Revolutionise your municipal utilities: The comprehensive KIROI master plan for the successful implementation of artificial intelligence

The municipal utility and energy supplier sector is facing major challenges and opportunities in the digital age. Artificial intelligence (AI) offers the potential to increase efficiency, improve customer service and optimise operational processes. However, the implementation of AI is complex and must be carefully planned in order to maximise the benefits.

Five key challenges when implementing AI at energy suppliers:

• Data management: Municipal utilities and energy suppliers collect large amounts of data. This data must be managed and analysed sensibly in order to gain valuable insights.
• Regulatory requirements: Compliance with legal requirements and data protection regulations is essential and can be a challenge.
• Technological integration: Existing systems and infrastructures often have to be adapted or replaced in order to successfully integrate AI.
• Cultural acceptance: The acceptance of AI among employees and the adaptation of the corporate culture are crucial for success.
• Competence development: Employees need new skills and knowledge to be able to work effectively with AI.

Why a standardised AI strategy is important for energy suppliers in all departments:

A uniform AI strategy ensures that all departments work towards common goals and synergies are utilised. This avoids duplication of effort and maximises the overall benefit of AI initiatives. A centralised strategy also helps to consistently comply with regulatory and ethical standards.

Why the KIROI strategy is so highly valued by over 400 companies

The KIROI masterplan offers a structured and practice-orientated approach to successfully implementing AI throughout the company. It takes into account both technological and human aspects and promotes the sustainable integration of AI in all areas of the company.

KIROI masterplan for municipal utilities and energy suppliers:

Step 1: Share your knowledge:

• Identify internal and external stakeholders with whom you should talk about the opportunities and benefits of AI.
• Create awareness of the transformative power of AI within management and the workforce.
• Organise workshops and information events to communicate the basics and potential of AI.
• Encourage dialogue between different departments to develop a common understanding.
• Use internal communication channels such as newsletters and the intranet to provide regular updates.
• Encourage employees to ask questions and actively participate in knowledge sharing.
• Share best practices and success stories from other companies or industries.
• Emphasise the role of AI in future competitiveness and increased efficiency.
• Develop an internal training programme for different levels of knowledge.
• Promote a culture of continuous training and adaptability.

Step 2: Explore tools:

• Analyse existing tools and technologies that are already in use in your company.
• Create an overview of potential AI tools that are suitable for different use cases.
• Carry out pilot projects to test the suitability and benefits of new tools.
• Evaluate the scalability and integration of new tools into existing systems.
• Consider user-friendliness and acceptance by employees.
• Collaborate with technology providers and start-ups to discover innovative solutions.
• Consider costs and ROI when selecting AI tools.
• Ensure that all tools comply with regulatory requirements.
• Promote an open culture of innovation in which new tools are continuously evaluated.
• Document the experiences and results of the tool tests for future reference.

Step 3: Big data and smart data:

• Identify the most important data sources within your organisation.
• Develop strategies for data cleansing and integration.
• Use AI tools to analyse large amounts of data and identify patterns.
• Implement data management systems that enable efficient storage and retrieval.
• Develop smart data approaches to gain actionable insights from large amounts of data.
• Promote the exchange of data between different departments.
• Implement security measures to protect sensitive data.
• Use predictive analytics to forecast future trends and demand.
• Develop dashboards and visualisation tools to make data understandable and accessible.
• Train employees in the use of data analysis tools and techniques.

Step 4: Cultural aspects:

• Promote an open and innovative corporate culture that welcomes change.
• Develop communication strategies to reduce fears and reservations about AI.
• Integrate AI as an integral part of the corporate strategy and vision.
• Reward innovation and the use of new technologies.
• Create platforms for internal dialogue and collaboration.
• Promote diversity and inclusion to integrate different perspectives and ideas.
• Develop programmes to promote the digital skills of all employees.
• Implement change management processes to facilitate the transition to new ways of working.
• Support managers in acting as role models when dealing with AI.
• Emphasise the ethical and social benefits of AI in corporate communications.

Step 5: Ethics and compliance:

• Develop an ethical framework for the use of AI in your company.
• Ensure that all AI applications comply with the applicable data protection regulations.
• Implement processes to monitor and evaluate the ethical impact of AI.
• Create transparency in the use and decision-making of AI systems.
• Develop guidelines for the fair and responsible use of AI.
• Train employees in ethical issues and compliance requirements.
• Work with external experts to ensure compliance with standards.
• Promote a culture of responsibility and accountability in dealing with AI.
• Implement mechanisms for reporting and investigating ethical concerns.
• Continuously monitor and evaluate the ethical implications of new AI technologies.

Step 6: Own department:

• Analyse the specific challenges and needs of your department.
• Develop customised AI solutions to optimise your work processes.
• Use predictive analytics to increase operational efficiency.
• Implement automation tools to reduce repetitive tasks.
• Encourage the exchange of best practices within your department.
• Create training programmes to enable employees to use AI tools.
• Develop KPIs to measure the success of your AI initiatives.
• Take feedback from employees into account when developing your AI strategy.
• Promote continuous improvement and customisation of your AI applications.
• Ensure that all AI applications are ethical and compliant.

Step 7: Ideas for other departments:

• Identify potential areas of application for AI in other departments.
• Develop pilot projects to demonstrate the benefits of AI.
• Promote interdisciplinary exchange and cooperation.
• Support other departments in the implementation of AI solutions.
• Create platforms for knowledge transfer and collaboration.
• Develop common goals and KPIs for cross-departmental AI projects.
• Use synergies between different departments to maximise the overall benefit.
• Create incentives for collaboration and the exchange of best practices.
• Support training programmes for all departments to promote AI skills.
• Promote a standardised and integrated AI strategy throughout the entire company.

