Brief

AI in Energy: From Pilots to Payoff

Most energy and natural resource companies remain stuck in AI pilot purgatory. Here’s what the companies making progress do differently.

By Andrea Beccia, Timothy Balthazar, Aadarsh Baijal, Peter Meijer, and Elizabeth Karl

Published on September 01, 2026
min read


At a Glance AI conviction is high, but fewer than 20% of energy and natural resource executives report scaled, measurable impact today. The most common bottleneck: Project outcomes are often unclear or untethered to business value. Companies are also struggling to overcome talent scarcity, immature data and governance, and operating models built for a pre-AI era. Those furthest ahead established a narrow focus, made the right structural changes, and created a repeatable playbook for scaling. Amid growing enthusiasm for AI’s potential in energy and natural resource industries, executives are struggling with a classic challenge in any technology shift: How can their companies graduate from pilots to production at scale? Like many industries, AI is now a strategic priority for energy and natural resource (ENR) companies, and most executives have high expectations. Few are seeing results. Bain’s ENR Executive Survey 2026—our annual pulse check of more than 800 executives worldwide across oil and gas, utilities, chemicals, mining, and agribusiness—found that 68% of executives expect AI to have a substantial or transformative effect on business performance over the next 5 to 10 years. Yet fewer than 20% of those surveyed reported that their companies are scaling up AI applications with measurable impact or executing companywide transformations today. Most are still experimenting with AI on a small scale or running coordinated pilots that haven’t met aspirations (see Figure 1). Figure 1 Most energy and natural resource executives have high expectations for AI, but few are seeing results Source: Bain ENR Executive Survey 2026 (n=859) Progress beyond small‑scale experimenting is broad but shallow. Executives in the Middle East and Asia-Pacific regions are slightly more optimistic about AI’s potential, with roughly 10 percentage points more expecting high impact compared to most other regions and reporting marginally greater progress. Oil and gas and chemicals executives report the most headway so far. Customer service, R&D, and operations and maintenance are the most mature applications so far, as we detailed in our article exploring the full set of insights from this year’s ENR survey. What’s holding organizations back? The survey surfaced organizational, data, and operating model challenges. Nearly half of executives say the biggest impediment to AI achieving the desired outcomes is that the outcomes are unclear or have no link to business value. Shortages of technical expertise and poor data availability and governance ranked second and third on the list of bottlenecks, respectively (see Figure 2). Figure 2 Organizational, data, and operating model challenges are keeping energy and natural resource companies’ AI efforts from scaling Source: Bain ENR Executive Survey 2026 (n=859) The playbook for scaling AI The survey highlights where companies are stuck. Bain’s experience working with global ENR clients illuminates a practical pathway from pilots to scaled impact, consistent with the survey findings. The companies that are making real progress share four common principles: 1. Focus on a few end‑to‑end domains where AI can move the needle. Rather than spreading effort across dozens of pilots, companies can start by selecting two or three end‑to‑end domains where AI can address material profit and loss or system challenges highlighted in the survey. For example, this could involve asset and process performance, customer and technical services enhancements, or supply chain optimization. Within each domain, the most successful AI organizations define their initial use cases with clear outcome metrics and ownership, directly tackling the gap between high expectations and unclear value. In upstream oil and gas, one global operator ran an AI‑enabled diagnostic to determine how a mature offshore asset could reach its full potential. It used data science tools and large language models to clean up and connect previously unlinked data sets, which helped uncover opportunities in asset care, logistics, and well performance economics. AI helped the company better forecast each well’s production, develop a marine logistics data model, and create an equipment maintenance database directly linked to associated work orders and deferments. The company made actionable plans for more than a dozen performance improvement initiatives valued at more than $15 million per year, with clear timelines, decision points, and initiative owners. 2. Redesign processes and roles, applying AI where it adds business value. Many AI initiatives try to bolt models onto existing workflows. In our work with ENR companies, we’ve seen that moving from pilots to production actually requires updating the function itself (such as maintenance planning or grid operations), not just the tools. The most effective companies start by mapping the current process from start to finish and identifying decision points, handoffs, and bottlenecks. That helps guide the redesign of roles, metrics, and ways of working, with AI embedded. This might look like planners, dispatchers, technicians, traders, or schedulers using AI‑enabled recommendations as part of standard routines. Early adopters are also beginning to integrate AI agents into certain processes while keeping humans in the loop. The last step (a crucial one): retiring legacy reports, tools, and approvals that would otherwise drag AI‑enabled processes back to the old mode. 3. Build “good‑enough” foundations in data, technology architecture, and talent for key domains. ENR companies don’t actually have to solve each of these problems across every application before value can be created. We’ve seen companies achieve success by focusing first on the data sets, governance rules, and platform capabilities needed to industrialize AI in priority domains. What might this look like on the ground? For a utility, it’s asset and outage data for grid optimization. For an oil and gas major, it’s work management and sensor data for operations and maintenance. In agribusiness, it’s integrated spending and contract data for procurement. The most effective organizations stand up small, cross‑functional teams that combine domain experts, data engineers, and AI specialists. They use early wins to build capabilities and confidence and to guide subsequent investments in common platforms and data products. That methodical approach will be much more effective than launching broad, abstract data lake or GenAI platform efforts. A global agribusiness focused its first wave of AI investments on non‑commodity procurement. It prioritized seven AI tools. To build a solid initial foundation, the company cleaned and restructured its spending data, and it embedded generative AI‑enabled classifiers, contract optimizers, and negotiation assistants directly into the company’s category management workflows. The result was higher savings targets, faster strategy development, materially better compliance and reporting, and a foundation for later AI pushes in manufacturing and logistics. 4. Institutionalize a repeatable engine for scaling AI. Companies that move beyond pilot mode tend to align their “scale engine” with their internal culture and regulatory context. In practice, this often includes: An AI operating model that clarifies who owns value, who builds and runs solutions, and how funding works. This could be a small, central AI team plus empowered business‑unit squads in priority domains. Lightweight but effective governance covering prioritization, risk, and responsible AI—critical in regulated ENR sectors. A playbook for going from idea to scaled solution, with common patterns for discovery, design, pilot, industrialization, and change management. A European utility company found that its AI experiments were scattered across business units with little central visibility. It designed a new AI operating model informed by a deep dive into its processes, governance, skills, and technology, as well as a scan of external benchmarks. The new approach included creating an AI center of excellence and responsible AI committee. The company also developed clearer protocols for managing AI costs, a technology architecture blueprint that generative AI tools could easily work with, and a detailed rollout plan to achieve AI at scale within six to nine months. The decisions ahead The distance between AI's promise in energy and natural resources and its impact today is real, but it’s not a technology gap. As this year's executive survey makes clear, the obstacles that matter most are organizational and structural: project outcomes that are unclear or untethered to business value, scarce talent, immature data and governance, and operating models built for a pre-AI era. That is, in a way, encouraging news. These are problems executives can act on directly, and the companies establishing an early lead in AI are doing so with sharper focus, processes redesigned around AI, sufficient (not perfect) initial foundations, and a repeatable playbook for scaling. What remains is a set of consequential choices. Leaders must decide how much responsibility to concentrate in a central AI group vs. empower business-unit squads; how to fund, govern, and sequence investments so early wins compound rather than fragment; and where to build proprietary intelligence rather than buy or partner. While rolling out a new customer relationship management system, one utility decided to develop its own AI-orchestrated next-best action engine instead of buying one from a vendor. Why? To keep such a core part of the business closer to the chest and limit vendor lock-in. The bottlenecks holding ENR companies back are difficult, but none are intractable. The executives who confront them now, with clarity on where AI creates value and the discipline to scale it, have a chance to do more than close the gap between expectation and impact. They can set the pace that the rest of the industry has to follow.

