Trace3's LiveAI | Episode 15 | Improving Visibility & Trust in Digital Delivery with Dynatrace
December 18, 2025
In this episode of LiveAI, host Josh Lindstrom talks with Nish Rama from Dynatrace about how they partnered with Kroger to transform their digital delivery. By consolidating 16 different monitoring tools into one observability platform, Kroger's "Captain America" SRE team shifted from manual troubleshooting to high-level innovation. The discussion highlights the move from traditional IT metrics to business-focused outcomes, such as tracking revenue lost due to failed orders, and the future of agentic AI where systems can identify and fix vulnerabilities before an engineer even wakes up.
Chapters
00:00 AI as an Enhancement: Opening thoughts on AI being an "enhancement" rather than a replacement for human thinking.
02:18 The Kroger Use Case: Nish explains the initial challenge at Kroger: over 16 fragmented tools and data silos making troubleshooting slow and difficult.
04:12 Scale of Kroger’s Operations: Highlighting Kroger’s massive footprint (2,800 stores, 9 million customers) and the critical need for seamless mobile app and checkout experiences.
05:42 Beyond Traditional IT: How Dynatrace helps solve customer experience issues rather than just "keeping the lights on."
06:40 Business-Centric SLOs: Moving from basic uptime metrics to tracking business impact, such as revenue lost during high-traffic events like Thanksgiving.
08:22 Freeing Engineering Time: The transformation of the SRE team from "ticket jockeys" to innovators, reducing tickets from 700 to 7 per day in just one month.
10:43 The Importance of Context (Davis AI): A look at Dynatrace’s AI engine, "Davis," which auto-baselines environments to provide the "who, what, where, how, and why" of any problem.
15:42 Opening the Ecosystem (MCP): Discussing the Model Control Plane (MCP) and how Dynatrace integrates with developer tools like GitHub Copilot to automate vulnerability patching.
21:30 The Future of AI Observability: Shifting the perspective of observability from a "nice-to-have" pilot phase to a core part of an agentic platform.
24:30 The Agentic CIO: Exploring the concept of an "Agentic CIO" who can provide natural language answers to complex CEO questions about revenue and customer impact during outages.
27:31 Final Advice: Nish emphasizes that observability should be built into every AI initiative from the start to ensure trust and transparency.
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Trace3's Live AI | Episode 17 | Managing the Deluge of AI Use Cases
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.
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.
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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Trace3's LiveAI | Episode 16 | Turning Data into Business Value with NVIDIA and Everpure
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 in Customer Experience: Guardrails, Automation & the Human Element
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