Wipro CTO Sandhya Arun outlines key technology trends set to shape enterprises in 2026

Global enterprises are set to enter a new phase of artificial intelligence adoption in 2026, with AI systems expected to operate at scale across core business functions, according to Sandhya Arun, Chief Technology Officer at Wipro Limited.

In a technology outlook shared by the company, Arun said 2025 marked a shift from experimentation to meaningful enterprise adoption of AI. Generative AI and automation moved into mainstream use, while early agent-led systems began supporting decision-making, with human oversight remaining central.

Looking ahead to 2026, Arun said the focus will move to AI systems embedded deeply within critical workflows. Enterprises are expected to deploy AI across operations while elevating the role of people from execution to orchestration, governance, and strategic control. She added that talent readiness and continuous skilling will be key to unlocking value from these technologies.

Arun outlined several technology trends expected to shape enterprise technology strategies in 2026.

She said agentic AI will play a larger role, with enterprises moving beyond isolated pilots to enterprise-wide networks of AI agents. These systems are expected to manage workflows across IT, finance, human resources, marketing, sales, legal, procurement, supply chains, and customer engagement, while humans focus on oversight and governance.

Arun also pointed to the growing role of embodied AI, where artificial intelligence is embedded in robots, vehicles, machines, and connected devices. She said these systems will support use cases across sectors such as healthcare, manufacturing, energy, utilities, logistics, and mobility.

Another area highlighted was the combination of digital twins and AI. According to Arun, AI-enabled digital twins will allow organizations to simulate real-world conditions, predict outcomes, and optimize assets and processes through continuous monitoring and analysis.

She also noted a shift toward domain-native AI models, which are trained on industry-specific data and designed to meet regulatory, safety, and risk requirements. These models, she said, are expected to deliver greater accuracy while being more cost-effective.

In financial services, Arun said programmable money and distributed ledger technologies are moving toward real-world adoption. Use cases include tokenized assets, automated settlements, and cross-border payments, supported by growing regulatory clarity.

Arun added that advances in quantum computing are opening new possibilities in areas such as life sciences, financial services, and materials research, while also increasing the need for quantum-safe security standards.

She concluded that workforce readiness will become a key leadership priority, with organizations needing to invest in learning, collaboration, and change management as AI systems scale across enterprises.