Executive summary
Energy companies might feel like their value is chained to commodity prices, as they struggle to differentiate themselves from competitors. Data can be the key to differentiation, giving companies the power to forecast, adapt and inform energy delivery — while giving them better insight into their own future cash flow and value. Energy companies are now adopting and advancing AI-driven capabilities that build insight, resilience and enterprise value.
Data-driven forecasts can build resilience
The value of energy companies can rise and fall with the price of a commodity. That makes it hard for companies to forecast their futures — but forecasts are essential for planning and adaptation.
Forecasts are also essential to show resilience. “Resilience is built by setting expectations and then meeting those expectations,” said Grant Thornton Global Valuation Services Co-Lead and CFO Advisory Services Partner Bryan Benoit. “To maintain resilience, you need to make your employees, shareholders, banks and others happy by delivering on their expectations for your company.”
Energy companies must develop forecasts in a turbulent environment where commodity price changes can impact their outlook and perceived value. Grant Thornton Energy Industry Leader Tyler Jones explained, “Cash flow forecasting for energy companies is particularly critical, because leaders should have a number of scenarios they can evaluate as commodity prices change. Those scenarios determine how you generate cash flows. So, the earlier you can pull levers and mitigate or reduce expenses on the service or supplier side, the better equipped you’ll be for resilience through the downturns.”
“Companies that serve or supply energy companies need to understand at what price points they will curtail activities,” Jones said. “That becomes really important to your forecast and, by extension, your ability to be resilient.”
So, when do companies need to adapt — and how can their adaptations keep up with the accelerating speed of change?
The speed of change
For energy companies, volatility is nothing new. “Energy prices are never consistent, and you have more than just domestic factors at play,” said Grant Thornton CFO Advisory Services Managing Director Keith Klemowits. “Global conflicts and other issues create a lot of volatility in addition to the normal ebb and flow of the energy prices. So, it’s hard to forecast the price out of the gate.”
Jones agreed, “Fluctuating commodity prices, which are the core of cash flow generation for all energy companies, are a challenge. Then, the further you get away from the well, it becomes more challenging to correlate the price change to how that changes activity levels.” Production levels add layers of volatility. “If you have a significant deviation in output, multiplied across hundreds of wells, those fluctuations add significant volatility to the core business beyond the commodity price,” Klemowits said.
The speed of change has been accelerated by recent political and trade instabilities, so companies can lose significant value if they wait for a quarterly review to adapt. To keep up with changes, companies need to adapt dynamically.
The ability to adapt
AI-driven forecasts and planning can help energy industry leaders quickly move to the most profitable path as the landscape shifts. Benoit noted, “Mid-market energy companies know that forecasting is key, but their staff is consumed by other day-to-day needs that are mission-critical. Those companies cannot free up the resources to focus on improving their forecasting.” Jones agreed, and acknowledged, “Most companies are using scenario planning to some degree, but their limitation is resources — either in terms of people or software.”
As AI capabilities become more broadly available, more companies can apply their existing resources to achieve returns. Many companies are already exploring initial implementations of machine learning models, predictive analytics, AI-enhanced scenario planning and agentic AI.
Machine learning models
Machine learning models enable more dynamic, data-driven decision-making that is changing how energy companies forecast their cash flow. These models can analyze vast and varied datasets, from operational metrics and market trends to weather patterns and energy consumption, uncovering hidden correlations and likely fluctuations in revenue and expenses.
Unlike traditional forecasting methods that rely heavily on historical averages and manual inputs, machine learning models can adapt to real-time changes and continuously refine predictions to help firms better anticipate demand shifts, optimize pricing strategies and manage liquidity. Even limited solutions can help companies reduce forecasting errors, respond more effectively to market volatility and make more informed financial planning decisions.
Predictive analytics
Predictive analytics can help energy companies turn complex data analysis into actionable insight for smarter and faster decisions. Predictive models analyze historical patterns alongside real-time energy demand, commodity prices, weather conditions and other variables to anticipate future cash inflows and outflows with greater accuracy. This helps finance teams identify potential shortfalls or surpluses early, informing proactive adjustments to budgets, investments and operational strategies.
The ability to run continuous forecasts and update projections as new data becomes available also improves responsiveness to market volatility, ultimately supporting more stable financial planning and risk management.
AI-enhanced scenario planning
AI-enhanced scenario planning pairs machine learning with predictive analytics, using current and past data to forecast likely outcomes for a range of potential market conditions, operational disruptions, regulatory changes and other scenarios.
