Key opportunities for AI in the Energy supply chain: Insights and trends

However, this surge in interest has yet to translate into widespread implementation. Only 2% of energy sector job openings seek AI expertise, with just 313 AI-focused positions across European energy companies in 2024. If companies were already making widescale investment into AI, they would be hiring more AI experts.

The same narrative can be seen when we analyse the products & services energy companies have publicly released. We used AI to scrape 10s of thousands of webpages related to the offerings of 5 of Europe’s big energy companies, and we could only find evidence of 3 production-grade applications that have a significant use for generative AI.

We know from our own work that the opportunity with generative AI is significant. And this shift from ‘walk to talk’ is already starting. But the data shows that it’s still early days.

1. Enhanced Investment Decision Support

Energy companies face unprecedented challenges in making long-term investment decisions. Traditional forecasting approaches struggle to cope with market volatility, supply chain disruptions, and the accelerating energy transition. Infrastructure investments often involve hundreds of millions in projects with time horizons stretching into decad

We demonstrated how alternative data analysis can provide early market signals that complement existing forecasting methods. For example, our analysis of hydrogen-related business events showed a significant spike in 2020 followed by a decline, potentially indicating a shift from hype to more measured, infrastructure-focused development.

Similar analysis of EV charging infrastructure events revealed early warning signs of a market slowdown that preceded traditional indicators.

2. Intelligent Documentation Management

Energy infrastructure projects generate thousands of pages of critical documentation, from tender specifications to regulatory compliance documents. The administrative complexity has become a significant bottleneck in project delivery.

Read about the Kone case here

We demonstrated an AI-powered solution that can:

• Automatically extract key information from complex technical documents
• Match tender requirements against product specifications
• Reduce review cycles from weeks to days
• Enable more strategic customer interactions

One of our client implementations is on a path to reduce the tender review cycle from three weeks to one day, fundamentally changing how teams interact with customers and allowing for more strategic discussions about different scenarios and options.

3. Enhanced Project Management

The coordination of large-scale energy infrastructure projects remains a significant challenge, with projects often facing years of delays. Solar and wind farm projects require many years of planning and administration, while nuclear projects can face decades of delays.

In this demo, we showed a prototype of our ‘AI Consultant’ Connie, who lives in our Slack and helps to execute tasks on our behalf.

We showcased how emerging AI technologies, particularly AI agents, could transform project management. The demo illustrated how multiple AI agents can work together to handle complex, multi-step workflows, freeing up human experts to focus on higher-value activities. While this technology is still emerging, it represents the next frontier in AI implementation.

As Jarkko Kuismanen from UPM Energy emphasised, successful AI implementation isn't about replacing existing processes but augmenting them. The energy sector is already resource-constrained, making it crucial to focus on getting more value from existing assets and expertise.

Key recommendations include:

1. Start by using AI as a "second opinion" alongside existing processes
2. Focus on building bridges between AI capabilities and business requirements
3. Combine alternative data sources with traditional analytics and domain expertise
4. Prioritize force multiplication over cost reduction
5. Invest in people who understand both the technology and business context

The energy sector faces unique challenges in AI adoption, but the potential benefits are substantial. Success will require a balanced approach: embracing new technologies while maintaining the industry's necessarily conservative approach to risk and reliability.

Companies should start by identifying areas where AI can augment existing expertise rather than trying to replace established processes. The focus should be on empowering existing experts with better tools and insights, enabling them to make better decisions and work more efficiently.

As Jarkko noted, "It's ultimately about money - doing more with the same resources rather than just cutting costs." Companies that can strike this balance between innovation and reliability will be best positioned to thrive in an increasingly complex and volatile energy landscape.

This blog post covered some of the topics we discussed in our webinar "Rethinking the energy supply chain with data & AI". Click here to see the full recording of the webinar.