AI Meets Organizations: From Pilot Projects to Scaled Deployment 

In brief  

The Application of AI: AI offers organizations vast potential for automating tasks, improving efficiency, and enhancing customer experiences. However, challenges such as outdated infrastructure, lack of expertise, and data governance issues often hinder the scaling of AI beyond pilot projects. 

Learning from the Past: Appointing a Chief AI Officer (CAIO) alone is insufficient to drive transformation; AI needs to be integrated into core business processes. A structured and integrative approach, including AI strategy development and employee involvement, is essential to avoid the mistakes of past digitalization efforts. 

Case Study – Deutsche Bahn Long-Distance: DB Long-Distance successfully utilized AI to improve customer service, notably through the Railmate platform, and established an AI Competence Center to centralize AI strategy and governance. This center has evaluated over 200 AI projects and plays a crucial role in driving AI innovation across the organization. 

AI Transformation: Starting with a central AI nucleus with share responsibility in AI development is recommended, especially in organizations with limited AI expertise, to pilot initial projects and build knowledge. As AI maturity grows, scaling efforts can be decentralized, supported by shared platforms and standards to ensure consistency across the organization. 

The Application of AI: Enormous Potential and Major Challenges 

AI presents vast opportunities for organizations, particularly in automation and enhancing operational efficiency. AI has the potential to significantly reduce labor hours, with estimates suggesting that automation could affect 7% to 15% of working hours across European professions by 2028, with the potential to rise to 30% by 2030 in some sectors. Public sector organizations, in particular, are seen to benefit the most, especially given the chronic shortage of skilled workers. 

AI can streamline routine tasks, provide insights through advanced data analysis, improve product and service quality, and offer personalized customer services. For example, AI-powered chatbots can deliver 24/7 customer support, increasing service accessibility. However, implementing AI at scale is not without difficulties. Organizations often lack the necessary expertise and struggle to integrate AI models into their existing systems. Common obstacles include outdated IT infrastructure, insufficient cloud solutions, and challenges related to data security, compliance, and data governance. Moreover, organizational culture and resistance to change can impede progress. 

No organization that wants to remain relevant in the future can escape the transformation brought about by artificial intelligence (AI).

Learning from the Past: Paths for a Regulated AI Transformation 

Past experiences with digitalization have shown that simply appointing a Chief AI Officer (CAIO) or another C-level executive and a dedicated organizational unit to oversee and drive AI initiatives is insufficient to reach meaningful transformation. As with the Chief Digital Officer (CDO) role, CAIO positions often fail to integrate AI into the organization’s core processes, leading to isolated pilot projects that never reach full deployment. Many such initiatives falter because they are not fully aligned with the business's broader goals or integrated into its everyday operations. 

The article suggests that organizations need to take a more structured approach to AI implementation. This begins with developing a clear AI strategy that aligns with organizational goals and involves all stakeholders, especially employees, who need to understand and support the vision. A well-balanced portfolio of AI projects should be established, blending short-term "quick wins" with long-term, visionary projects. Ethical considerations, compliance with regulations (such as the EU AI Act), and the use of appropriate technological platforms are also vital components of a successful AI strategy. Moreover, AI solutions must be developed and operated by cross-functional teams to ensure that they are effectively integrated across the organization. Finally, organizations should build partnerships with external data suppliers, universities, and think tanks to augment their AI capabilities. 

Concrete applications of AI systems for large-scale organization: the Deutsche Bahn’s Long-distance division

Deutsche Bahn’s (DB) Long-Distance division offers a concrete example of how an organization can implement AI successfully. DB Long-Distance has been investing in AI to enhance operational efficiency and improve the customer experience for years. One of the earliest AI systems deployed was the Railmate feedback platform, introduced in 2015. This system processes over 3.2 million pieces of customer feedback annually, automatically categorizing and responding to them. 

Despite its early success with AI, DB recognized the need for a more coherent, organization-wide AI strategy. In 2022, it established a central AI Competence Center to define the division’s AI focus areas and anchor AI initiatives at the management level. The center was tasked with overseeing governance, speeding up implementation through standardization, and identifying the most relevant AI use cases to focus on limited resources. Since its inception, the AI Competence Center has evaluated and prioritized over 200 AI projects, with several key initiatives directly improving the travel experience for customers. Additionally, more than 2,500 employees have been trained in AI-related topics, demonstrating the importance of workforce development in driving AI transformation.

 

  • 7% to 15%

    of European working hours could be automated by 2028 

  • Over 200

    AI projects have been evaluated and prioritized since the AI Competence Center was founded in 2022

  • 3.2 Million

    pieces of customer feedback are processed annually by DB Long-Distance using AI.

AI Transformation: The Nucleus as a Starting Point 

To succeed in AI transformation, the article emphasizes the importance of a centralized AI unit, or "nucleus," particularly during the initial phases when AI expertise within the organization is limited. The nucleus acts as the focal point for AI-related activities, concentrating the organization’s limited AI specialists to lead initial projects. The knowledge gained from these projects is then disseminated throughout the organization, gradually raising the organization’s overall AI maturity. 

The nucleus is responsible for critical management tasks, such as portfolio management, ensuring that AI initiatives are aligned with broader organizational goals. In this phase business departments already need to be in charge for the concrete AI product development.  

Over time, as the organization’s AI maturity increases, AI implementation can scale across decentralized business units. This scaling process relies on the creation of shared platforms, standards, and regulations to ensure consistency and efficiency across the organization. 

The article outlines a phased approach to AI transformation. In the first phase, the nucleus leads the initial AI projects and facilitates the transfer of AI knowledge to other parts of the organization. In the second phase, as the organization’s AI maturity grows, business units take over more responsibility for AI implementation, with the nucleus continuing to provide support and governance. This phased approach helps organizations avoid the pitfalls of isolated pilot projects and ensures that AI is fully integrated into core business processes. 

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