AI in 2025: Breakout Year and Challenges

The year 2025 marked a significant point for artificial intelligence (AI) as it became mainstream among various user groups. However, its impact within large enterprises was less definitive. Despite considerable experimentation and investment, AI struggled to translate early adoption into scalable business value, revealing a gap between enthusiasm and actual impact.

AI Adoption and Scalability

• According to a McKinsey Global Survey, nearly 90% of global enterprises use AI in some capacity, but only 7% have fully scaled these use cases.
• The MIT Project NANDA report highlights that despite $30-40 billion investment in generative AI, 95% of organizations see no return.
• Only 5% of integrated AI pilots are extracting significant value, while most pilots show no measurable impact.

Reasons for the Gap in AI Value

• Business Domain Reimagination: AI is often viewed as a tool for efficiency rather than transformative innovation. Successful companies aim for a 2x–10x transformation led by domain leaders.
• Workflow Redesign: New technologies require end-to-end process redesign. Implementations are more effective when tech stacks are modular and scalable after redefining the business problem.
• Organizational Rewiring: Scaling AI demands new governance, quicker decision-making, and leadership adoption. Leaders must demonstrate AI use themselves to avoid adoption stalls.

Current Enterprise Strategies and Workforce Impact

• Enterprises are focusing on reshaping cost structures, often leading to layoffs. In 2025, around 122,549 tech employees were laid off globally from about 257 companies.
• AI is primarily used as a cost lever, with companies citing efficiency gains from automation and AI-assisted tasks.

Future Prospects: AI in 2026

Looking forward to 2026, AI is expected to become more significant in enterprise growth, according to ISG's survey on enterprise AI adoption. The number of use cases in production has doubled since 2024.

Strategies for AI Growth in Enterprises

• Many enterprises report an AI value gap with efficiency gains but lagging growth impacts. Nearly half expect meaningful AI-driven growth only by 2026 or later.
• To accelerate adoption, leaders should focus on growth-oriented use cases such as new product design, customer experience, and industry reinvention.
• Data integration is crucial as enterprise data contextualizes AI, enhancing its functionality and output relevance.

India's AI Developments

In India, the focus in 2026 is on "sovereign AI," emphasizing tech sovereignty amid geopolitical tensions. Major tech firms like Amazon, Microsoft, and Google are investing approximately $70 billion in India to meet demand.

Initiatives and Investments

• India will host the Global AI Summit in 2026, highlighting its intent to lead the next AI adoption phase focused on public infrastructure and responsible deployment.
• Under the IndiaAI Mission, significant milestones include the announcement of BharatGen, the first multi-modal LLM, and the onboarding of over 38,000 GPUs, far exceeding the initial target.
• The AIKosh platform, a repository for datasets and models, expanded to over 3,000 datasets and 243 AI models across 20 sectors.