The Data Advantage in Energy and Materials

One of the most striking aspects of the energy and materials sectors is the sheer volume and variety of data they generate. From real-time sensor data and smart meter readings to energy market trends and maintenance records, these industries are awash in information. 

But on top of that, it’s estimated that up to 90% of this data is unstructured, stored across disparate systems and in various formats. And for many, this presents both a challenge and an opportunity.

The challenge lies in making sense of this vast sea of information available. Traditional analytics methods often fall short when dealing with semi-structured or unstructured data. However, this is where AI, particularly GAI, comes into play. 

Certain GAI capabilities excel at processing and deriving insights from diverse data types, including seasonal, historical, and real-time data. Particularly large language models (LLMs) and foundation models (FMs), which have shown remarkable proficiency in handling various forms of data. So, the versatility of GAI in managing diverse data types also extends to its use into other business intelligence domains as well as forecasting.

Strategic AI Adoption: A Nordic Perspective

Nordic companies in the energy and manufacturing sectors are well positioned to take advantage of AI/GAI’s capabilities. The region’s strong telecom, energy, and utilities manufacturing sectors provide a solid foundation for AI/GAI integration. 

Yet, although this may be true, the successful adoption requires more than just technological competence. It demands a clearly defined AI governance strategy and a focus on identifying valuable use-cases to see how best GAI can perform.

“We’re seeing encouraging signs of this strategic approach in the Nordic region. According to recent reports, 92% of participants in the energy and utilities sectors state that GAI is already a topic for leadership and boardroom discussions. This proactive stance puts Nordic companies at the forefront of the AI revolution in these industries”, says Shahin Atai, Head of AI at HiQ.

Transformative Use Cases

The potential applications of AI in energy and materials are vast and varied. Based on findings from the report, here are some key areas where we anticipate significant impact:

1. Predictive Maintenance: AI is taking predictive maintenance to new heights. By analyzing vast amounts of structured vs unstructured data, AI/GAI systems can predict equipment failures with unprecedented accuracy, allowing for proactive maintenance strategies that minimize downtime and reduce costs
2. Grid Operational Efficiency: As power grids become increasingly complex with the integration of renewable energy sources, AI/GAI can potentially play a crucial role in optimizing operations. For example, AI/GAI systems can balance supply and demand in real-time, manage energy storage, and improve overall grid resilience
3. Exploration and Resource Management: In industries like oil and gas, specialized models can process seismic data to identify key attributes such as horizon tracing or fault location, aiding in resource exploration and extraction
4. Process Optimization: AI/GAI can analyze production data to identify inefficiencies and suggest improvements, leading to increased productivity and reduced waste
5. Safety and Risk Management: By processing vast amounts of data from various sources, AI/GAI can help identify potential safety hazards and mitigate risks across operations

Challenges and Considerations

While the potential of these new and emerging GAI capabilities in energy and materials is wide and varied, its adoption is not without challenges. Companies will still need to consider:

1. Data Quality and Management: Effective AI implementation requires high-quality, well-managed data. Companies need to invest in robust data management practices to ensure their AI systems have reliable information to work with.
2. Integration with Legacy Systems: Many energy and manufacturing companies operate with legacy systems. Integrating AI solutions with these existing systems requires careful planning and execution.
3. Skill Development: As AI becomes more prevalent, there’s a growing need for employees with AI literacy and specialized skills. Companies need to invest in training and development to build these capabilities in-house
4. Ethical Considerations: Given the potential for future AI systems to become more powerful in application and memory, companies need to ensure they’re used ethically and responsibly, particularly when it comes to decision-making that affects safety and environmental impact

The HiQ perspective

At HiQ, we see the adoption of AI/GAI in Nordic energy and manufacturing sectors as a an very important indicator that the region’s proactive approach, coupled with its strong industrial base, is well positioned to be leaders in this technological revolution.

We believe that success in this new era will hinge on several key factors:

1. Agility and Adaptability: Companies must be ready to swiftly adopt and integrate emerging AI/GAI technologies as they evolve
2. Strategic Data Management: The focus should be not just on collecting data, but on ensuring its quality and relevance for AI/GAI applications and value-add use-cases
3. Talent Development: Investing in AI literacy across the organization and building teams with specialized AI skills will soon be essential
4. Ethical Considerations: As the architectural evolution of AI/GAI systems becomes more powerful and in return, make such systems more pervasive, companies must prioritize ethical AI considerations
5. Collaboration and Knowledge Sharing: Given the complexity of integrating  as well as implementing AI / GAI-based systems or applications, the need for collaboration between industry players, technology providers, and business decision makers  will be crucial towards navigating such complexity efficiently

“The AI revolution in energy and materials is not just on the horizon—it’s already here. Nordic companies have a unique opportunity to lead this transformation, leveraging their data resources and technological capabilities to drive innovation, efficiency, and sustainability. By embracing AI and focusing on strategic implementation, these companies can not only enhance their operations but also contribute to solving some of the most pressing challenges in energy and resource management”, says Shahin Atai, at HiQ.

Are you ready to explore how AI can transform your business? Let’s start the conversation and unlock the potential of AI for your organization.