EnBW: On the way to enterprise-wide scaling of AI

"appliedAI helps us develop a structured approach to scaling our AI activities and exchange ideas and challenges with a professional community." Rainer Hoffmann, Lead AI at EnBW

EnBW Energie Baden-Württemberg AG is one of the largest energy companies in Germany and Europe with over 23,000 employees. It supplies around 5.5 million customers with electricity, gas and water, and also offers energy solutions and related services.

Since 2012, EnBW has been responding to the fundamental changes brought about by the energy transition with a broad transformation of the company. It aims to play a leading role in reshaping the energy sector. EnBW's strategy focuses on massive expansion of renewable energies and networks.

Additionally, the company aims to develop sustainable mobility and intelligent infrastructure solutions. To leverage the value in their data, EnBW decided to use AI. EnBW actively works to implement AI in their product portfolio and processes. The company's efforts are already bearing fruit: In August 2020, EnBW was named AI Champion in Baden-Württemberg. It won the state-wide competition held by the Baden-Württemberg Ministry of Economics, Labor and Housing with its forecasting service for virtual power plants and the scaling of this technology.

But how did EnBW manage to implement AI in the organization? What hurdles had to be overcome? And what role has appliedAI played within the transformation since EnBW joined our partner network in early 2019?

Starting Situation

EnBW started its first AI initiatives and experiments many years ago. The first projects predicted load, wind, and photovoltaic input. In 2018, the company established a central AI team. As a result, many decentralized teams developed AI capabilities. Currently, EnBW operates more than 20 AI solutions in all business areas and most supporting units. More than 100 employees work in various roles on AI-related projects.

The goal of EnBW is to achieve a higher level of AI maturity to use AI as much as possible. This aims to enrich the work of its employees and improve decision-making. According to the appliedAI framework for AI maturity, EnBW is a good example of a company at the "Practitioner" level.

There are already some implemented use cases, and more are being developed. The challenge is to scale the identification of use cases even further. Thus, the entire organization can be focused on driving the internal AI transformation and ensuring that these cases are actually implemented. This is the only way EnBW can realize the value of AI.

For this reason, EnBW is pushing the development of communities and forming internal developer communities for AI and machine learning. It also supports AI development with a holistic data strategy and centrally promotes infrastructure development.

What it means to be a practitioner

Practitioners take on the challenge of operationalizing, developing, and implementing their AI strategy. In doing so, they focus on optimizing processes or reinventing products. This includes a principled approach to identify opportunities, build core AI teams and initial collaborations, implement the right training programs for the organization, and prepare the AI data strategy.

Change management is primarily responsible for breaking down barriers. Technical prototypes move from a proof of concept to an active service, and processes are adapted to align development with AI systems.

Problem Statement

After several successful AI solutions were developed and implemented, EnBW faced two main questions in 2019: How can AI be scaled across the entire company? And how can as many EnBW employees as possible be enabled to recognize potential AI use cases?

In particular, EnBW faced the following challenges:

• Firstly, several internal teams developed AI-based products. However, the initiatives were never really coordinated. This meant that EnBW lacked an overarching perspective to align, scale, and standardize its activities and solutions.
• Furthermore, some EnBW business areas were not yet involved in AI because they did not have an expert to show them possible use cases and value pools within their respective business areas.
• Finally, potential use cases were not visible enough. As a result, the AI teams could not effectively support and drive projects forward.

With all these measures implemented, EnBW conducted appliedAI's maturity assessment to gain a deeper understanding of how its measures were already impacting the organization and the extent to which they were bearing fruit.

The results indicated good progress in adopting AI on a large scale. But they also helped identify areas of focus where there is still room for improvement.

To achieve their goals and overcome challenges, appliedAI and EnBW developed a targeted approach to implementing AI in the organization.

First, EnBW conducted multiple AI workshops across various areas of the organization. These were based on the methodology of appliedAI to create a cohesive AI vision that applies to the entire organization and provides a comprehensive perspective. During the workshops, EnBW also assessed the status of their AI-enabling factors.

At the same time, appliedAI offered an engineering training with EnBW to train their software engineers in dealing with artificial intelligence. The training was aimed at 20 software engineers and included a week of on-site workshops. These workshops introduced the latest tools, frameworks, and approaches to use machine learning for real business problems. During these workshops, participants gained practical experience in Colab and practiced with real datasets. In in-depth discussions between EnBW's software engineers and appliedAI's machine learning engineers, questions were answered. The EnBW team was thus equipped with the right tools to apply AI independently and to help internal teams to define their AI use cases.

AI multipliers from various business units were then nominated and trained specifically on AI and how to identify use cases.

The designated multipliers participated in a two-day intensive training at appliedAI in Munich. This included a general introduction to machine learning and its requirements. In addition, train-the-trainer formats were used to teach methods for identifying AI use cases and conducting corresponding workshops.

