Global equity markets retreated sharply during the last 4 days, as investors reassessed the long-term implications of artificial intelligence (AI) on business models and sectoral profitability. The shift in sentiment reflected growing recognition that while AI promises substantial efficiency gains, it also poses structural risks to traditional revenue streams. In the United States, the Dow Jones Industrial Average declined from 50,188.14 on 10 February to 49,451.98 on 12 February, marking a 1.47 percent drop. The NASDAQ Composite fell 1.75 percent to 24,687.61, while the S&P 500 declined 1.57 percent to 6,832.76. Asian markets followed suit: 10-13 February, 2026 Japan’s Nikkei 225 slipped 1.23 percent to 56,941.97, India’s Sensex dropped 1.95 percent to 82,626.76, the Nifty declined 1.79 percent to 25,471.1, and Hong Kong’s Hang Seng fell 2.27 percent to 26,567.12. This synchronized decline underscores how AI sentiment has transitioned from unqualified optimism to a more nuanced evaluation of both opportunity and disruption.

The trigger traces to early February 2026 announcements from Anthropic, the AI startup behind Claude models, whose new tools like Claude Cowork and Opus 4.6 ignited a global “SaaSpocalypse.” These agentic AI capabilities automate complex workflows in legal research, CRM, analytics, coding, and finance—handling tasks across files, directories, and multi-agent coordination—prompting a $285 billion rout in software, financial services, and asset management stocks.

Investors increasingly recognize that AI-driven efficiencies may compress margins in established sectors. In logistics, predictive routing systems optimize loads and delivery networks in real time, reducing operating costs but intensifying competitive pressures. Commercial real estate faces disruption from AI-based valuation tools and automated asset management platforms that streamline oversight while diminishing traditional advisory and brokerage fees. Financial services firms are adapting to AI-enabled compliance systems and risk analytics that lower error rates but challenge legacy cost structures. Amid these uncertainties, capital rotated toward safe-haven assets, with U.S. 10-year Treasury yields easing to 4.10 percent, signaling short-term caution.

Despite near-term volatility, AI’s structural role in reshaping global economic activity remains clear. Across industries, enterprises are embedding AI to automate repetitive processes, enhance predictive accuracy, and personalize customer engagement. Manufacturing firms are deploying self-learning robotics to reduce waste and optimize throughput. Healthcare providers are utilizing diagnostic algorithms to improve early detection and clinical precision. Financial institutions are leveraging advanced analytics to streamline compliance, reduce fraud, and enhance decision-making. These applications collectively reinforce productivity growth and operational resilience.

In India, leading corporates are actively translating AI adoption into measurable performance gains. Bajaj Finance demonstrated this during its Q3 FY26 earnings call, where CEO Rajeev Jain outlined transformative use cases. The company analyzed 2 crore customer calls using AI-driven voice-to-text tools, generating actionable insights for 5.2 lakh customers and enabling 100,000 new loan offers. This initiative contributed ₹1,600 crore in disbursals—approximately 10 percent of Q3’s ₹16,545 crore total—while call data analytics added another ₹325 crore in volumes. AI also generated 2.7 lakh marketing videos and 1.2 lakh banners, while 11 live text bots enhanced customer engagement across products, with conversational bots planned for all 26 offerings by May 2026. Face-matching technology verified 46 million customers and automated 43 document types at 95–96 percent accuracy, accelerating onboarding. Document quality automation has reached 41 percent, with a target of 85–90 percent within 15 months, and digital platforms report 25–45 percent efficiency gains. Over the next six months, the company aims to deploy more than 800 autonomous agents across core functions.

Other major Indian enterprises are pursuing similar strategies. HDFC Bank is advancing an “AI-first” framework through Project Indra, scaling generative AI pilots across personalization, operations, and cybersecurity while reengineering data flows through APIs. Reliance Industries has introduced an AI Manifesto targeting a tenfold productivity enhancement for its 600,000-plus workforce through workflow redesign and agentic automation. In the technology sector, Tata Consultancy Services has consolidated AI initiatives into a dedicated transformation unit, delivering 25–35 percent KPI improvements, while Infosys reports 8–15 percent gains from AI-assisted development, with expectations of further expansion.

Industry assessments indicate that nearly 60 percent of Indian firms now utilize AI—exceeding global averages—with potential productivity gains of 0.3 to 3 percent over the coming decade. While markets may fluctuate as business models adjust, AI’s trajectory suggests deeper integration across sectors. Firms that strategically align technology with operational redesign are likely to convert disruption into a durable competitive advantage, positioning India for sustained productivity-led growth in a data-driven global economy.