At AI Infra Summit 2026, Lenovo’s Trusha Pandya discussed why scaling AI from pilot to production depends on strong foundations, complementary expertise and ecosystem partnerships

Contact center success depends on orchestrating AI automation and human expertise to improve resolution, reduce friction and continuously optimize customer journeys

Manufacturers are proving that AI can create value, but scaling it safely across plants requires a common data architecture, closed-loop workflows and governance built for operational risk

HCLTech’s The AI Impact Imperatives, 2026 report shows that enterprise AI success depends on the right foundation, the right AI governance and the right partners

HCLTech research with 467 senior executives finds respondents expect, on average, 43% of major AI projects initiated over the next 24 months to fail, highlighting three imperatives for impact

Banks are under pressure to resolve payment exceptions and disputes faster than ever, yet speed alone is not enough: automation must scale without weakening oversight, auditability or customer trust.

Private equity firms can turn Agentic AI into measurable portfolio value by combining industry-specific use cases, repeatable agent models and disciplined financial measurement

As AI moves deeper into manufacturing, organizations need a digital thread connecting intelligence to physical action, supported by cost-efficient edge infrastructure and greater control over data

In an era of persistent disruption, competitive advantage is shifting from planning accuracy to the ability to sense and act on changing physical conditions across the value chain

Industrial manufacturers are shifting from one-time equipment sales to outcome-based services, using AI, connected assets and lifecycle data to reshape customer experience, operations and aftermarket

AI is helping manufacturers move beyond operational visibility toward predictive and prescriptive insights that can improve quality, optimize resources and support better decisions on the shop floor

As AI moves from pilots to business-critical systems, enterprises need greater control over where data is processed, where models run, how infrastructure is managed and how governance is enforced