Beyond the Hype: Why "Wait and See" is a Dangerous Strategy for GenAI
GenAI has arrived at the "Peak of Inflated Expectations" on the Gartner Hype Cycle (or has already passed it), and particularly in Germany, one often encounters a certain sense of vindication at the board level: "Good thing we didn't jump on this trend more aggressively. There are hardly any applications that genuinely improve our business today. We will continue to wait until truly value-creating opportunities emerge."
Undoubtedly, the majority of AI investments are currently not profitable. In his widely noted article "AI’s $600B Question" in June, Sequoia’s David Cahn did a rough calculation highlighting the gap between investments in AI infrastructure (primarily for Nvidia GPUs and building the data centers to run them) and the revenue ultimately generated in the AI ecosystem. If one excludes the resale of computing power by hyperscalers (Azure, Google Cloud, AWS, CoreWeave) and focuses on AI services like OpenAI’s ChatGPT or the Copilots from GitHub and Microsoft, global revenue in 2024 will barely exceed $20B. Goldman Sachs discusses similar arguments in the report "GenAI: Too much spend, too little benefit?".
However, waiting is a dangerous strategy, especially in the German economy. This is evident in the transformations within the automotive, chemical, or steel industries. Many business models and industrial logics that have been proven for decades, and are now changing due to decarbonization, cannot be future-proofed by simply swapping individual components one-for-one (e.g., electric instead of combustion drive, hydrogen instead of natural gas/coal). Similarly, no switch can be flipped to make companies "AI-ready" overnight. A programmatic approach should—naturally weighted and tailored to the specific company—take the following elements into account:
1) Performance Improvement // Targeted transformation of "mature" functions In selected areas such as customer service or IT, significant productivity gains can already be achieved today through the use of AI. An example of this is Klarna, which introduced an OpenAI-based AI assistant in the first quarter; in a first step, it took over about one-third of the workload, thereby replacing around 700 FTEs. Another example is Vodafone, with an announced investment of €140m this year alone to revamp its customer service chatbot, Tobi, also using OpenAI’s LLM. Another "mature" function is IT, where we see an average productivity increase of up to 30%, with new software development, in particular, being considerably simplified. Even rapidly growing companies like Meta and Microsoft have released over 15,000 employees each in multiple waves of layoffs over the past three years. In total, technology companies cut over 550,000 jobs globally between 2022 and 2024. While this development is driven by several factors, the significantly increased productivity of senior developers—for whom tools like GitHub Copilot act almost like an 'Iron Man suit'—plays a decisive role.
2) Organizational Capabilities // Broad development of AI skills Even in areas where no value-adding AI solutions are established yet, competence in handling AI should be specifically fostered. The focus here is on experimentation and exploring potential. Only in this way can employees quickly seize opportunities as soon as transformative AI solutions become available. Key success factors include, on the one hand, the establishment of a disciplined, iterative development process that includes reliable (financial) success measurement. This ensures that resources are used efficiently and are aligned with actual needs. On the other hand, building a network of experts and development partners is crucial. This can be supported by replicable methods for partner identification, such as hackathons, as well as a playbook that enables successful collaboration with external developers and solution providers. This creates a solid foundation to quickly identify necessary competencies outside one's own organization and to successfully implement and operate AI use cases. An example of this is S&P Global, which recently commissioned Accenture to conduct systematic GenAI training for all 35,000 employees. In our experience, such programs are most effective when learning and practical application are combined so that employees can develop and implement concrete solutions for the actual challenges in their work environment.
3) Portfolio Strategy // Active risk management and inorganic options for action Analogous to the practices of leading private equity funds and active fund managers, companies should specifically review their portfolios for the opportunities and risks of developments in the field of Artificial Intelligence. In the technology sector, market valuations of AI winners and losers diverged early on. An example of this is Chegg, the US market leader for online tutoring, whose value has dropped by more than 90% since November 2022 because generative AI cannibalized its existing offering. In contrast, Duolingo, the world's leading language learning app, is considered a winner of AI development: Thanks to significant productivity gains in the creation of lessons and software, as well as the introduction of the premium offering Duolingo Max, supported by generative AI, the company was able to significantly increase its earnings potential. In many other areas, the effects of AI are less obvious or will only become visible in the future. Nevertheless, these examples show how crucial it is to engage intensively with the topic in order to manage the portfolio early and actively.
4) FinOps // Basis for economic viability and scalability of AI projects FinOps is far more than just a framework and toolkit for monitoring, measuring, and managing cloud resource consumption. It embodies a cultural shift that focuses on optimizing IT costs across functions and divisions. A robust FinOps practice forms the foundation for developing precise cost attribution, making the specific costs for AI services transparent per team and project. This enables targeted optimizations: For example, for a chatbot that frequently receives similar questions, it could be checked whether results can be cached to provide answers more cost-effectively instead of repeatedly sending API calls to an LLM. Likewise, prompts could be reviewed to determine which Foundation Model can answer the respective request most economically. In this and similar ways, FinOps contributes significantly to the realization of economically viable AI use cases. However, FinOps itself also benefits from advances in AI: Natural Language Processing makes it possible to directly answer questions like "How high will the costs for this workload be?" or "How can the costs of the workload be reduced without compromising performance?". FinOps is thus not only the basis for the economic development of AI applications but also a beneficiary of technological advances that further increase its effectiveness.
5) Strategic Workforce Management // Development paths and involvement of employee representatives With the introduction of artificial intelligence, few roles will disappear completely, but almost all will change. In addition to these changing requirements, the weightings will also shift significantly. This requires more than just a simple "hire and fire." Rather, it requires comprehensive transparency regarding impending changes as well as clearly defined development paths to successfully guide the majority of the workforce through this transition. At the center of the process is the analysis of roles whose importance will decrease in the future, and the comparison of their competency profiles with those of growing roles. For example, "Accountants & Auditors" possess strong mathematical skills and work in a structured and conscientious manner. These characteristics make them excellent candidates for positions such as "Data Scientists" or "Cyber Security Analysts." Based on this, development paths are established that enable the systematic further development of the workforce. Employee representatives and unions should also be involved in this process at an early stage. In our experience, it is crucial to speak openly about a healthy balance between protecting the interests of employees and securing competitiveness through the use of AI. Together, viable models can be defined that do justice to both sides.