At a Glance

AI conviction is high, but fewer than 20% of energy and natural resource executives report scaled, measurable impact today.
The most common bottleneck: Project outcomes are often unclear or untethered to business value.
Companies are also struggling to overcome talent scarcity, immature data and governance, and operating models built for a pre-AI era.
Those furthest ahead established a narrow focus, made the right structural changes, and created a repeatable playbook for scaling.

Amid growing enthusiasm for AI’s potential in energy and natural resource industries, executives are struggling with a classic challenge in any technology shift: How can their companies graduate from pilots to production at scale?

Like many industries, AI is now a strategic priority for energy and natural resource (ENR) companies, and most executives have high expectations. Few are seeing results.

Bain’s ENR Executive Survey 2026—our annual pulse check of more than 800 executives worldwide across oil and gas, utilities, chemicals, mining, and agribusiness—found that 68% of executives expect AI to have a substantial or transformative effect on business performance over the next 5 to 10 years. Yet fewer than 20% of those surveyed reported that their companies are scaling up AI applications with measurable impact or executing companywide transformations today. Most are still experimenting with AI on a small scale or running coordinated pilots that haven’t met aspirations (see Figure 1).

visualization — Most energy and natural resource executives have high expectations for AI, but few are seeing results

Progress beyond small‑scale experimenting is broad but shallow. Executives in the Middle East and Asia-Pacific regions are slightly more optimistic about AI’s potential, with roughly 10 percentage points more expecting high impact compared to most other regions and reporting marginally greater progress. Oil and gas and chemicals executives report the most headway so far. Customer service, R&D, and operations and maintenance are the most mature applications so far, as we detailed in our article exploring the full set of insights from this year’s ENR survey.

What’s holding organizations back? The survey surfaced organizational, data, and operating model challenges. Nearly half of executives say the biggest impediment to AI achieving the desired outcomes is that the outcomes are unclear or have no link to business value. Shortages of technical expertise and poor data availability and governance ranked second and third on the list of bottlenecks, respectively (see Figure 2).

