This recap includes the following episodes and guests. 
Guest title reflects the guest's position at the time of the episode.

Recipes:

• Understanding Scope 1, 2, and 3 Emissions

• Use Data-Driven Infrastructure Insights

• Leverage AI for Infrastructure Efficiency

• Act on What You Measure

• Art of Automation

• Season with Diverse Perspectives

• Executive Recipe: Turning Sustainability Ambition into Action

🧑‍🍳 Cookbook Chapter: Intelligent Infrastructure Strategies

🌍 Understanding Scope 1, 2, and 3 Emissions

🎯 Goal: Use data to drive sustainability across the supply chain.

Mike Hollinger broke down the three scopes of emissions and explained how data plays a critical role in managing them:

The takeaway? Organizations need to orchestrate and normalize data across systems to understand their full environmental impact—and act on it. Scope 3, in particular, requires collaboration, transparency, and smart data integration to uncover hidden emissions and drive meaningful change.

🥄 Practical Tips:

• Inventory your data sources across IT and OT systems.

• Use carbon accounting tools to visualize and analyze emissions.

• Prioritize Scope 3 visibility by collaborating with suppliers and partners.

🍳 Use Data-Driven Infrastructure Insights

🎯 Goal: Optimize physical and cloud systems through measurement.

In our conversations, Mike Hollinger emphasized the power of data as the foundation for sustainable infrastructure. He said, “We have this data, we have the ability to drive insights.” That’s the first ingredient in our recipe—knowing what you have before deciding what to cook. Jerry Cuomo added, “In the precious data logs of SREs lies answers.” It’s not just about collecting metrics; it’s about surfacing the right ones with context.

🥄 Practical Tips:

• Start with a clear question before building a dashboard.

• Bridge IT and OT data sources to get a full picture.

• Use consumption metrics alongside service-level indicators to guide decisions.

⚡ Leverage AI for Infrastructure Efficiency

🎯 Goal: Apply predictive analytics for capacity and energy usage.

AI is the sous-chef in our infrastructure kitchen. Jerry reminded us, “Striving for autopilot brings amazing things.” But it’s not about handing over control—it’s about augmenting human judgment. Kevin (me!) shared how tools like Instana are changing the game: “I can pick a golden signal and apply it across all systems in seconds.” That’s automation with purpose.

🏗️ Act on What You Measure

🎯 Goal: Drive real-world change from digital insights.

Mike’s story about the Sund & Baelt bridge was a standout. “The fusing of visual data with sensor data… lets the team choose where to invest.” That’s what intelligent infrastructure looks like—data that leads to action. I added, “Why don’t we surface that context in the first place?” Let’s stop making engineers dig through dashboards and start giving them answers.

🤖 Art of Automation

🎯 Goal: Eliminate toil and scale reliability with smart systems.

🥄 Practical Tips:

• Identify high-toil tasks and automate them with AI-assisted workflows.

• Use historical failure patterns to guide automation triggers.

• Don’t automate blindly—always validate with service-level outcomes.

🧂 Season with Diverse Perspectives

🎯 Goal: Challenge assumptions and enrich infrastructure decisions.

Innovation thrives on diversity. Mike shared, “If we can’t answer why we’re doing it this way, it’s a trigger for change.” That’s the kind of question that leads to better systems. Jerry added, “Technology gives us back the greatest gift—time.” But only if we design it with intention and inclusivity.

🥄 Practical Tips:

• Invite cross-functional teams into infrastructure conversations.

• Use ESG frameworks to guide sustainable design choices.

• Encourage questioning of legacy practices—especially from new voices.

🧭 Executive Recipe: Turning Sustainability Ambition into Action

🎯 Goal: Guide leadership teams to operationalize sustainability through data, insight, and action.

In our conversation, Mike laid out a clear and practical framework for C-suite leaders looking to move from sustainability ambition to measurable outcomes. His advice was grounded in real-world experience and focused on data as the starting point.

“The first thing is understanding what data you have… you might be surprised at what you’re already paying for.”
“Once you know what you have, ask what you need—and what gaps you must fill to take action.”

Jerry reinforced this with a call to embrace AI as a productivity tool, not a replacement for human insight:

“Become a prompt engineer. It doesn’t require a degree—just curiosity and repetition.”
“Go shopping for the smallest possible model that’s documented and fine-tune it for your tasks.”

Together, their insights form a practical 4-step recipe for executives:

🪪 Step 1: Inventory Your Data

• Audit existing systems across IT and OT.

• Identify silos from M&A, legacy systems, or departmental divides.

• Understand what telemetry, usage, and environmental data is already available.

🧩 Step 2: Identify Gaps and Needs

• Ask: What decisions do we want to make?

• Determine what data is missing to support those decisions.

• Consider inferencing models where direct measurement isn’t possible (e.g., cement production).

🔁 Step 3: Build Feedback Loops

• Use dashboards not just for visibility, but to drive action.

• Connect insights to operational systems—whether it’s rolling a truck, reallocating VMs, or shutting down idle equipment.

• Ensure every insight has a path to execution.

🧠 Step 4: Empower Teams with AI

• Encourage teams to experiment with prompt engineering.

• Collaborate with data scientists to fine-tune models for specific use cases.

• Focus on augmentation—AI that assists, not replaces.

Mike summed it up perfectly:

“If I have a dashboard and take no action from it, what good is it?”

And Jerry added:

“Spend more time on things your users appreciate—nothing’s better than working on a product people love.”

This recipe isn’t just for sustainability—it’s for building smarter, more resilient organizations.

🧠 Questions to Stir the Pot

1. What’s one automation you’ve implemented that didn’t deliver the expected value—and why?

2. How do you decide which metrics are worth acting on versus just monitoring?

3. What’s your process for validating that an AI model is truly helping your infrastructure goals?