Where Are You Building the Capability to Look Forward?

A conversation with Simon Ninan, Chief Strategy Officer at Hitachi Vantara

Simon Ninan has a job that most people in enterprise AI don't have: he thinks about infrastructure for the physical world.

As Chief Strategy Officer at Hitachi Vantara (the Silicon Valley-headquartered IT and data management arm of Hitachi, Ltd.), Simon sits at the intersection of two domains that have historically lived in entirely separate worlds: IT (information technology) and OT (operational technology). The trains, power grids, factories, and smart buildings that Hitachi builds are OT. The data storage, AI infrastructure, and software platform that Hitachi Vantara builds is IT. Simon's job is to make sure those two worlds connect in ways that produce real value.

The Question Every AI Leader Needs to Answer

Large language models are trained on the accumulated body of human knowledge. That is the source of their extraordinary power: they can synthesize, pattern-match, and generate across an enormous range of domains at a speed and scale no human team can match.

And yet, because of how they're built, LLMs are perpetually backward-looking. Simon made this point directly:

"You can train it on the entire body of human knowledge up to this point," he said. "And you can keep training it. But it is perpetually backward looking. For our world to evolve, where are you building the capabilities to look forward?"

The Edge Is Your People

Most of the enterprise AI conversation right now focuses on productivity: what can be automated, accelerated, measured against a 12-month ROI target. Simon's argument is that this framing is incomplete, and running underneath it is something he came back to repeatedly: domain knowledge is where the moat in AI actually lives, not in the model itself.

Hitachi's own competitive position illustrates why. Simon described the company's "secret sauce" as domain knowledge: understanding how things work on the ground, "every process, every broken rivet and the impact that it can have, every operational impact on the downstream." Purely IT-native AI companies can build great platforms, but they have no understanding of what a train sensor means when it fires, or what a power grid anomaly looks like at the physical layer. That knowledge takes decades to accumulate and cannot be trained in.

The same principle applies to any organization deploying AI. The institutional expertise, process understanding, and human judgment shaped by years in a specific domain are what AI cannot replicate from training data. Organizations that trade those capabilities for short-term AI efficiency will feel that cost.

How to Avoid Being A Bad Leader

Simon's challenge to leaders was direct: "If you are a leader in an organization and you are trying to solve, 'I need AI productivity in the next 12 months,' but you're not thinking about what the shape of your organization looks like five years and ten years from now in the context of all of this evolution — I'm sorry, you're doing a bad job as a leader."

When Sachin pushed back, noting that most executives are anxious because things are moving so fast they can't see 12 months ahead, let alone 10 years, Simon reframed the challenge. Predicting the technology isn't the answer, he said. Protecting the core is. "What's the core value of the company? If I believe that there's an essence of my core value that's going to stay intact or going to evolve around that perimeter five, ten, fifteen years from now, how do I protect that core value and build the right knowledge systems, technology systems, and other systems around it?"

Hitachi runs 50-to-70-year planning cycles, with a dedicated think tank working through mega-trends (climate change, social-political unrest, demographic divides, resource constraints) and working backward to identify what to build today. The company is 115 years old and still regularly appears on lists of the most innovative companies in the world.

Leaders who overlook creativity, ideation, and the next generation growing through their organizations as they race to deploy AI will eventually face a set of problems that a new model release won't address.

What Hitachi Is Building: iQ and iQ Studio

Simon's reflections on long-term thinking are grounded in something concrete: a product stack that Hitachi Vantara has been building and refining for years.

The Problem: Accelerating AI ROI for Enterprise Buyers Under Pressure

The fundamental challenge in the market, as Simon put it, is "the delivery of AI ROI and faster time to ROI."

He described how that pressure is reshaping the enterprise buyer. Previously, IT purchasing was an IT decision: the business shipped requirements over the wall, IT negotiated the best price, and everyone took what they got. That model has broken down. Line-of-business leaders are now in the room asking about ROI, and increasingly, AI spend is being charged to their budgets rather than IT's. It's changing the financials, the commercial model, and the nature of the conversation with customers.

