Unlocking AI potential in the energy industry - the importance of data

The latest advancements in artificial intelligence (AI) are changing our society in ways we could not imagine. Generative AI solutions are changing how we interact with computers and solve problems. What will the impact on enterprises be? 

Processes will be optimized or disrupted, leading to the creation of new roles, the phasing out of certain positions, and a notable boost in workplace efficiency in various scenarios. There is a potential in the energy industry to leverage AI to solve challenges such as digital skills shortage, for instance, developers, where the demand is extremely high.  AI will also be used to improve safety and reduce environmental impact. 

As an illustration, AI is already used to monitor and predict equipment failures, helping prevent accidents and reduce downtime. Energy companies also use AI to optimize energy consumption and decrease waste production, reducing the environmental footprint linked to their operations. 

AI can further help companies in the energy industry to understand their customers and their needs better. By analyzing customer data, AI can help companies to personalize their offerings and improve customer satisfaction. This can lead to increased customer loyalty and revenue growth. 

AI can also help companies in the energy industry to stay ahead of the competition. For instance, by analyzing market trends and customer behavior, companies can identify new opportunities, develop innovative products and services - and gain a competitive advantage by differentiating themselves from competitors. However, for the energy industry to make optimal use of the latest available AI technology, companies must invest in data availability, accessibility, and, perhaps now more than ever, quality. 

Data challenges in the energy sector 

The energy industry faces growing challenges due to an exponentially increasing amount of data, tightening constraints around cybersecurity, supply security, and the push for diversifying toward renewable energy sources. What is essential for the energy industry when preparing for AI on corporate data? 

Of utmost importance is data quality. Poor-quality input data will result in poor-quality output from AI systems. If companies should rely purely on AI without ensuring the highest quality of the data used, that could lead to poor decision-making, loss of revenue, and reputational damage. In addition, poor data quality might lead to biased or inaccurate results, for instance in AI-optimized power grid management, which could have severe consequences on the energy supply chain.

Companies in the energy industry need to invest in data quality and availability to ensure that their AI systems produce accurate and reliable results.

It will require significant investments in data infrastructure, data management, and data governance, but the potential benefits of AI in the energy industry make this investment worthwhile.  

Data quality is not the only aspect of using AI on corporate data. Data discovery, data flow, and data cost need to be addressed. Data discovery helps users find and access the right data for their AI goals. Data flow enables the movement and transformation of data between different apps and services. Data cost involves the expenses of storing, processing, and transferring data in the cloud. These aspects are crucial for companies in the energy industry when using AI on corporate data. By addressing these, companies can ensure that their data is ready and available for AI and that their AI systems will deliver the best results and outcomes. 

Enabling for AI-driven data analytics – getting to know your data estate 

Companies are using data in ever more sophisticated ways. Analytics platforms are taking over from standard Excel, where anyone can make a dashboard. Data science practice is becoming mainstream, and notebook-type data analysis and low-code/no-code platforms are changing how companies create apps. The latest advances in AI make it possible to rethink how we analyze and interact with data and how we make business apps - new user experiences are emerging where conversational interactions take center stage.  

Businesses are ready to change, but many struggle to prepare data for AI. So how can we prepare the data to be AI-ready for the user experience (UX) we aim to create? 

To harness the power of generative AI in your company you need access to data in your internal systems. The only challenge is you do not know what data you have available.

In Cegal we believe that getting to know what is in your data estate is a good start on your AI data journey. In the realm of oil and gas subsurface data, Cegal offers a relevant service product offering a valuable starting point on this journey. The Cegal Data Program identifies the organizations' data and can reveal data quality and duplication challenges. Although not originally intended for AI – a well-understood data foundation is key when using modern AI. We see in trials with custom LLM-powered chatbots that data and metadata need to be of high quality if you are to get good responses for your AI investments. This kind of methodology can be interesting to pursue for other data domains.  

Once you've uncovered what's in your data estate, you're ready to move on. The next step to consider is to enable the data to be consumed programmatically – meaning that you need to access the data from a database or through an API. Correctly building this is a difficult and time-consuming exercise, but the reward is high if you do it right. We see that clients who have invested in data platforms with modern architecture have a head start in the AI race. They store the data and its metadata in a specific location and format, which can enhance the performance of your AI system when used properly.  

Preparing for AI – making data available 

At Cegal, we have a leading integrations group that deploys and operates integration platforms for clients – this is one of the building blocks we could use to prepare data for AI. By helping our clients with a modern cloud-native integration platform, we are helping to pave the way for the AI-driven workflows of the future. For example, Microsoft invests heavily in creating copilots for all their products and services. For it to work well on domain data, Microsoft Graph connectors to the integration platform can be developed – with data curated in a structured way, you can get more out of your existing Microsoft product investments.   

Another example is Cegal Prizm made by the Cegal geoscience product department. This product can connect to a closed license-controlled and highly specialized domain data source on demand. This opens potential AI workflows and automation via AI workflow systems like LangChain and Microsoft Semantic Kernel.  

Making data in your organization available also has external value – we see that the large AI players will be forced to pay for access to news data and platform data to avoid legal challenges. By leveraging an API-based approach to data access, you can open connections to this data to external parties and sell data as a product useful in model training scenarios. Reddit is a recent example where they sold access to all their user-generated data for 10s of millions of dollars. For a company navigating challenges, this opens up a significant opportunity for a potential shift towards achieving success. However, it is important to consider the ethical implications of training models on user-generated data, as this practice may raise concerns regarding GDPR compliance and other data regulations. 

Future of AI in the workplace – it’s all about the data

In Cegal we witness the swift integration of AI as a vital component in modern enterprise operations. Harnessing vast amounts of data and leveraging AI for predictive analytics, automation, and improved decision-making processes plays an essential role in providing businesses with a competitive edge.

To unlock the potential of AI in the energy industry, it is crucial to invest in data quality and availability, get to know your data estate, and enable data to be consumed programmatically.  

Moving forward, companies should continue investing in AI technologies, ensuring that data privacy and ethical considerations are at the forefront of such advancements. The potential of AI is boundless, and with the right strategies in place (and access to great data), it can lead to unprecedented growth and innovation.