The 5 Tenants of Network Modernization - Trace3 TrailblazersTV with HPE

In Episode 17 of Trace3’s Live AI series, Trace3 CTO Tony Olzak and Kevin Geron, CIO of Farm Credit Mid-America, tackle one of the biggest hurdles facing enterprise leaders today: managing the overwhelming influx of AI use cases.

While generative and enterprise AI offer limitless potential, organizations must establish clear frameworks to prioritize, govern, and execute high-impact initiatives without getting bogged down by noise or technical debt.

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In this episode, we cover:

How to filter and prioritize AI ideas based on business ROI and feasibility.

Establishing governance and risk controls without slowing down innovation.

Best practices for moving from experimentation to scalable enterprise deployment.

Real-world frameworks Trace3 uses to help organizations align AI strategy with business goals.

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CHAPTERS:

0:00 - Introduction & The AI Influx Challenge

01:45 - Why Organizations Struggle with AI Prioritization

04:10 - Frameworks for Evaluating AI Business Impact & ROI

07:30 - Governance, Risk, and Compliance in Rapid AI Adoption

10:50 - Transitioning from Proof of Concept to Production

13:40 - Key Takeaways & Building an AI Roadmap

15:30 - Final Thoughts & Closing

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In Episode 16 of LiveAI, host Josh Lindstrom (Senior Director of Data & Analytics at Trace3) sits down with industry leaders Jason Hardy (VP of Storage Technology at @NVIDIA ) and Kaycee  Lai (VP of Enterprise AI at @everpure-data ) to discuss how enterprises can move past the proof-of-concept phase and turn dormant data into tangible business outcomes.

From the evolution of data storage to extreme co-design, the explosion of agentic workflows, and the reality of physical and edge AI, discover what it truly takes to fuel modern AI factories and achieve measurable ROI.

Key Topics & Timestamps:

00:00 – Welcome to LiveAI: Moving AI from Philosophy to Production

01:03 – Guest Introductions: Jason Hardy (NVIDIA) & Kaycee Lai (Everpure)

02:07 – The Power of Co-Design: Building the Modern AI Factory

03:52 – Beyond Speeds & Feeds: Activating Stored Data for AI

06:00 – Fueling the AI Factory: Data Prep, Libraries & Blueprints

08:30 – Data Resilience, Readiness & Overcoming the Governance Hurdle

14:41 – Moving Out of PoC: How Enterprises Uncover & Prioritize AI Use Cases

18:05 – Aligning AI Blueprints to Industry Verticals & Clear ROI

27:10 – Model Strategy: Open Weights, Frontier Models & Tokenomics

30:38 – What’s Next in AI: Agent Swarms, Physical AI & Edge Inferencing

37:00 – Advice for the Next Generation: Creativity as the Superpower

42:50 – Closing Thoughts & Next Steps

Key Takeaways:

Data Is the Fuel: High-performance infrastructure means nothing if your data isn't prepared, resilient, and accessible to AI agents.

Use-Case Driven ROI: Successful enterprises don't just buy hardware—they align specific business pain points to tailored AI blueprints and workflows.

The Agentic Shift: Future enterprise productivity will be driven by swarms of 24/7 autonomous agents handling both structured and unstructured data across the network.

AI can transform customer experience—but without the right guardrails, it can also create new friction.

In this Trace3 PressBox conversation, Trace3's Asher Lohman and Michael Burr from @NICE-Systems discuss how organizations can use AI to augment contact-center teams, protect the human element, and connect automation to measurable business outcomes.

Topics include:

How CX is evolving from individual interactions to end-to-end customer relationships

Where organizations should begin with AI adoption

How to identify low-risk, high-value use cases

Why business problems should come before technology decisions

The role of human oversight, data access controls, and AI guardrails

How agentic and deterministic workflows can work togetherA CX transformation involving 7,700 agents

Chapters:

00:00 - Introduction: The Balancing Act of AI in CX

00:56 - Trace3 & NICE Partnership & Customer Journey Evolution

02:24 - NICE’s Cognigy Acquisition & CXAI Strategy

03:24 - Navigating People, Processes, and Tech in AI Adoption

05:07 - Identifying Real CX Pain Points vs. Symptoms

07:05 - Augmentation First: Preserving the Human Element

08:15 - Guardrails, Governance, and AI Risk Management

10:12 - Agentic Flows vs. Deterministic Workflows in Cognigy

12:13 - Case Study: Transforming a 7,700-Agent Financial Institution

14:00 - Key Takeaways & Closing Thoughts