Hitachi Vantara Exchange India: Building the AI-Ready Enterprise, From Data Foundations to Governed Autonomy

Hitachi Vantara Exchange India brought together enterprise leaders and technologists around a shared reality: AI value is no longer constrained by model capability alone. It is constrained by enterprise readiness—data foundations that can be trusted, governance that can scale, and resilience that can withstand disruption. The programme moved deliberately from the fundamentals of building a modern data foundation to the more complex challenge of operationalising agentic AI in real enterprise environments, with recurring emphasis on trust, accountability, and cyber resilience by design.

Dataquest partnered with Hitachi Vantara on this platform to keep the conversation anchored on enterprise outcomes rather than experimentation theatre. As the knowledge partner, Dataquest also fully curated the Data Innovation Awards 2026—assembling the independent jury, managing the nominations process, coordinating evaluation, and supporting final jury deliberations.

Welcome address: setting the enterprise lens

The day opened with Hemant Tiwari, Managing Director, India & SAARC Region, Hitachi Vantara, framing data as operational infrastructure. It is no longer a back-office resource to be “managed”; it is the substrate on which AI outcomes are built. That shift changes leadership expectations. If data is infrastructure, it must behave like infrastructure—available when needed, protected by default, and governed consistently across teams and environments. The opening framing made the day’s intent explicit: outcomes over novelty, and execution discipline over pilot theatre.

Building a data foundation for what’s next

Adrian Johnson, Chief Revenue Officer, Hitachi Vantara, returned to a truth most CIOs have learned through experience: the future cannot be built on fragmented foundations. Hybrid estates, rising compliance requirements, fast-growing unstructured data, and the pressure to operationalise AI all converge on the same dependency—standardised, reliable data services that reduce friction across systems.

The practical message was not “collect more”, but “operate better”. Modernisation succeeds when organisations remove operational drag: fewer brittle handoffs between platforms, clearer service-level expectations, and governance that remains consistent even as the environment becomes more distributed. It is this base—repeatable, controllable, and auditable—that determines whether AI becomes a scalable capability or remains a series of isolated proofs of concept.

The data engine for tomorrow’s workloads

Jay Subramanian, General Manager, Core Storage Platforms, Hitachi Vantara, focused on breaking data silos and unifying data management with simplicity—speaking directly to a fundamental enterprise problem: the data engine is rarely one engine. Most organisations manage multiple silos across business-critical applications, modern mainframes, regulated and compliance-heavy workloads, unstructured stores, containers and virtualisation layers, public cloud estates, and edge environments. AI-era patterns add another layer of complexity, increasing sensitivity to data quality, access discipline, and operational stability.

Silos do not merely slow programmes down; they create second-order problems that compound over time—cost sprawl, inconsistent controls, overlapping tools, uneven service levels, and risk exposures that are difficult to understand end-to-end. When AI sits on top of this fragmentation, it amplifies weaknesses: data access becomes harder to govern, lineage becomes harder to trace, and reliability becomes harder to assure.

A practical leadership takeaway from this segment was the emphasis on operating discipline through explicit expectations: predictable availability and performance, resilience that is engineered rather than assumed, and sustainability that is measurable rather than claimed. In an AI era, the objective is not simply to “support AI workloads”, but to build a foundation that behaves predictably under stress—because enterprise AI is a production system, not a lab exercise.

Trusted AI at scale: security becomes a design condition

A late-morning session on trusted AI reinforced a growing consensus: scale without trust is not scale—it is exposure. Vijay Logani, National Sales Manager, Cloud & AI Infrastructure Group, Cisco Systems (India & SAARC), reinforced the idea that as AI enters critical workflows, security cannot be treated as a wrapper around systems. It becomes a design condition within systems.

For leadership teams, this reframes the checklist. It is not enough for models to be capable; enterprises need systems that are observable, policy-compliant, and controllable once deployed. Trusted AI at scale depends on the environment: access control, data lineage, observability, and governance mechanisms that survive the transition from pilot to enterprise rollout.

Agentic AI revolution: from autonomy to impact

The programme’s centrepiece combined expert talks and a moderated panel under a clear objective: exploring how enterprises can move beyond experimentation to scale agentic AI and analytics for measurable business outcomes, while ensuring governance, trust, and accountability.

Jason Hardy, CTO, Artificial Intelligence, Hitachi Vantara, positioned agentic AI as the next leap beyond chatbots and copilots—systems designed to reason, plan, act, and improve within enterprise workflows. That distinction matters because it changes the nature of enterprise responsibility. Copilots assist humans; agents take on chunks of work. Once systems begin to act, the architecture question shifts from “how do we deploy a model?” to “how do we design an enterprise system that can act safely, remain accountable, and improve over time?”

