Enterprises Have Plenty of AI Pilots. Production Is Where They Stall.

Roughly half of GenAI projects are abandoned after the proof of concept. The demos are impressive, but the trouble starts when the demo has to become a business.

Production AI rests on three tenets: a trusted data foundation, data activation, and governed agentic AI, all connected end to end. They serve four pillars of AI readiness: data, infrastructure, business, and operations. Most vendors solve one or two of them, which leaves customers to assemble the rest from separate tools and then spend their time stitching them together. A single, industry-ready solution that combines infrastructure, data, and governed AI as one path is the antidote.

But infrastructure alone is not enough. Production AI also needs data, business, and operations that are ready for it, with governance built in, so practitioners across the organization can adopt AI without creating risk. Explainable AI can no longer be a “nice to have.”

“By 2027, 60% of organizations that fail to address the cultural challenges associated with data and analytics governance will fail to govern AI successfully,” according to Gartner®^[1]. By adopting a data foundation that allows these cultural shifts, organizations are more likely to see business outcomes from production AI.

A Trustworthy Foundation That Scales

According to research conducted by Deloitte, only 21% of enterprises have mature governance in place to manage the risks of agentic AI. A second study from Accenture found that organizations leading in AI-ready data capabilities achieved profit margin uplifts of up to 1.6 times those of industry peers.

The destination is clear. The hard part is choosing a unified platform that actually gets you there.

If the data layer in a unified platform is slow to deploy, expensive to grow, or inconsistent from site to site, every AI project built on top inherits the problem, like a house built on a cracked slab. A strong foundation performs differently. It ensures a first deployment matches the actual workload instead of a worst-case forecast. It deploys easily, so teams spend their time on use cases rather than standing up hardware. And it scales as production demands grow, adding capacity without redesigning what already exists.

Southwest Power Pool (SPP), a regional transmission organization serving 14 states, is a good example of an organization leveraging the power of a unified platform. In partnership with Hitachi, SPP implemented an integrated AI-based solution that accelerated generator interconnection while also informing faster, higher-quality decision-making by customers. It also markedly improved SPP's ability to generate capacity to keep pace with increasing demand for electricity. Because pressure on the power grid is rising, capacity margins in SPP's footprint could fall from 24% in 2020 to 5% in 2029 without an intervention like this.

"Our industry has struggled to keep up with this sudden and significant shift," SPP President and CEO Lanny Nickell said. "There are a lot of would-be power producers out there waiting to connect to the grid."

To further address this challenge, SPP leveraged Hitachi’s robust offerings across group companies — from software engineering services and energy portfolio management asset modeling solutions, to AI-based energy grid algorithms and its integrated storage and compute platform Hitachi iQ.

Bringing Analytics to the Data

Every time data moves, AI loses a little momentum and a little trust. Moving data is another area where many AI efforts stall.

Copies, for example, can drift. If an explanation is generated against an extract that’s weeks old or was filtered differently, it may not match what the model actually saw. When analytics remain in-place, the explanation and the prediction are computed against the same data.

Tracing data lineage also becomes a risk when data is not analyzed in-place. Every time data is exported, reshaped, and loaded elsewhere, the trail back to its source gets fuzzier. When a model runs in place, however, it can trace any prediction back through the exact tables, columns, and transformations that fed it.

The answer is to bring analytics to the data, not data to the analytics. The challenge is becoming more acute as AI scales, and data gravity — the idea that data naturally attracts compute, services, and applications to where it resides, risks the loss of data control. Rather than continually moving more data through increasingly complex infrastructures, organizations are looking for ways to bring analytics to trusted data sources. In a 2026 survey by Google of more than 1,400 senior IT leaders, for example, 48% said they’re prioritizing infrastructure that provides data residency controls.

"Enterprises must embrace federated data access, enhanced governance, and automation to unlock business agility, improve trust, and position data as a core enabler of competitive differentiation." Stewart Bond, Vice President of Data Intelligence and Integration Software at IDC, said.

When queries run in place on trusted, governed data, teams make more enterprise data available to analytics and AI without multiplying copies, pipelines and infrastructure. Activating data means not collecting it into yet another repository but making the data an organization already trusts usable, in place, by the people, and agents who need it.

"Data stewardship programs are at the heart of the data foundation and data governance,” Simon Ninan, SVP of Business Strategy at Hitachi Vantara, added. “They serve as documentation for capturing data at the right time, for the right people, who manage it in the right way. That way, you have ownership, and data is tied to clear business value propositions.”

Governed AI Built for Industry Standards

Governance is the difference between a demo and a deployment. A demo only has to work once. A deployment has to work every day, for the right people, with an audit trail. That takes role-based access, observability, and agent lifecycle management, so teams have a controlled environment to build, connect and operate AI agents using enterprise data, models and workflows.

With that in place, trusted data becomes practical industry outcomes, without giving up control. Solving one or two pillars gets you a pilot; connecting all four gets you production. Here’s what that looks like across industries.

Moving From Infrastructure to Outcomes

The value comes from the connection between layers, not from any single product, and that connection is what point solutions can't offer. Picture one flow: data lands on a scalable foundation sized to the site, is queried in place without extra copies, and is used by a governed agent to complete a real workflow, with access controlled and every step observable.

That is the shift from infrastructure to outcomes, and it is how pilots become production AI.

Pilots are easy. Production is the hard part. See how Hitachi Vantara's solutions close the gap.

^[1]Gartner Press Release, Gartner Predicts 60% of Organizations That Ignore Data Governance Culture Challenges Will Fail to Govern AI Successfully by 2027, September 21, 2026. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.