabilities, but on proprietary datasets, integration into enterprise workflows, regulatory readiness, and ability to scale cost-effectively. An impactful partnership with a trusted AI advisor or consulting firm will play a decisive role in shaping the future of your firm.