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This episode demystifies AI agents, explaining how they differ from chatbots and showcasing their real-world impact in business applications. Chris Fairfax, Technical Product Manager for Industry Cloud at Hitachi Solutions, shares actionable insights on deploying agents to drive efficiency, empower employees, and unlock new value from enterprise data.

Highlights:

• Agents are AI-powered tools that execute business processes, going beyond simple chatbots by leveraging multiple skills within platforms like Microsoft Dynamics.
• Automation with agents can increase productivity by 20–30%, but the focus is on augmenting—not replacing—employees through human-in-the-loop design.
• Practical use cases include process management, identity verification, call center workflows, and field service automation.
• Data quality and a modern data estate are essential for successful agent deployment and scalability.
• Early wins are seen in industries like insurance, with plans to expand agent capabilities for industry-specific needs.
• Transparency, confidence scores, and purposeful design are key to building trust and driving adoption of AI agents.

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In this episode of Exchanges with Hitachi Solutions, host Ginny Lebeck joins Matt Volke, Chris Satterly, and Mark Shoesmith to preview FabCon 2026 in Atlanta. The conversation explores why FabCon has become a must-attend event for data and AI professionals, how customer expectations around data have evolved, and what organizations need to succeed on their AI journey. Drawing from real-world client conversations and healthcare use cases, the panel discusses Microsoft Fabric as a unifying data platform, the importance of strong data foundations and governance, and how emerging capabilities—from low-code tools to advanced analytics—are enabling teams to move faster and unlock more value from their data. You can find Hitachi Solutions at Fabcoon Booth #545 March 18-20.

Highlights:

• FabCon brings together customers, partners, and Microsoft experts to share practical insights on data management, analytics, and AI, with a strong emphasis on real-world business challenges.
• Organizations are increasingly treating data as a strategic asset rather than a byproduct, focusing on real-time insights, advanced analytics, and AI readiness.
• Microsoft Fabric is emerging as a key enabler, simplifying data ingestion, modeling, and curation while supporting both self-service analytics and advanced data science workloads.
• Healthcare organizations are adopting Fabric to modernize legacy, on-premises data platforms, reduce manual ETL effort, and better handle diverse and high-volume data sources.
• Tools like pipelines, copy jobs, mirroring, and Copilot-assisted development help small data teams work more efficiently and respond faster to analytics demands.
• Strong data models, governance, and curated information are essential prerequisites for successful AI and advanced analytics initiatives.
• Hitachi Solutions’ presence at FabCon focuses on connecting customers with experienced data engineers, data scientists, and industry experts to help translate technology into outcomes.

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Generative AI has captured the imagination of the enterprise world—but the reality is more complex than the hype. In this fireside chat, Stuart Morris, Director of R&D at Hitachi Solutions, joined me to unpack the findings of MIT’s “Gen AI Divide” report and share practical insights from the front lines of AI deployment.

The conversation begins with a sobering statistic: 95% of generative AI pilots fail to deliver measurable impact on P&L. But as Stuart points out, that doesn’t mean they’re worthless. Many projects drive internal process improvements, enhance compliance, and streamline operations—benefits that don’t always show up on a balance sheet but are critical to business success.

In our chat above, Stuart emphasizes that readiness is key. Companies often jump into AI without ensuring their data is clean, organized, and accessible. “There’s no amount of AI you can throw at a data situation that’s in shambles,” he warns. Instead, success starts with foundational work—cleaning up CRM systems, structuring data warehouses, and defining clear business processes.

This discussion highlights a recurring theme: back-office automation consistently delivers higher ROI than customer-facing AI. Internal processes are well-documented and easier to optimize, while public-facing AI carries reputational risks. Stuart shares examples of chatbots behaving badly on social media, underscoring the importance of starting with internal use cases.

Key Takeaways:

• Most AI pilots fail to impact P&L—but still deliver internal value.
• Data readiness is essential; clean, structured data is the foundation.
• Back-office automation offers higher ROI and lower risk than customer-facing AI.
• Targeted, repeatable solutions outperform one-off proofs of concept.
• Avoid vendor lock-in by deploying in customer tenants and delivering code.
• Success lies in purposeful design, tailored agents, and a steady approach to innovation.

global.hitachi-solutions.com