The playbook for scaling AI

The survey highlights where companies are stuck. Bain’s experience working with global ENR clients illuminates a practical pathway from pilots to scaled impact, consistent with the survey findings. The companies that are making real progress share four common principles:

1. Focus on a few end‑to‑end domains where AI can move the needle. Rather than spreading effort across dozens of pilots, companies can start by selecting two or three end‑to‑end domains where AI can address material profit and loss or system challenges highlighted in the survey. For example, this could involve asset and process performance, customer and technical services enhancements, or supply chain optimization. Within each domain, the most successful AI organizations define their initial use cases with clear outcome metrics and ownership, directly tackling the gap between high expectations and unclear value.

In upstream oil and gas, one global operator ran an AI‑enabled diagnostic to determine how a mature offshore asset could reach its full potential. It used data science tools and large language models to clean up and connect previously unlinked data sets, which helped uncover opportunities in asset care, logistics, and well performance economics. AI helped the company better forecast each well’s production, develop a marine logistics data model, and create an equipment maintenance database directly linked to associated work orders and deferments. The company made actionable plans for more than a dozen performance improvement initiatives valued at more than $15 million per year, with clear timelines, decision points, and initiative owners.

2. Redesign processes and roles, applying AI where it adds business value. Many AI initiatives try to bolt models onto existing workflows. In our work with ENR companies, we’ve seen that moving from pilots to production actually requires updating the function itself (such as maintenance planning or grid operations), not just the tools.

The most effective companies start by mapping the current process from start to finish and identifying decision points, handoffs, and bottlenecks. That helps guide the redesign of roles, metrics, and ways of working, with AI embedded. This might look like planners, dispatchers, technicians, traders, or schedulers using AI‑enabled recommendations as part of standard routines. Early adopters are also beginning to integrate AI agents into certain processes while keeping humans in the loop. The last step (a crucial one): retiring legacy reports, tools, and approvals that would otherwise drag AI‑enabled processes back to the old mode.

3. Build “good‑enough” foundations in data, technology architecture, and talent for key domains. ENR companies don’t actually have to solve each of these problems across every application before value can be created. We’ve seen companies achieve success by focusing first on the data sets, governance rules, and platform capabilities needed to industrialize AI in priority domains.

What might this look like on the ground? For a utility, it’s asset and outage data for grid optimization. For an oil and gas major, it’s work management and sensor data for operations and maintenance. In agribusiness, it’s integrated spending and contract data for procurement.

The most effective organizations stand up small, cross‑functional teams that combine domain experts, data engineers, and AI specialists. They use early wins to build capabilities and confidence and to guide subsequent investments in common platforms and data products. That methodical approach will be much more effective than launching broad, abstract data lake or GenAI platform efforts.

A global agribusiness focused its first wave of AI investments on non‑commodity procurement. It prioritized seven AI tools. To build a solid initial foundation, the company cleaned and restructured its spending data, and it embedded generative AI‑enabled classifiers, contract optimizers, and negotiation assistants directly into the company’s category management workflows. The result was higher savings targets, faster strategy development, materially better compliance and reporting, and a foundation for later AI pushes in manufacturing and logistics.

4. Institutionalize a repeatable engine for scaling AI. Companies that move beyond pilot mode tend to align their “scale engine” with their internal culture and regulatory context. In practice, this often includes:

An AI operating model that clarifies who owns value, who builds and runs solutions, and how funding works. This could be a small, central AI team plus empowered business‑unit squads in priority domains.
Lightweight but effective governance covering prioritization, risk, and responsible AI—critical in regulated ENR sectors.
A playbook for going from idea to scaled solution, with common patterns for discovery, design, pilot, industrialization, and change management.

A European utility company found that its AI experiments were scattered across business units with little central visibility. It designed a new AI operating model informed by a deep dive into its processes, governance, skills, and technology, as well as a scan of external benchmarks. The new approach included creating an AI center of excellence and responsible AI committee. The company also developed clearer protocols for managing AI costs, a technology architecture blueprint that generative AI tools could easily work with, and a detailed rollout plan to achieve AI at scale within six to nine months.

The decisions ahead

The distance between AI's promise in energy and natural resources and its impact today is real, but it’s not a technology gap. As this year's executive survey makes clear, the obstacles that matter most are organizational and structural: project outcomes that are unclear or untethered to business value, scarce talent, immature data and governance, and operating models built for a pre-AI era. That is, in a way, encouraging news. These are problems executives can act on directly, and the companies establishing an early lead in AI are doing so with sharper focus, processes redesigned around AI, sufficient (not perfect) initial foundations, and a repeatable playbook for scaling.

What remains is a set of consequential choices. Leaders must decide how much responsibility to concentrate in a central AI group vs. empower business-unit squads; how to fund, govern, and sequence investments so early wins compound rather than fragment; and where to build proprietary intelligence rather than buy or partner. While rolling out a new customer relationship management system, one utility decided to develop its own AI-orchestrated next-best action engine instead of buying one from a vendor. Why? To keep such a core part of the business closer to the chest and limit vendor lock-in.

The bottlenecks holding ENR companies back are difficult, but none are intractable. The executives who confront them now, with clarity on where AI creates value and the discipline to scale it, have a chance to do more than close the gap between expectation and impact. They can set the pace that the rest of the industry has to follow.