More than 50% of Hitachi Vantara's customer base is large banks, healthcare companies, and governments. Forward-thinking in some areas, highly cautious in others. With that kind of customer, deployment involves extended POC phases where they're validating governance, security, compliance, and accountability structures before anything goes to production. The tools themselves have to be sovereign, governed, and accessible to line-of-business operators.

The Solution: Vertically Integrated Stack, with Domain Expertise Built-In

Hitachi iQ is Hitachi Vantara's converged AI infrastructure stack, built on NVIDIA GPUs. It brings together compute, storage for both structured and unstructured data, and a platform to manage across them at high performance, scalable from small deployments to very large ones through what Simon described as "t-shirt sizing" to match customer requirements.

Hitachi iQ Studio is the software layer on top. Launched in a first version and then significantly updated with a major release, iQ Studio is a low-code, no-code agent builder with pre-built blueprints, automations, integrations, and MCP support baked in.

Its differentiator is what comes pre-loaded: Hitachi's R&D teams have been building and perfecting specialized algorithms for specific industrial use cases (trucking, fleet management, predictive maintenance) long before the LLM era. Those algorithms are now folded directly into iQ Studio as ready-to-use blueprints. A line-of-business operator can spin up a governed, sovereign AI agent using industry-specific building blocks validated by decades of operational work, without touching code or leaving their own infrastructure.

Simon described it as "an essential glue layer to work between the underlying infrastructure and the overlying applications," the connective tissue between the AI data foundation and the real-world workflows running on top of it.

The Approach: Heterogeneous by Design

The platform is designed to be heterogeneous and inclusive. MCP is built in for model-agnostic orchestration regardless of where the data sits (the approach to data itself is hybrid by default). Simon described a view they've held for years: enterprise data will never be entirely public cloud or entirely on-premise. It will always require managing across multiple environments, with the ability to orchestrate across them and get "the right data from the right place at the right time."

His go-to-market framing was equally clear: "Our architecture will allow you to use this even if you don't use our product in the background. But if you use our product, all the better for you." That stance mirrors elvex’s beliefs about not locking enterprises into a single  vendor's roadmap.

Changing Personas and AI Risk Profiles

The BizTech Buyer Is the New Enterprise Persona

Simon has watched the enterprise buyer change significantly in recent customer conversations. IT used to own the purchasing decision: it was complex, technical, and largely opaque to the business. Line-of-business leaders are now in the room asking about ROI, and AI spend is landing on business-unit budgets.

Simon called this person the "BizTech buyer" and said it's "the new persona that we have to try to sell to." This person is the hybrid persona, straddling technical and business domains, that is increasingly the person who has to make AI decisions and justify their value to a cross-functional stakeholder group.

He watched the tension play out with his own customers: traditional storage people who know the world is changing around them and now need to think outside their box. "The world is being eaten from both sides," he said. The tools, conversations, and onboarding motions the old enterprise buyer needed are a poor fit for this one.

70% of CIOs Said They'd Shut Down Agent Access Entirely

When autonomous agents came up in a room of CIOs and CFOs Simon spoke to, he took a live poll. Would they let employees run with agents and patch security risks as they went, or shut access down entirely? Seventy percent said they'd shut it down.

When employees build their own agents and download third-party skills, they're often connecting to external repositories they don't fully understand. Simon cited data showing that 60% of individually-built apps in enterprise environments carry day-zero vulnerabilities, and every dependency those agents pull in extends the attack surface.

His position is that human-in-the-loop is a required design element. "Where is your point of someone looking at it, someone validating it, but also someone taking accountability for it?" Even Hitachi is still working through the accountability question internally. Simon compared it to drones in the military: intermediary systems that create a separation between action and accountability. "Trust is very, very difficult to build, but very easy to break. It's fragile."

He also echoed a point Sachin raised: enterprises that opt out of agents entirely will lose ground to startups willing to accept the risk and move faster. Governance is the path forward.

About Simon Ninan

Simon Ninan is Chief Strategy Officer at Hitachi Vantara, where he leads AI and hybrid cloud strategy. He also serves on the Corporate Strategy Office of Hitachi, Ltd., advising on North American cross-business synergies.

• LinkedIn: linkedin.com/in/simonninan
• Hitachi Vantara: hitachivantara.com