A Dual Strategic Challenge in Energy

An AI-based assistant gives energy company geoscientists the ability to quickly visualize and analyze hundreds of CT scan images. Visualization, image analysis, and AI/machine learning techniques are increasingly areas of innovation and value for science-driven businesses. Shown here, a thin section classification tool with analogues in multiple other science-driven industries. 

A Dual Strategic Challenge in Energy

Authors: Mason Dykstra, Ph.D., VP Energy Solutions and Charlie Cosad, M.Sc.

The Challenges

Energy companies, those primarily working in oil and gas, face a dual strategic challenge as they work to become ‘digital energy companies’. The first challenge is if – and how – to move beyond oil and gas, to other sources of energy. The second is how to exploit the significant potential of digital technologies, once thought of as enablers of incremental efficiency, but today foundational to innovation, often disrupting entire industries. 

In pursuing (or not…) other sources of energy to become part of their core business, the strategies of these companies vary widely, as well as if and how to pursue and fund being ‘carbon neutral’. Shareholder and NGO activism motivated by climate change are rapidly becoming driving factors.

For exploiting digital technologies, a number of these companies have aggressive strategies to transform their businesses, now tempered by the oil market. Part of that transformation includes accessing a much larger and different ‘digital service sector’; the behemoths that are Amazon, Microsoft, IBM, Dell Technologies, as well as a multitude of smaller companies working in scientific software, data analytics, and AI/machine learning. 

The ‘Energy Company’ Energy Strategy Spectrum

European companies are the leaders in becoming true multi-energy companies; among them Equinor, Eni, Shell, Repsol and Total stand out. Consider the recent Eni organizational announcement which included the following organizational structure and quote by Chief Executive Officer Claudio Descalzi: 

The Company is creating two new business groups:

  • Natural Resources, to develop the upstream oil & gas portfolio sustainably, promoting energy efficiency and carbon capture.
  • Energy Evolution, dedicated to supporting the evolution of the company’s power generation, product transformation and marketing from fossil to bio, blue and green. 

“This new structure reflects Eni’s pivot to the energy transition. An irreversible path that will make us leaders in decarbonized energy products.” 

For international operators, it has become a strategic choice, driven both by politics and economics, with all engaging to varying degrees. There is a parallel to strategies in the auto industry, where Toyota chose to pursue hybrid vehicles (the Prius introduced in 1997), and Volkswagen Group chose to exclusively pursue the most efficient combustion engines.  

At the other end of the spectrum are US onshore unconventional operators. With a bit of innovative thinking and positive economics, they could have an option to expand on their energy mix. Oil & gas upstream infrastructure, for example, includes components which could be used as a springboard for wind or solar projects in those same geographic areas. 

Economics aside, the main problem faced by onshore unconventionals companies is that the alternative energy market has taken off, and the business maxim of first mover advantage is a tall hurdle to overcome. 

The recent, extreme swings in oil industry economics, the political lightning rod that is climate change, and the ascending importance of social responsibility have added urgency to oil companies’ agendas in evaluating long term energy strategies. Overlaying any strategic approach is the historical power and politics of oil in a given country. 

The Energy Industry Supply Chain for ‘Digital’ 

Many of the oil industry’s major service providers are strategically advancing digital skills and offerings, and a number of niche ‘digital only’ oil & gas technology providers have been formed. While these industry-centered companies are helping to advance their clients digitally, the strategic challenge for operators is discovering the many more companies out there which offer significant opportunities for collaborative innovation and value. 

Many of these companies have limited, if any, history in the energy industry. At the large company end of the spectrum, Amazon and Microsoft are readily accessed for their cloud services, and the energy clients would likely not make their top 10 client list, if not top 20. The technological advances by these large companies will be primarily driven by other industries, to the benefit of energy companies. 

At the small company end of the spectrum are those providing scientific software, data & data flow, and compute enabling infrastructure technology. Some have energy as a small part of their business, whereas the majority have no footprint in energy. However, the challenges they face with energy companies are similar to those in other industries they service. 

Consider visualization and image analysis, with application of AI/machine learning, a key to removing drudgery from the work of scientists and engineers, significantly increasing understanding, efficiency, productivity, and improving decision making. The underlying principles and technologies are the same, whether it is organoids, semiconductor wafers or reservoir cores. Many other industries are more advanced than energy in these techniques, as well as in the enabling infrastructure. The challenge in oil & gas therefore is how to access and exploit this expertise and the associated technologies.  

There will likely be a number of companies with digital technologies and skills not available to oil & gas dominated energy companies, driven by shareholders and environmentally driven NGOs. The most visible recent example is Google. Their principled stance to no longer support the oil industry in its advanced scientific computing initiatives was made easier by a lagging position in that market segment, as well as the anticipation of positive optics related to the announcement. 

A Future of Strategic Challenges  

For those with an interest in corporate strategy and industry evolution, this dual challenge of  energy mix and digital transformation will prove fascinating to follow with pivots that will no doubt be a source of business school case studies for years to come.

About the Authors

Mason Dykstra, Ph.D., VP Energy Solutions at Enthought, holds a PhD from the University of California Santa Barbara, a MS from the University of Colorado Boulder, and a BS from Northern Arizona University, all in geosciences. 

Charlie Cosad, M.Sc., holds a B.Sc. in Mechanical Engineering from Syracuse University and a M.Sc. in Aerospace & Mechanical Sciences from Princeton University.

Share this article:

Related Content

R&D イノベーションサミット2024「研究開発におけるAIの大規模活用に向けて – デジタル環境で勝ち残る研究開発組織への変革」開催レポート

去る2024年5月30日に、近年注目のAIの大規模活用をテーマに、エンソート主催のプライベートイベントがミッドタウン日比谷6FのBASE Qで開催されました。

Read More



Read More

科学研究開発リーダーが知っておくべき AI 概念トップ 10


Read More


OpenAIのChatGPTやGoogleのBardなど、大規模言語モデル(LLM)は自然言語で人と対話する能力において著しい進歩を遂げました。 ユーザーが言葉で要望を入力すれば、LLMは「理解」し、適切な回答を返してくれます。

Read More



Read More



Read More

Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More

By Mike Heiber, Ph.D., Di…

Read More

Life Sciences Labs Optimize with New Digital Technologies and Upskilling

Labs are resetting the tr…

Read More

From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions

Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.

Read More

The Importance of Large Language Models in Science Even If You Don’t Work With Language

OpenAI's ChatGPT, Google's Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language....

Read More