One of the most actionable concepts in this segment was the flywheel logic that connects data, outcomes, and adoption. Data flywheels are self-reinforcing loops: better data improves outcomes, improved outcomes increase usage, and usage generates more signal and feedback. Agentic systems can compound this effect, because each execution can strengthen retrieval quality, evaluation signals, guardrails, and domain customisation. The practical implication for leaders is straightforward: start small with self-reinforcing loops, capture early wins, and let momentum compound rather than attempting a “big bang” autonomy rollout.

Sanjeev Azad, CTIO and SVP, APAC, GlobalLogic, focused on the necessary counterbalance: autonomy must be engineered as a governed capability. The approach was framed as a maturity path—from human-in-the-loop towards increasingly autonomous systems—supported by reusable skills and accelerators to reduce time-to-market. The trust-by-design anchor was explicit: governance is not a compliance afterthought; it is a design protocol that makes autonomy deployable in enterprise environments.

Moderated by Pradeep Gupta, Chairman, CyberMedia Group, the panel discussion brought the conversation from concept to execution, with perspectives from enterprise leaders and customer voices. Rather than treating autonomy as an abstract trend, the discussion returned repeatedly to the questions leaders must answer if agentic systems are to create value responsibly. What does accountability look like when systems act rather than assist? How do organisations keep autonomy auditable and reversible so governance does not collapse under speed? And how should ROI be measured beyond pilots—in cycle-time improvements, error-rate reductions, risk posture strengthening, and demonstrable business outcomes?

The broader message from this segment was clear: agentic AI is not a novelty layer. It is an operating model shift—one that raises the bar on foundations, controls, and measurement.

From infrastructure backbone to cyber resilience

The afternoon programme returned trust to the fundamentals of backbone infrastructure, protection, recovery, and continuity. Sameer Bangalore, Regional Technical Leader, Brocade India, addressed the role of secure, scalable connectivity as enterprises prepare for AI-driven traffic patterns and distributed workloads. The cyber resilience track then reinforced a core point: as AI increases the value of data, it also increases the cost of disruption—raising the bar for recovery readiness and resilience-by-design.

In the data protection and cyber resilience segment, led by Sanjay Agrawal, CTO and Head of Presales, India & SAARC, the emphasis stayed on enterprise outcomes: reducing recovery uncertainty, strengthening control points, and ensuring resilience is verifiable under pressure. The resilience theme continued with Mayank Mishra, Country Manager, India & SAARC, Cohesity, reinforcing the shift from “prevent and hope” to “assume disruption and design recovery”.

In this context, resilience means more than security tools. It includes continuity design, compliance resilience, operational simplicity, and the ability to restore services and data with integrity.

Data Innovation Awards 2026: the jury lens and what the categories signal

The day concluded with the Data Innovation Awards 2026, curated by Dataquest as the knowledge partner.

To ensure independence and rigour, Dataquest assembled a six-member jury anchored by three co-chairs—Prof. S. Sadagopan (Former Director, IIIT Bangalore), Pradeep Gupta (Chairman & Managing Director, CyberMedia), and Hemant Tiwari (Managing Director and Vice President, India and SAARC Region, Hitachi Vantara)—supported by Ranendra Datta (CIO & Advisor), Ramkumar Ramamoorthy (Partner, Catalincs; former CMD, Cognizant India), and Manoj Chugh (Independent Advisor; Chairperson, Manoj Chugh Advisory LLP).

As part of the awards segment, Ranendra Datta presented the jury’s evaluation approach, underscoring fairness, transparency, and industry relevance, with nominations assessed through an outcome-first lens across impact, execution excellence, and scalability, and final winners selected through independent jury deliberation and consensus. With discretion requested around customer identities, the most meaningful story sits in what the ten categories represent—Data Resilience Champion, AI Impact Leader, Hybrid Cloud Innovator, Sustainable Infrastructure Leader, Customer Experience Transformer, Digital Transformation Excellence Award, Data Analytics Excellence Award, Intelligent Automation Leader, Industry Transformation Pioneer, and Data Governance & Trust Champion—together reflecting a clear enterprise definition of progress in 2026: measurable value, scalable architectures, governance engineered into systems, and resilience that can be demonstrated under real-world pressure.

Taken together, these categories offer a coherent definition of enterprise progress in 2026. They reflect a shift from isolated innovation to disciplined execution: architectures that scale across hybrid environments, governance engineered into systems, resilience that is demonstrable, and impact that can be measured credibly.

The awards segment also featured cricketing legend Sunil Gavaskar as the celebrity guest speaker, who presented the honours to the winners, bringing a fitting close to the day’s proceedings.

Closing thoughts and key takeaways

If Hitachi Vantara Exchange India carried one consistent message, it was this: foundations enable autonomy, and trust enables scale. Enterprises that want agentic AI to deliver measurable outcomes must begin with data platforms designed for continuity and compliance, engineer autonomy through governed pathways, and treat cyber resilience as a core design condition—not an afterthought. In that framing, agentic AI is not simply a new interface. It is a new way work gets done, and the themes surfaced across the day suggest enterprises are preparing to run that shift with far greater rigour than the pilot era ever demanded.