During a closed roundtable at HCLTech’s pavilion at the 2026 World Economic Forum, C-suite and senior leaders discussed how innovation, ecosystem collaboration, and evolving business models are reshaping the technology industry. The conversation explored AI economics, infrastructure strategy, services transformation, edge and device computing, and the emerging category of Physical AI, all anchored around a central question: what does value mean in an era where technology is embedded everywhere, and how should leaders measure it?
A key takeaway was that ROI and impact are not synonymous. Organizations can demonstrate AI-driven improvements in time savings, faster cycles, enhanced quality, and better experiences, but translating these into measurable financial outcomes requires deliberate operating model changes. Leaders emphasized the need for broader success metrics that include employee and customer experience, process efficiency, and innovation velocity, alongside traditional financial measures.
The roundtable highlighted AI cost management as an emerging governance challenge. As AI shifts toward usage-based consumption and token-driven models, costs can rise unpredictably, requiring organizations to treat AI like a managed resource with usage controls, observability, and unit economics. Establishing guardrails, tracking yield, and orchestrating workflows effectively were identified as essential for balancing adoption with fiscal discipline.
Efficiency gains often arrive before productivity improvements, particularly in regulated or constrained environments. While AI can reduce administrative burdens and after-hours work, these gains do not automatically translate into higher throughput or economic output. Leaders noted that policy, incentives, and workflow redesign are critical to convert efficiency into measurable productivity and value.
AI is also driving a fundamental reset in services. Organizations are shifting from labor delivery to service redesign, prioritizing outcomes and measurable impact rather than incremental optimization. Automation, AI agents, and integrated tooling are reshaping customer expectations, with buyers increasingly evaluating partners on demonstrated adoption, behavior change, and real-world results rather than traditional staffing or training metrics.
Infrastructure strategy emerged as a competitive differentiator, influenced by latency, data locality, reliability, energy, memory, and regional ecosystem strengths. AI adoption requires distributed architectures spanning cloud, edge, and devices, with infrastructure planning becoming a strategic, board-level consideration for resilience, cost control, and speed to value.
Sovereignty in AI was reframed from self-sufficiency to resilience. Leaders emphasized diversified dependencies, risk management, and strategic partnerships rather than isolation. Decisions around data governance, workload placement, and stack control were guided by continuity, compliance, and competitive positioning in a fragmented geopolitical landscape.
Finally, Physical AI—robotics, sensor-driven autonomy, and real-world AI systems—was identified as a rapidly emerging category with implications for workforce design, safety, liability, and cultural adoption. Leaders highlighted the need to frame Physical AI around outcomes and jobs to be done, designing safeguards, change programs, and governance structures from the outset.
Across all discussions, a consistent message emerged: as AI becomes embedded and invisible, organizations must redefine value. Success depends on treating AI as an operating model shift, linking usage to outcomes, redesigning workflows and services, investing in resilient infrastructure, and preparing for the Physical AI wave with integrated safety and governance.
