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Why is Big Tech bullish on AI agents?
Unlike basic chatbots, AI agents are autonomous systems that can plan, reason and complete complex multi-step tasks with minimal input—such as coding, data analysis and generating reports. Developers use them across IT, customer support, and enterprise workflows. Google and Microsoft recently called AI agents the next big shift, unveiling Project Mariner, Gemini tools, Azure AI Foundry and NLWeb. Salesforce, Amazon, IBM, and Meta are also building these platforms to automate workflows and enhance productivity. Nasscom has said 46% of firms are experimenting with AI agents, mainly in IT.
What should firms be wary of?
Many so-called agentic use cases today can be done with simpler tools, says Gartner, which predicts over 40% of such projects will be scrapped by 2027 due to high costs, vague value or weak risk controls. It adds that of thousands of vendors, only about 130 are seen as credible; and many engage in “agent washing”—rebranding chatbots, robotic process automation (RPA), or assistants as agents without real autonomy. Most current systems also lack the maturity to deliver complex outcomes or sustained return on investment (ROI). Nasscom echoes this, noting 62% of enterprises are only doing in-house agent testing.
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How big is the market for AI agents?
The global AI agents market, valued at $5.4 billion in 2024, is pegged to touch $50.3 billion by 2030, per Grand View Research. North America led with 40.1% revenue share, while Asia-Pacific is the fastest-growing region. Machine learning, single-agent systems, and ready-to-deploy agents dominated through tech, system type, and deployment model, respectively.
How can companies unlock real value?
AI agent adoption is hindered by privacy concerns, regulation gaps, and limited focus on talent readiness. Integration with legacy enterprise software remains complex and costly. Regardless, Gartner predicts 15% of daily work decisions will be made autonomously by agentic AI by 2028, up from 0% in 2024. It also urges agentic AI-use only when the returns are clear. Nasscom sees strong potential in real-time decision-making and agility. Key focus areas include data governance and AI risk protocols.
Can humans make AI agents more effective?
Though AI agents are designed to be autonomous, Nasscom’s recent study of over 100 global enterprises reveals how businesses are transitioning from early-stage GenAI applications towards more goal-oriented, human-plus, AI agentic systems. It believes that to scale responsibly, enterprises must prioritize human-AI collaboration, trust and data readiness. Nasscom adds that most (77%) firms adopt AI agents with a “human-in-the-loop” approach, reflecting the need for oversight and contextual judgment.
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