With this analysis, finance teams can assess and prioritize potential risks and opportunities, test contingency plans and ultimately make more informed decisions about capital allocation and liquidity management — both for today, and for immediate adaptation in future scenarios. The result is a more resilient forecasting process that builds adaptability for the volatile energy landscape.
Agentic AI
Agentic AI introduces small autonomous solutions that can analyze, decide and even act upon financial data with minimal human intervention.
These intelligent agents can build upon machine learning and predictive analytics to continuously ingest and interpret real-time inputs, generating adaptive forecasts that reflect current conditions. Agentic AI systems can also apply AI-driven scenario planning to simulate financial scenarios for emerging risks and optimize strategies on the fly. This autonomy accelerates forecasting cycles while also improving accuracy and resilience, so that energy companies can better manage liquidity and navigate market volatility with consistent confidence.
Emerging AI capabilities can work together to help small finance teams outperform their resource limitations. “It will be disruptive to the forecasting process in a positive way,” Benoit said. “It’s in line with getting the most out of the people that you have in the organization.”
Focused resources
“The idea of using machine learning and big data to make more informed forecasts has been around for a long time,” Benoit said. “The big change is that you’re going to see people freed up to bring all of that to bear, with tools that can make estimates more robust, data-driven and precise.”
So, how can an energy company focus its limited resources on the technology with the best returns? The answer could be in your data. “We have a tremendous amount of access to data,” Klemowits said. “Start thinking about what’s critical for data collection, so that you can look back and identify the trends that help you forecast going forward. You need proper collection and an understanding of what you’re collecting, or it could all become noise.”
As you consider the data you have available, and the ways that you could turn it into value, also consider how data could determine the resilience and value of your organization in the future.
The value of data
“One thing to consider is the value of data: what it has been historically and what it might be going forward,” Benoit said. “It’s happening now, and in the future you will see greater value in data — not just in the tech industry, but in all industries including energy. Your proprietary data and your knowledge from historical observations become part of the value proposition of the company. So, it follows that the people in the company who are creating that data, through their actions and processes every day, are creating value and propelling the value of the business.”
Benoit suggested learning from the initial adoption of enterprise computing, as we now enter the age of AI adoption. “Look at what’s happening now, with forecasting, modeling and the impact that advanced technology is having. Look back to what happened in the tech explosion between 1975 and 1990 and the radical impact it had on how businesses were run, how efficiencies were obtained, and the returns that were earned on investment in that time period. You can draw some parallels about what might be possible now, as we go into this next tech revolution.”
What used to be innovation is becoming equalization, as more companies enter the race to refine AI-driven forecasting and analysis. “It comes back to resilience,” Jones said. “If you don’t invest in this, it doesn’t mean that your business will fail — but it means that your business could be slower to react to changing dynamics. That could put you in a competitive disadvantage, which ultimately costs your business money. It might be hard to measure, but I think it’s simple economics.”
Contacts:
Head of Energy Industry
Grant Thornton Advisors LLC
Partner, Audit Services, Grant Thornton LLP
Tyler Jones serves as the Head of Energy Industry, and is an Audit Services partner in the Dallas office.
Dallas, Texas
Industries
- Energy
- Manufacturing, Transportation & Distribution
Service Experience
- Audit & Assurance Services
Co-Lead, Global Valuation Services, Grant Thornton International
Partner, CFO Advisory Services, Grant Thornton Advisors LLC
Bryan Benoit is a Partner in the CFO Advisory practice and is also the Global Valuation Co-Leader.
Houston, Texas
Industries
- Energy
- Banking
- Construction & Real Estate
- Healthcare
- Insurance
- Technology, Media & Telecommunications
Service Experience
- Strategic federal tax
- International Tax
- Advisory Services
- Valuation and Modeling
- Private Wealth Services
- Transaction Advisory
Managing Director, CFO Advisory Services
Grant Thornton Advisors LLC
Keith Klemowits, Managing Director in the Corporate Value Consulting practice of Grant Thornton LLP, is the leader of the Capital Asset Solutions group and has over eighteen years of experience advising domestic and international corporations on the valuation of tangible assets, business valuation, intangible assets, intellectual property, corporate securities, and partnership interests of privately-held, and publicly traded businesses.
Houston, Texas
Industries
- Construction & Real Estate
- Healthcare
- Manufacturing, Transportation & Distribution
- Technology, Media & Telecommunications
- Transportation & Distribution
- Energy
- Retail & Consumer Brands
Service Experience
- Advisory Services
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