**Executive Summary**
This document summarizes discussions from the India AI Impact Summit held on February 20, 2026, where industry leaders explored the impact of AI agents on SaaS and enterprise services. The panel highlighted the evolution of business models, enterprise readiness, and customer-centric AI adoption, with emphasis on the need for agility and innovation to succeed in the AI era. The discussion centered on how AI agents are reshaping business and operating models.
**Key Points / Main Content**
* **AI Agent Impact on SaaS and Enterprise Services:**
* AI agents are fundamentally disrupting the traditional SaaS model.
* Enterprises can integrate foundation models with specialized agents to unlock measurable business value.
* **Enterprise AI Adoption Requirements:**
* Enterprise AI adoption demands more than generic models and requires significant groundwork, including data rationalization and application modernization.
* Success in the AI era will hinge on agility, enterprise readiness, orchestration, and continuous problem-solving.
* Addressing customer pain points, observability, governance, auditability, and adoption are crucial.
* **Evolution of the Role of Engineers:**
* The role of software engineers is shifting toward high-level architecture and rigorous validation.
* HCL Technologies is building intellectual property and specialized services to bridge the gap between AI capabilities and enterprise-grade performance.
* **Market and Economic Opportunities:**
* AI is creating a $300 billion services opportunity by making the ‘impossible' economically viable.
**Impact Analysis**
**Software Engineers**
* **Impact:** Software engineers are shifting towards high-level architecture and rigorous validation.
* **Action Required:** Upskill and adapt to new roles that require high-level architecture and rigorous validation skills.
**Enterprises**
* **Impact:** Enterprises need to prepare for significant changes in business and operating models due to AI agents and must modernize to stay competitive.
* **Action Required:** Focus on data rationalization, application modernization, and develop capabilities in AI orchestration, agility, and problem-solving.
**SaaS Providers**
* **Impact:** Traditional SaaS models are being disrupted and providers need to adapt to integrating AI agents into their offerings.
* **Action Required:** Innovate to incorporate AI agents, address customer pain points, and focus on observability, governance, auditability, and adoption to ensure long-term sustainability.
Key Entities Referenced
India AI Impact Summit: A high-level panel discussion focusing on the impact of AI agents on SaaS and enterprise services.
Ministry of Electronics & IT: The government ministry associated with the summit and potentially related AI policies.
Ministry of Electronics & IT
Industry Leaders Discuss Impact of AI Agents on
SaaS and Enterprise Services at India AI Impact
Summit
Panel Highlights Evolution of Business Models, Enterprise
Readiness and Customer-Centric AI Adoption
AI Agents to Reshape Business and Operating Models; Agility
and Customer-Centric Innovation Key to Success in AI Era
Posted On: 20 FEB 2026 8:51PM by PIB Delhi
At the India AI Impact Summit, a high-level panel featuring Salil Parekh, Chief Executive Officer of
Infosys; K. Krithivasan, Chief Executive Officer of Tata Consultancy Services; C Vijayakumar, Chief
Executive Officer & Managing Director of HCL Technologies; and Arundhati Bhattacharya, Chairperson
& CEO of Salesforce India, examined whether AI agents are fundamentally disrupting the traditional SaaS
model. The discussion was moderated by Amitabh Kant.
Addressing concerns over sharp market reactions, including speculation around the future of SaaS,
Arundhati Bhattacharya cautioned against oversimplification. “Markets will say a lot of things, and not all
of it comes true,” she noted. “When you talk about the SaaS model, it’s not only about vibe coding or
creating an application, it’s about understanding workflows, recognizing customer pain points, andensuring you address them. It’s about observability, governance, auditability, and adoption.” She
emphasized that while ways of working will evolve, long-term sustainability will depend on delivering
real customer value.
From a services perspective, K. Krithivasan highlighted a fundamental shift in the role of engineers. “We
are entering an era where the role of the software engineer is shifting toward high-level architecture and
rigorous validation,” he said. While AI promises immense productivity gains, he stressed that enterprise
adoption requires significant groundwork, from data rationalization to application modernization. Rather
than contraction, he foresees expansion: “We don’t envision a shrinking of the sector, but rather a massive
explosion in the volume of what can be produced and the complexity of the problems we can solve.”
C Vijayakumar echoed the view that enterprise AI adoption demands more than generic models. “Large
language models and foundational models cannot yet be applied most efficiently to enterprise use cases,”
he said, noting a persistent gap between foundational capabilities and enterprise-grade performance. HCL
Technologies, he added, is building intellectual property and specialized services, including physical AI
and agentic AI, to bridge that gap and scale adoption, even if that means proactively evolving existing
business lines.Salil Parekh underscored the scale of opportunity ahead. “AI is creating a $300 billion services
opportunity by making the ‘impossible’ economically viable,” he said, pointing to legacy modernization as
a key example. Through Infosys’ orchestration platforms, he noted, enterprises can integrate foundation
models with specialized agents to unlock measurable business value.
Collectively, the panel delivered a clear message: AI agents will reshape business and operating models,
but they will not render them obsolete overnight. Success in the AI era will hinge on agility, enterprise
readiness, orchestration, and above all, the ability to continuously solve real customer problems in
increasingly complex digital ecosystems.
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Mahesh Kumar / Pawan Faujdar / Kanishk Sharma
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