Step 8: Skills development for employees:

• Analyse the current level of competence of your employees.
• Develop customised training programmes to promote digital and AI skills.
• Use e-learning platforms and interactive training materials.
• Promote continuous training and lifelong learning.
• Create mentoring programmes to support and guide skills development.
• Implement certification programmes to confirm the qualifications of your employees.
• Incentivise the acquisition of new skills and knowledge.
• Encourage dialogue and cooperation within and between departments.
• Develop career paths that reward the use of AI skills.
• Support employees in actively shaping the digital transformation.

Step 9: Skills development for managers:

• Train managers in the use of AI and its strategic applications.
• Promote a management culture that supports innovation and technological change.
• Develop specific training programmes for managers.
• Encourage dialogue and cooperation between managers from different departments.
• Support managers in acting as role models in the use of AI.
• Develop mentoring programmes to support new managers.
• Promote a culture of transparency and openness in dealing with new technologies.
• Support managers in taking ethical and compliance-relevant aspects into account.
• Promote continuous training and adaptation of management strategies.
• Develop KPIs to measure the success of the skills development measures.

The view from scientific research

Scientists see AI as having the potential to overcome the increasing complexity of the energy system and drive forward the energy transition[1][3][5].

A central field of application is the optimisation of energy generation and distribution. By analysing large amounts of data, AI can identify potential savings, integrate renewable energies more efficiently and better balance out fluctuations[5][12]. AI algorithms can also significantly improve the forecasting of energy demand, as researchers have shown using consumption data and weather forecasts[12].

However, the use of AI also harbours risks. Experts warn of cyberattacks, software errors and unforeseeable scenarios that need to be taken into account during the design phase[12]. Data protection and IT security also play an important role[8]. The integration of AI into existing systems also presents many companies with technical challenges[11].

Energy suppliers must therefore not only invest in the technology itself, but also in robust infrastructures, data security and skilled labour[2][10]. According to a study by PwC, companies will develop into holistic ecosystems in the future that digitally connect different areas of customers' lives[7]. This will require a consistent focus on customer needs and collaboration with partners from outside the industry.

Despite the hurdles, scientists agree that there is no way around AI. Machine learning and increasing computing power are making the systems ever more powerful[12]. It is crucial that the technology is used responsibly, with people and the environment at the centre[8]. AI can then decisively accelerate the energy transition and contribute to a sustainable future.

This KIROI masterplan provides a comprehensive approach to implementing AI in the financial sector. By applying the KIROI steps in a structured way, companies can ensure that all levels of the organisation are prepared for the use of AI and can use these technologies effectively.

Find out more on KIROI.ORG

Sources and further reading:

[1] https://www.bet-energie.de/webmagazin/artikel/zeitvariable-und-dynamische-tarife-eine-neue-aera-fuer-energieversorger-ab-2025
[2] https://www.digitale-technologien.de/DT/Redaktion/DE/Downloads/Publikation/052019_ssw_policy_paper_ki_energie.pdf%3F__blob=publicationFile&v=10.
[3] https://www.dena.de/kuenstliche-intelligenz/
[4] https://de.wikipedia.org/wiki/Energieversorgungsunternehmen
[5] https://eleks.com/de/blog/erneuerbare-energien-wie-ki-den-energiesektor-revolutioniert/
[6] https://www.dena.de/kuenstliche-intelligenz/?cHash=54f5acb7aab34f7a57e6d655ead3d3d1&tx_rsmpilotprojects_map%5Baction%5D=entries
[7] https://www.pwc.de/de/energiewirtschaft/digitalisierung-in-der-energiewirtschaft/studie-die-zukunft-der-energieversorger-sind-business-oekosysteme.pdf
[8] https://www.germanwatch.org/sites/default/files/K%C3%BCnstliche%20Intelligenz%20f%C3%BCr%20die%20Energiewende%20-%20Chancen%20und%20Risiken.pdf
[9] https://www.haw-hamburg.de/detail/news/news/show/interdisziplinaerer-blick-auf-die-ki/
[10] https://www2.deloitte.com/content/dam/Deloitte/de/Documents/energy-resources/Deloitte-Controlling-bei-Energieversorgern.pdf
[11] https://www.de.digital/DIGITAL/Redaktion/DE/Digitalisierungsindex/Publikationen/publikation-download-ki-herausforderungen.pdf?__blob=publicationFile&v=3
[12] https://eit.h-da.de/fileadmin/daFNE/SmartGridLABHessen/WhitePaper/Smart_Grid_LAB_Hessen_White_Paper-Machine-Learning-D_Pizzimbone_220420.pdf
[13] https://www.eswe-versorgung.de/fileadmin/user_upload/dateien/downloads/WdR-ESWE-Versorgung.pdf
[14] https://www.next-kraftwerke.de/wissen/kuenstliche-intelligenz-energiewirtschaft
[15] https://www.alexandria.unisg.ch/215241
[16] https://www.fieldfisher.com/de-de/insights/die-herausforderungen-bei-der-implementierung-von-kuenstlicher-intelligenz-im-oeffentlichen-sektor-meistern
[17] https://www.bet-energie.de/webmagazin/artikel/energieversorger-im-wandel-von-der-neuausrichtung-der-organisationsstrukturen-bis-zur-gestaltung-dynamischer-tarife-fuer-eine-kundenorientierte-zukunft
[18] https://epilot.cloud/blog/epilot/kuenstliche-intelligenz-in-der-energiebranche/
[19] https://www.energieforen.de/veranstaltungen/chatgpt-fuer-energieversorger-einsteiger
[20] https://www.mind-verse.de/news/energiehunger-der-kunstlichen-intelligenz-stellt-stromnetze-vor-herausforderungen