From Data Work to Decision Work: The Shift Energy and Sustainability Teams Can't Afford to Ignore

We know how busy your corporate energy and sustainability teams are. They are working long hours, generating massive output: the reports, the filings, the data analysis. But there is a hard ceiling on what’s possible when most of that effort goes into managing data rather than making decisions with it.

Many of brightest minds in energy and sustainability often find themselves trapped in a cycle of administrative maintenance. They aren't disconnected from strategic, company-level decisions because they lack talent or ambition. They’re disconnected because they are buried. They’re chasing utility invoices across dozens of regions, cleaning spreadsheets from legacy systems, and trying to keep up with volatile energy pricing. And every hour they spend extracting and formatting that information is an hour stolen from the strategic work that actually moves the business forward.

This lopsided dynamic dictates how the vast majority of any given week disappears for the average corporate energy and sustainability team.  The remainder of their time is left for the actual decision work, which encompasses the complex choices about which markets to enter, which suppliers to retain, and where to allocate capital for the biggest carbon reduction per dollar spent. These are the highly strategic decisions energy and sustainability leaders were hired to make, trained for, and built their careers around. Yet, instead of driving the business forward, this vital decision work receives only leftover margins of the week.

One sustainability leader summarized the frustration perfectly during a recent working session: "We spend so much time collecting and cleaning data that we have almost no time left to actually reduce emissions."

That reality should make every business leader pause and reconsider how their teams are structured. The very people tasked with reducing a company's environmental footprint are spending the bulk of their professional lives acting as data wranglers, trapped by the rigid demands of the legacy systems and fragmented processes they inherited.

The pace problem

The friction between data work and decision work is only getting worse as the speed of business continues to accelerate around us. Energy markets that used to offer long, comfortable windows for analysis now move in days, while regulatory expectations shift in months and supply chain disruptions land on the CFO's desk without warning.

The decision window is rapidly shrinking, yet the manual effort required to gather the data hasn't gotten any faster. You cannot simply hire your way out of this structural compression, because adding more headcount to a broken workflow just creates a larger, more expensive management headache. People are forced to triage their days, picking the most urgent fire to extinguish while pushing strategic, long-term thinking to some mythical future week when things finally calm down.

Where the decision work actually happens

The opportunity here is specific, and it fundamentally redefines the role of both internal experts and the hired out external consultants. Energy and sustainability teams sit at the intersection of capital allocation, regulatory exposure, supplier strategy, and enterprise risk.

Emissions strategy isn't just a reporting exercise anymore; it's a massive capital deployment question. Decarbonization investment is a portfolio allocation decision with a decade-long time horizon, and supplier risk directly impacts revenue continuity. Handling these challenges requires deep, context-heavy decision work. While accurate data informs the process, the actual choices demand human expertise, industry nuance, and the deep institutional knowledge that your internal sustainability directors and your trusted consulting partners bring to the table. That collective expertise is your organization's most valuable asset, and right now, it is caught in an unresolving loop of monotonous data work.

A visionary operating model

The conversation in energy and sustainability circles needs to shift away from generic automation toward a much more visionary question: what if the data work had an entirely different place to go?  The energy transition will not wait for sustainability teams to finish cleaning spreadsheets; the function has to evolve, or it risks becoming the bottleneck to the very outcomes it was built to drive.

Imagine an operating model where the heavy lifting of collection, validation, reconciliation, and anomaly detection happens continuously in the background. In this model, software that acts like teammate handles the pattern-matching and data processing without constantly demanding a human's attention at every step, only at critical steps. We are already seeing this become a reality. This isn't hypothetical. Just last month, a corporate sustainability leader watched watched an intelligent agent detect an anomaly in energy data, pull the original utility invoice, identify a transcription error made by a third-party firm, cross-reference it against historical patterns, and correct the record before anyone had even opened their laptop for the day. Multiply that single interaction across thousands of daily data points, and the true value emerges. By resolving endless streams of data maintenance quietly in the background, the technology restores the most valuable asset teams have: the time to actually make decisions.

The path from data work to decision work is not a straight line, and applying artificial intelligence will not magically remove all the energy, carbon management, climate risk or reporting bottlenecks - it may just move the bottlenecks. As artificial intelligence takes on the data collection, connection and quality burden, it trades one problem for a better one. Instead of struggling to piece the numbers together, teams will likely be handed an unprecedented wave of insights and optimization paths to act on. That is not a problem to fear. It is the next challenge to solve.

This creates a new risk to anticipate: decision fatigue. When a system presents fifty optimized pathways for decarbonization, a human still has to choose one. The challenge of the future may not be surfacing the insights; it will be filtering the noise to laser in on exactly what matters. The ultimate value of this technology is about clearing the path so experts can make the hard, strategic decisions where accountability has to stay with a human, not a model.

The leadership invitation

For the first time, technology is starting to match the scale of what energy and sustainability actually demands. But the shift from data work to decision work isn't a technology choice, it’s actually a leadership invitation.

It asks you to look at your best internal experts and your most trusted partners — the people who understand the complexity of your energy portfolio, your carbon exposure, your supply chain risk — and ask an honest question: are they doing the work that only they can do? Or are they doing work a well-built system could handle in seconds?

The leaders who figure this out first won't just move faster. They'll move differently.

That difference is yours to define, starting now.

Contributor:

Jen Velarde-Menary, Sr. Product Marketing Manager