The role of artificial intelligence in the energy sector

As part of a master's thesis, I was interviewed on this topic. Based on our video interview, I wrote this blog post.

Over the last few years, artificial intelligence (AI) has had a major impact on various industries. One particularly exciting development is taking place in the energy sector. In this blog article, I, Mike Schwede, would like to share my experience and insights on how AI is changing the energy sector and what opportunities and challenges the use of AI in this area brings.

The current impact of AI on marketing in the energy sector

I have worked in digital marketing for 25 years and have been working intensively with AI tools, especially over the last three years. AI's impact on marketing processes is groundbreaking, especially in the analysis, planning, creation and production of campaigns. AI can support the entire marketing process from planning and production through to reporting, and deliver efficiency gains.

In the planning phase, AI plays a decisive role, helping to analyse data and gain insights for campaign strategy. Today, we can analyse hundreds of reels or posts within minutes to see what works well and which content achieves the highest engagement. That saves a great deal of time and provides detailed insight into how campaigns should be set up for best results. AI also helps create detailed personas by analysing and segmenting existing customer data. This helps us understand the needs of our target groups better and create content tailored to them. Analysing product and purchase data also helps us identify patterns and develop personalised offers that can significantly increase conversion rates.

AI can also support the concept and creative phase by generating ideas and providing inspiration. However, it remains true that creative direction from a Creative Director is irreplaceable. AI is a valuable tool for providing first approaches or supporting brainstorming, but the truly creative and original ideas come from experienced minds. 'What is really poor, at least with generative AI, is idea development – you still need a good Creative Director who cannot be replaced,' I stressed in the interview.

In the production phase, however, AI shows its full strength. Here, it can take over repetitive tasks and produce creative assets at scale. At one agency I work with, we no longer use traditional image libraries. Instead, we generate images directly with AI and then edit them to ensure the brand identity remains consistent. This not only saves time, but also increases flexibility, because we can tailor content precisely to each campaign. Another example is the creation of multilingual content, which AI can produce in different languages with ease. That is a major advantage in Switzerland, with its language regions. Video formats can also be adapted almost automatically for different platforms, formats and content snippets, which speeds up the production process significantly.

AI offers enormous potential in the analysis and optimisation of marketing campaigns. This is especially clear when it comes to processing large volumes of data and recognising patterns. For example, we can analyse campaign and purchase data to find out which content performs best and how we can improve our measures further. However, there are also limits. Many of the common analysis tools are still too superficial, and specific, in-depth insights are often missing. The generic recommendations provided by ChatGPT or similar models are not always enough for truly well-founded decisions. That is why, based on our analyses, we trained a MikeGPT, or rather PowdienceGPT, to generate better evaluations and sound conclusions. Today, these automated campaign analyses from www.powdience.com are very good. I now rarely have anything to add to the AI-generated recommendations. Human expertise is still needed, especially when evaluating and interpreting the results, so that generic findings are placed in the specific context of the business in a meaningful way.

The advantages of AI in the marketing process

The possibilities AI offers are enormous. From image generation to campaign optimisation and search engine optimisation, AI can be used in many areas. By automating many processes, we can focus more strongly on creative concepts and strategic planning. AI shows its full potential especially in content production and the subsequent evaluation of campaigns. AI can generate images, create video sequences and even produce multilingual content with very little effort.

However, it is important that the quality of generated content is not guaranteed automatically. 'Someone with strong visual training, for example in photography, can use AI in a way that ensures the generated results are visually excellent.' It is therefore clear that the best results come from combining technology and human expertise.

Challenges in integrating AI

Despite the many opportunities, there are also some challenges when using AI in the energy sector. One of the biggest difficulties is data quality and availability: 'Many companies, especially the large ones, have an enormous amount of data, but it is often not available in a usable form. The systems are fragmented, Excel spreadsheets and legacy systems such as SAP dominate. That makes it difficult to use AI.'

Smaller companies, especially SMEs, often have an advantage here because they use more modern tools and less outdated system landscapes. They can implement AI faster and at lower cost, and achieve results within weeks, whereas large companies often need years to clean up their data structures.

Another important topic is the so-called skill shift. Employees need to develop new skills to keep up with the technology. The best results come from a team that understands technology but also has creative skills. While simple tasks can be automated, specialist talent is needed to develop creative campaigns and to use the data strategically. A data scientist now belongs in every good marketing team.

The energy sector compared with other sectors

If all companies use the same AI tools, marketing becomes more interchangeable too. That is why it is important to train your own models in the medium term, to retain uniqueness and appear more specific to the brand. If this is done successfully, the company will be among the winners and stand out, rather than sinking into the average.

The energy sector differs in some respects from other sectors such as fashion. Energy as a product is less emotional than a handbag or an item of clothing, but the importance of the brand should not be underestimated. A good example is Octopus Energy in the UK, which has built a strong brand through excellent communication and a compelling product.

'In Switzerland, there is no electricity company that I would really call a brand. Most have only a logo, but no brand,' I explained in the interview, underlining the relevance of a clear brand strategy in the energy sector too. AI can help here by making it possible to generate personalised and relevant content based on customer data, offering customers real added value.

AI also plays a major role in customer service. It helps answer customer enquiries efficiently and to a high standard. At the same time, customer communication must be tailored to the target audience and should not be written in bureaucratic officialese, to avoid hard-to-understand communication. This shows that the human factor remains crucial in getting the language and tone right.

The future of the energy sector with AI

I see the future of marketing in the energy sector very positively. In the coming years, AI will increasingly become part of the process and simplify and automate many tasks: 'It will probably automate a huge amount, from customer advice to ad delivery.' I find the move towards real-time personalisation particularly interesting. This has been discussed for two decades, but only now, with Generative AI, is it possible. For example, serving advertising at exactly the right moment and in the right place, based on customer behaviour. The difference between a human interaction and an AI-generated response will become almost impossible to tell.

Another major area of potential is price optimisation for energy service providers. AI can help develop dynamic pricing models that benefit both the company and customers.

Conclusion: High standards for data and creativity

The use of AI in the energy sector offers enormous opportunities, but also brings some challenges. What matters is that companies do not rely on the technology alone, but also create the necessary foundations. That means collecting and maintaining data in a usable quality, and training employees accordingly.

AI can make work more efficient and more creative. Ultimately, success will depend on how well companies manage to use the technology strategically and combine creative and data-driven talent. 'It takes the really good people to achieve really good results with AI,' I stressed.

Overall, the energy sector is still at the beginning of an exciting development that offers both customers and companies new opportunities – from personalised offers to more efficient internal processes. AI will play a central role here and reshape marketing in a lasting way.