Over the past two years, most large organizations have proven that AI tools can work. The challenge for 2026 is different: can it work responsibly, economically, and repeatedly across the enterprise?
Boards now ask tougher questions: what does each decision cost to serve? How is the model governed? And where, beyond customer service and automation, will AI drive impact?
CTO and Head of Ecosystems, HCLTech.
Behind the scenes, inference costs and power consumption are becoming part of those discussions, as leaders assess at the total cost and carbon footprint of AI decisions at scale.
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Against that backdrop, here are the four trends I see defining the year ahead.
1. Responsible AI, trust and change management will be vital as AI systems become more integrated into decision-making
Responsible AI and trust-building will be vital as AI systems become more integrated into decision-making. AI red teaming, stress testing models for vulnerabilities before they reach production, is becoming a critical tool to ensure systems behave safely, reliably, and as intended.
In HCLTech’s recent research on the payments industry, 99% of leaders say they already use AI in some part of their operations, yet 47% report they do not have AI policies in place. This is a trust gap that slows scaling and invites risk.
Closing this gap is as much about people and IT management as it is about change and technology. Organizations need structured change programs, clear accountability, and continuous training so teams can design AI systems with human-in-command, human-in-the-loop, or autonomous modes, depending on the need.
Explainability and auditability are important considerations. In regulated sectors such as financial services and healthcare, evidence is the basis for scale and durable returns on AI investment.
2. Industry-tuned AI will drive the next wave of enterprise value
AI is rapidly moving across enterprise value chains. In 2026, the defining shift will be toward domain-specialized intelligence designed for the specific demands of each sector.
Organizations that achieve meaningful ROI will deploy purpose-built AI that understands its operational environment. Organizations are expected to use small, task-specific AI models three times more than general-purpose LLMs, driven by the need for contextual, reliable, and cost-effective solutions.
From transforming supply chains to enabling hyper-personalized retail and accelerating industrial automation, industry-tuned AI is redefining how companies create value, make decisions, and build competitive advantage.
3. AI Factories and Physical AI will connect digital intelligence with the real world
As AI moves from isolated experiments to enterprise-wide capability, organizations are beginning to think in terms of AI Factories that standardize data, safety, governance, and deployment, and package reusable components for products, channels, and edge environments.
Rather than rebuilding the same capabilities in every business unit, AI Factories allow teams to plug into common services, accelerating time to value while keeping cost and risk under control.
At the same time, from warehouses and shop floors to hospitals and smart infrastructure, Physical AI, including cognitive robotics, will increasingly bring intelligence into the real world. Here, AI systems must perceive, reason, and act in dynamic, safety-critical environments, often in close collaboration with people.
Together, AI Factories and Physical AI will blur the line between digital and physical operations. Enterprise leaders will need to design architectures, safety regimes, and operating models that span both worlds, so that artificial intelligence and human intelligence are harmoniously orchestrated to deliver clear, measurable business outcomes.
4. The shift to AI-led modernization
AI is set to transform one of the biggest challenges facing enterprises today: legacy modernization. Instead of multi-year, high-risk transformation programs, 2026 will mark the rise of AI-driven modernization engines capable of analysing, translating, and refactoring decades-old systems at unprecedented speed and accuracy.
The organizations that pull ahead won’t be those simply lifting and shifting legacy workloads, they will be the ones using AI intelligence to understand codebases, redesign architectures, and migrate logic into modern, scalable platforms with minimal disruption.
Across industries, AI-led modernization is reducing technical debt, accelerating cloud adoption and unlocking innovation that was previously trapped inside outdated systems. For leaders, the message is clear: embracing AI-led modernization isn’t just an IT upgrade, it’s a strategic imperative to stay competitive in a world where agility, resilience, and innovation will define competitive advantage.
Responsible, economical and repeatable
If 2024-2025 was about proving that AI can work, 2026 is about proving that it can work responsibly, economically, and repeatedly.
That means accepting the realities of inference cost and power, making trust an explicit promise to customers and regulators, resisting the urge to sprint into agentic everything without an operating model built for value and measurable ROI, investing in people and change alongside technology, and pivoting toward industry-specific applications where reliability and outcomes matter more than hype.
Leaders who take that path will turn AI from a series of experiments into a competitive, durable advantage.
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