Subsequently, the AI-multipliers were given access to an internal community in order to continuously build and expand it. In this way, the multipliers help to pass on AI knowledge to the teams and support as well as to develop a central view of possible use cases.

As a result, EnBW can now provide all relevant business units with appointed AI experts who bring their expertise to the teams and implement AI.

The AI Maturity Assessment is an interactive tool developed by appliedAI. It allows companies to assess their level of AI adoption and determine where they are on their AI journey.

The goal is to support companies in their transformation from initial touchpoints with AI to widespread adoption of the technology throughout the organization.

The application of AI can only be achieved in a long-term, transformative process. appliedAI has mapped out the various phases that companies go through - from initial experiments with artificial intelligence to its enterprise-wide use on a large scale.

With the AI Maturity Assessment, companies can determine their level of AI maturity across eight strategic dimensions.

The tool covers various aspects, such as the visibility of an overall strategy, the availability of skills, IT infrastructure, data management, and execution.
 

Depending on how companies perform in all maturity dimensions, the tool helps identify areas for improvement. It also provides concrete recommendations for action to develop a roadmap towards AI maturity.

Tool for the maturity assessment by appliedAI

Thanks to the analysis of the maturity assessment at EnBW, appliedAI was able to identify clear action needs and measures. A large number of EnBW employees participated in the assessment and many of them already have a good understanding of how AI will affect their company and industry.

In addition to the challenges described, the assessment showed some AI-related strengths, such as EnBW's ability to use a structured approach in deciding which use cases to implement. Furthermore, most employees feel encouraged to come up with AI use cases. They see AI as a way to solve their business problems. In addition, EnBW is already exchanging ideas about AI in internal and external communities.

The top management is actively driving the topic forward and providing the necessary resources to improve EnBW's AI maturity. To accelerate the use of AI, EnBW appointed responsible persons for established AI systems that the company has developed. This means that each established AI system at EnBW has an owner who is responsible for this topic in the long term. He also ensures that the system is regularly improved.
 

In addition to these outstanding achievements, EnBW has already identified other areas where action is needed. For example, it wants to reach the next level of AI maturity. In order to further increase the AI maturity level, an even more precisely formulated AI ambition is required in coordination with the overall corporate strategy. In addition, EnBW needs to further standardize the development processes used across different teams.

In this way, it wants to ensure that AI is applied and implemented in a coordinated manner. The same applies to the need to further develop a centralized and scalable infrastructure. On the other hand, EnBW needs to increase the visibility of its data assets to leverage the potential of AI on a large scale.

Based on these results of the assessment, EnBW now has a clear understanding of what measures it needs to take to further improve its maturity level.

As a result of the measures implemented, the EnBW is starting to see the first results aimed at achieving the 3rd level of AI maturity. The work is bearing fruit.

Through workshops, the EnBW established a clear and central AI vision across all business areas and aligned it with the overarching corporate vision. The EnBW is now using the results of the AI maturity assessment as a basis to define its next strategic steps to introduce AI on a large scale. With the developed vision and AI strategy, the EnBW now ensures a comprehensive perspective and orientation, according to which its activities should be scaled and aligned.

In addition, the training of the 30 multipliers from various business areas and affiliated companies has already enabled the central AI team to get closer to the business areas. It knows which AI use cases are currently being discussed and what AI-related needs exist in the organization. This helps the central team to expand and improve its service offerings.

In this way, it supports the multipliers in bringing AI into their field of business. The multipliers help drive change and create a mindset that is open to AI and advocates for AI to be an integral part of EnBW's strategy rather than seeing it as a threat. Multipliers also share ideas in communities to facilitate cross-functional collaboration and learning. EnBW employees are generally positive about AI and have already implemented AI use cases themselves with ideas.

As a result of the initial technical training described above, EnBW's IT team can now support departments with AI use cases. Thus, it helps to initially evaluate use cases as well as to assess and select the right implementation partners.

Thanks to the background knowledge it has acquired about tools and approaches, it even implements simple use cases itself. In addition, the IT team can now communicate more easily with the internal AI teams. Together, they drive the development of infrastructure and tools. This makes it easier to develop and deploy AI solutions.

Outlook

In the near future, EnBW will face many exciting challenges and continue to increase its level of AI maturity. For example, it will be crucial to accelerate AI development and implementation (together with IT).

Another task will be to expand the ecosystem by finding the right partners. Even though AI is already present in EnBW's corporate culture, data competency must be further advanced. This is the only way to involve as many employees as possible in the community that drives AI implementation.

In addition, EnBW wants to support the development of AI by implementing a data governance framework and a data platform.

These are just a few examples of tasks for the near future. They illustrate that while EnBW has already made great progress, there is still a long way to go to reach the highest level of maturity for the company.

In partnership with: Energie Baden-Württemberg (EnBW)