There is no clean handoff when extreme weather hits.
One moment, systems are operating normally. The next, leaders and frontline teams are making high-stakes decisions under pressure. Power restoration. Resource deployment. Public safety. Trust.
These moments do not wait for strategy decks, governance frameworks, or perfect data models. They demand action, often with incomplete information and very real consequences.
As Fred Knops, Senior VP, Energy and Utility Solutions, reminded us in a recent conversation, “We should never think that the era of large-scale power outages is behind us. They don’t require a storm to make them happen.”
Extreme weather does not just test infrastructure.
It tests how well organizations understand their data and how prepared their people are to act on it.
The First Hours Reveal What Really Matters
Across the energy and utility sector, advanced analytics and AI are no longer experimental. Dashboards, predictive models, drones, sensors, and real-time monitoring are part of everyday operations. Many organizations have invested heavily in technology over the last decade, and that progress matters.
And yet, outcomes still differ dramatically from one organization to another when systems are under stress.
Why?
Because tools alone do not create clarity.
What matters in the first critical hours is not the volume of data available, but whether people know how to interpret it, prioritize it, and trust it enough to act.
Valerie Logan, Chief Strategy Officer and Founder of The Data Lodge, described those early moments this way: “It’s about situational awareness and adaptability. What information is available, who has it, and how quickly decisions can be made.”
In those moments, data quality and access matter. But literacy is what allows teams to turn information into insight and insight into action. Without shared understanding, even the best tools can slow response instead of accelerating it.
Data Abundance Is Not the Same as Data Readiness
One of the defining challenges facing utilities today is scale. Over the last decade, the amount of data flowing through operational systems has grown exponentially. Sensors, video feeds, predictive analytics, and new data sources have fundamentally changed how decisions are made.
This should be an advantage. And often, it is.
But it also introduces complexity.
As Fred noted in our conversation, tools are advancing quickly, but the expectations placed on people are rising just as fast. “The bar keeps getting higher. Tools are advancing, but literacy is lagging.”
In many organizations, data fluency is uneven. Certain teams or roles may be deeply comfortable with analytics, while others rely on intuition or experience alone. During routine operations, those gaps can stay hidden. During a crisis, they become visible very quickly.
Why ROI Is Hard to Measure Until It Is Not
Leaders frequently ask how to measure the return on investments in data infrastructure, analytics platforms, and AI upskilling. In regulated, interconnected systems like utilities, attribution is complex. There are too many variables, too many dependencies, and too many external factors to point to a single cause-and-effect relationship.
As Valerie explained, “It’s nearly impossible to isolate the true return because there are so many systems and variables involved.”
But when weather disrupts operations at scale, the value of those investments becomes easier to see.
“These moments are the real gauge,” she added. “Are your investments paying off when it matters most?”
Faster restoration. Clearer decisions. More confident coordination between leadership and frontline teams. These outcomes are not accidental. They are the result of sustained investment in both systems and people.
Technology Rarely Fails First
One of the most important insights from our work across energy and utilities is this: technology is rarely the first thing to fail.
More often, challenges trace back to how information is interpreted, communicated, or acted upon. Confusion over dashboards. Uncertainty about model outputs. Lack of shared language between technical teams and operational leaders.
Fred framed this shift clearly: “The question isn’t where have we failed. It’s where could we build on our successes to do even better.”
This is an important distinction. Many utilities have made real progress in resilience, prediction, and recovery. The work now is about building on those gains by strengthening the human systems that sit alongside the technology.
Readiness Is Built Long Before the Storm
One of the most consistent patterns we see is that truly ready organizations do not wait for crises to learn. They invest ahead of time, when there is space to reflect, experiment, and improve.
They listen closely to frontline expertise.
They design learning around real decisions, not abstract tools.
They focus on shared language, mindset, and skills, not just software adoption.
Valerie highlighted a critical challenge many organizations face: “You can learn these capabilities in a classroom, but it’s not until they are tested in real moments that readiness is revealed.”
This is why continuous data and AI upskilling matters. Not as a one-time training initiative, but as an ongoing capability tied directly to operational reality.
AI Raises the Stakes for Human Understanding
As AI systems take on a greater role in decision support, forecasting, and automation, the need for human understanding becomes even more important.
AI does not eliminate judgment. It changes where judgment is applied.
When agents and models are generating insights at speed, people must be able to assess confidence, understand limitations, and decide when to intervene. That requires a level of fluency that goes beyond knowing which button to click.
As Fred reminded us, “Even highly technical organizations can develop blind spots. The definition of data fluency has changed very quickly.”
In other words, past expertise does not guarantee future readiness. The pace of change demands continuous learning and honest self-assessment.
Learning While the Moment Is Fresh
Post-mortems matter. They provide structure, accountability, and long-term insight. But learning is strongest when it happens close to the experience.
Extreme weather events create a rare opportunity to see what actually worked and what did not. Where information flowed smoothly. Where decisions stalled. Where people felt confident, and where they did not.
Organizations that capture those lessons while they are still fresh are not just responding to the last storm. They are actively preparing for the next one.
A Conversation Worth Having Now
Extreme weather events make one thing clear very quickly.
Some capabilities show up when pressure is real. Others do not.
The organizations that learn the most are the ones willing to talk honestly about what worked, what did not, and where readiness still needs to be built.
If you are reflecting on your own experience, whether gaps surfaced in data understanding, decision-making, or workforce confidence, this is the right moment to talk it through.
Fred Knops, Senior VP, Energy and Utility Solutions, works closely with energy and utility leaders navigating exactly these challenges. Not in theory, but in real operational environments under real-world stress.
If you want to discuss what this storm revealed for your organization and what it means for your data, AI, and workforce readiness going forward, we invite you to connect with Fred.
Set up time to continue the conversation.
To go deeper, watch the full discussion with Valerie Logan, Chief Strategy Officer and Founder of The Data Lodge, and Fred Knops, Senior VP, Energy and Utility Solutions. The conversation offers a thoughtful perspective on how data, AI, and workforce readiness show up when systems are under real-world stress.
Frequently Asked Questions: Data and AI Readiness During Extreme Weather
Data readiness is not about volume or sophistication. It is about whether people know how to interpret information, prioritize what matters, and act on insights with confidence. Organizations can have dashboards, AI models, and real-time monitoring in place and still struggle if teams lack shared language, situational awareness, or trust in the data during a crisis.
