Enterprises are increasingly moving from experimental projects involving agentic AI to deploying pragmatic, enterprise-wide strategies that emphasise tangible business value. As these technologies mature, the competitive advantage will belong to organisations that invest equally in their people and their platforms, says Sandhya Arun, CTO, Wipro. “Ultimately, success will depend on talent readiness and continuous skilling,” she tells Sudhir Chowdhary in an interview. Excerpts:
As we move into 2026, what has fundamentally shifted in how organisations are thinking about AI – from experimentation to execution?
India’s IT industry saw some foundational shifts in 2025 with the mainstreaming of generative AI and hyper automation. If we see 2025 as a year of disruption and discovery, 2026 represents a clear inflection point. AI is no longer optional; it is driving core functionality and innovation across countless industries. The focus has now has moved decisively to execution at scale. Organisations are embedding AI into core business architectures and operating models, redesigning workflows and decision chains around it. The emphasis is now on reliability, governance, and measurable business outcomes that can be repeated.
How real is the shift towards agent-led and collaborative AI in large enterprises?
This shift is already playing out, particularly in complex enterprise environments where speed, coordination, and scale are essential. This year we will see the fully autonomous enterprise move from concept to reality, going beyond human-in-the-loop co-pilots to modular systems of collaborating AI agents that manage complex, end-to-end B2B operations. From autonomous procurement to intelligent supply chain orchestration, these AI teams monitored by humans, will drive exponential efficiency gains.
Importantly, these systems are being architected with governance-by-design, embedding human oversight, guardrails, and accountability so AI can handle execution at scale while humans retain ownership of intent, validation, and mission-critical outcomes.
As AI takes on more decision-making and execution, how is the role of people inside organisations evolving – from technologists to business leaders?
Human roles are shifting from performing tasks to orchestrating intelligent systems. For technologists, this means moving beyond model development to building resilient AI platforms, ensuring data integrity, and securing the intelligent core. For business leaders, the role increasingly involves setting strategic direction, validating AI-driven decisions, and owning outcomes. The most valuable skills are no longer just technical proficiency, but domain expertise, judgement, and the ability to collaborate effectively with intelligent machines.
How significant is the rise of embodied AI for enterprises?
Embodied AI marks a significant expansion of AI’s impact, from optimising digital workflows to transforming physical operations at scale. Building on the autonomous machines that emerged in 2025, AI models will break free from data centers, manifesting in humanoid robots, quadrupeds, and drones with genuine spatial awareness and real-time adaptability.
For enterprises, this enables smarter decision-making in dynamic, physical environments across manufacturing, logistics, healthcare, mobility, and hazardous operations. The result is improved safety, efficiency, and resilience, effectively bridging the gap between digital intelligence and physical value creation, thus unlocking large parts of the physical economy that were previously untouched by pure software.
Many enterprises are moving away from one-size-fits-all AI models towards domain- and industry-native intelligence. What is driving this shift, and why does it matter at scale?
At enterprise scale, accuracy, compliance, and contextual understanding matter far more than general capability. Domain-native AI models-trained on proprietary, industry-specific data-understand the nuances of sector-specific language, workflows, and regulations. These models are often smaller, more cost-effective, and significantly more reliable than general-purpose alternatives. As AI becomes embedded in mission-critical systems, this shift is essential to delivering consistent outcomes and maintaining trust across regulated and high-stakes environments.
What will separate organisations that successfully operationalise autonomous and domain-native AI from those that remain stuck in pilot mode?
The differentiator will be intent and architecture. Organisations that succeed will treat AI as a core enterprise capability, supported by robust platforms, strong AI application security, and clear governance frameworks. They will invest equally in technology and workforce readiness, enabling people to work alongside and supervise autonomous systems. Those that struggle will remain focused on disconnected pilots, without rethinking operating models or building the foundational capabilities needed to scale AI responsibly and sustainably. The future belongs to those who master this symbiotic relationship, transforming disruption into unprecedented growth.