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AI initiatives rarely fail because the technology isn’t powerful enough. More often, they stall long before delivering measurable impact, stuck in pilot mode due to gaps in data readiness, architecture, and long-term sustainability.

In this episode of Exchanges with Hitachi Solutions, I sat down with Evan Sotos, Senior Manager of Engineering, to break down the three most common hurdles that slow AI progress, and how organizations can move through them faster. From unclear data foundations to time-consuming infrastructure work and ongoing maintenance fatigue, these challenges are familiar to any team trying to operationalize AI.

Evan shares a practical perspective shaped by real-world implementations, along with how the Hitachi Unified Data Accelerator helps organizations bypass months of setup and complexity. By providing a proven foundation, built on best practices, flexible architecture, and preconfigured connectivity, teams can spend less time standing up environments and more time actually using their data to drive outcomes.

This conversation sets the stage for what it really takes to move from experimentation to execution, and how to build an AI foundation that’s designed to scale.

Key Takeaways

AI Stalls at the Starting Line
Most AI projects don’t fail because of weak models. Instead, they stall because the data isn’t ready. As a result, teams get stuck before they can show real business value.

Data Readiness Is Ongoing
There’s no finish line for data readiness. Instead, it depends on the outcomes you want to drive. As goals change, your data needs to evolve with them.

Setup Slows Progress
Before AI can deliver value, teams must build the foundation. That includes setting up environments, connecting data, and testing models. However, this work can take months and delay impact.

Governance Enables Scale
As AI expands, so does access to data. Therefore, strong governance is critical. Teams need clear controls for access, security, and data lineage to reduce risk.

Manual Work Holds Teams Back
Much of the effort goes into building and maintaining pipelines. Because of this, teams spend less time solving real business problems with data.

A Faster Way to Get Started
The Hitachi Unified Data Accelerator helps teams move faster. It brings together best practices, proven architecture, and prebuilt connectors. As a result, organizations can skip setup and focus on outcomes.

Less Maintenance, More Impact
Instead of adding another layer of work, the accelerator reduces it. For example, it minimizes ongoing fixes and updates. This allows teams to focus on insights and innovation.

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Modern data platforms are evolving—and speed, scale, and efficiency are becoming non‑negotiable.

In this episode of Exchanges with Hitachi Solutions, host Matt Volke sits down with Evan Sotos, Engineering Manager for the Empower Data Platform, fresh off his return from NVIDIA GTC. Together, they explore how GPU acceleration is moving beyond AI and machine learning—and into the core of data engineering.

The conversation dives into what Evan heard from engineers, partners, and vendors at GTC, why NVIDIA is positioning itself as an algorithms company, and how technologies like NVIDIA RAPIDS are being used to dramatically accelerate analytics and data pipelines without rewriting existing code.

What You’ll Learn

· Why GPU acceleration is becoming a core capability for modern data platforms, not just AI workloads

· What NVIDIA RAPIDS is and how it enables existing CPU‑based workloads to run on GPUs

· How GPU acceleration can significantly reduce processing time and overall compute costs

· Why “zero code changes” is such a critical advantage for real‑world data teams

· Which types of data workloads benefit most from GPU‑accelerated pipelines

From AI Buzz to Real‑World Data Engineering Impact

While NVIDIA GTC is often associated with AI and large language models, this conversation highlights a broader shift: GPUs are increasingly being applied to traditional data engineering and analytics workloads.

Evan shares how NVIDIA RAPIDS acts as a mapping layer that allows existing Spark and Databricks workloads to take advantage of GPU compute. Rather than forcing teams to refactor complex, production‑grade code, GPU acceleration can be enabled through configuration—making it practical for teams to test, validate, and adopt without disruption.

The result? Faster pipelines, improved cost efficiency, and a shorter path from raw data to actionable insight—especially for large, time‑sensitive workloads.

What This Means for Data Teams

For organizations running large‑scale analytics, predictive models, or operational reporting, time truly is money. Evan explains how accelerating data pipelines can directly impact downstream use cases—from predictive maintenance to real‑time decision‑making—by reducing the lag between data ingestion and insight.

Most importantly, this episode emphasizes practicality: GPU acceleration isn’t about chasing hype. It’s about giving data teams another tool they can turn on, test, and adopt when it makes sense—without introducing risk, rework, or operational complexity.

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The Future of Field Service: From Cost Control to Value Creation

Field service is evolving—and value is the new metric that matters.

In this episode, Ginny Lebeck, Michael Mendoza, and Boris Klimovitsky break down the biggest shifts shaping modern field service, from AI adoption to revenue‑driven transformation. Learn why leading organizations are moving beyond technology for technology’s sake and focusing on outcomes that drive real business impact.

What You’ll Learn

• Why value‑driven transformation is replacing cost‑only strategies
• How AI is unlocking new revenue and operational opportunities
• What it means to “go beyond the field” with end‑to‑end service optimization
• How to measure and prove the value of AI in service operations

Going Beyond the Field

True service transformation doesn’t stop with technicians. This conversation explores how organizations are extending service impact across customers, processes, and the broader business—connecting people, processes, and AI to deliver smarter, more scalable outcomes.

Meet Us at Field Service Next – San Diego

The team also shares what they’re looking forward to at Field Service Next, including connecting with peers, customers, and partners who are navigating these same challenges. If you’re attending, stop by booth #215 to continue the conversation.

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