Cost reduction and efficiency optimization are dominant economic principles – AI risk and the choice of the right AI model – Image: Xpert.Digital
Avoiding risks: How the right AI strategy secures a competitive advantage
The economic dimension of AI investments: Securing future viability through strategic model selection
In an era where cost reduction and efficiency optimization are dominant economic principles, investments in artificial intelligence (AI) are subject to the same economic laws. The decision for or against specific AI models and business models is far more than a technological question – it can determine a company's long-term success or failure. Misguided investments in this area are particularly serious, as they not only tie up financial resources but can also create strategic disadvantages in the competition. The rapid development of AI technology necessitates a careful cost-benefit analysis to make future-proof decisions and avoid economic disaster.
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AI as a crucial future factor for companies
The relevance of AI for the future of business can hardly be overstated. A survey shows that 72 percent of all respondents are convinced that a lack of investment in AI jeopardizes future viability. This is particularly evident in German industry, where 78 percent of companies are convinced that the use of AI will be crucial for future competitiveness. For 70 percent, AI is even the most important technology for the future viability of German industry.
These impressive figures illustrate that the decision for or against AI is no longer a matter of optional strategic choice, but is increasingly gaining existential importance. Experts from the Learning Systems platform, led by acatech, emphasize in this context the need for a clear AI vision and cross-industry collaborations to keep pace with international competition. The German economy is undergoing profound change: traditional product-oriented business models are being replaced in almost all sectors by data-driven products and services that are increasingly based on AI.
Particularly noteworthy is the fact that German companies possess an immense wealth of machine and operational data that could give them a potential competitive advantage – provided they leverage this data commercially using AI and develop innovative business models from it. Failing to recognize this potential or squandering it through poor investment decisions could have disastrous long-term consequences.
The speed of technological change as a risk factor
A crucial factor in AI investments is the relentless pace of technological progress. Sam Altman, CEO of OpenAI, recently warned in an interview: “If you, as a startup, think that progress will remain roughly the same, then we will definitely overtake you!” This stark statement underscores that business models based on the current generation of AI could be obsolete in the near future.
The dynamics of the AI market can be illustrated by the so-called “DeepSeek effect.” In January 2025, the Chinese startup DeepSeek caused significant stock market crashes among established tech companies by presenting a particularly cost-efficient AI model. The US chipmaker Nvidia, whose graphics processors had previously been considered indispensable for training AI models, lost almost 20 percent of its market capitalization in a single trading day—a loss of more than $500 billion. This example vividly demonstrates how quickly seemingly safe investments in AI technologies can be devalued by disruptive innovations.
The danger exists not only for technology providers, but also for companies that rely on specific AI solutions as users. Those who invest in expensive hardware and proprietary AI models today could find tomorrow that more cost-effective and powerful alternatives are available. Such misinvestments not only tie up financial resources, but can also limit a company's flexibility and adaptability.
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The need for a comprehensive cost-benefit analysis
Given these challenges, a thorough cost-benefit analysis is essential before implementing AI. Companies must consider both the upfront costs and the ongoing expenses associated with AI implementation. These include, but are not limited to, setting up the infrastructure, data collection, system integration, and maintenance.
At the same time, it is necessary to evaluate the added value that AI can create in business processes – be it through increased productivity, cost savings, or improved efficiency. Return on investment (ROI) plays a crucial role in this evaluation and helps in prioritizing AI initiatives.
The complexity of cost-benefit analysis is further increased by the diversity of AI methods, use cases, and application areas. A concrete cost-benefit analysis is particularly difficult in research projects, as often only assumptions about monetary costs and benefits can be made. Nevertheless, a positive cost-benefit balance is crucial for the acceptance of new technologies and thus for the overall speed of digital transformation.
Criteria for future-proof AI models and business models
To avoid backing a "dead horse," companies must consider several key factors when selecting AI models and business models. An AI business model consists of strategies and applications designed to make AI commercially viable and integrate it into the product portfolio. The future viability of such models depends on various factors.
Seamless integration into existing systems is of paramount importance. AI systems should integrate seamlessly into existing infrastructure and production systems. Even in the planning phase, it is essential to verify the compatibility of the desired system with current hardware, software, and existing databases. Factors such as data formats, communication protocols, and API compatibility play a crucial role in this process.
Another critical success factor is data quality and availability. The quality of the data ultimately determines the quality of the entire AI project – poor data inevitably leads to inadequate models and incorrect conclusions. This aspect is often underestimated, but it is crucial for the future viability of an AI solution.
The scalability of an AI solution must also be guaranteed. Many AI initiatives fail not due to initial implementation, but due to a lack of successful scaling beyond pilot projects. A survey shows that three out of four C-level decision-makers are convinced that the company's existence is at stake if they cannot successfully scale artificial intelligence within the next five years.
Last but not least, ethical and legal aspects must also be considered. The most advanced generative AI models currently originate from the USA and China and often fail to meet the ethical and legal requirements being discussed in Europe. This could lead to significant problems in the long term, particularly when questions of liability for AI decisions arise.
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Strategies for minimizing investment risks in AI projects
To minimize the risks of AI investments, experts recommend various strategies. One option is to avoid relying on a single AI product and instead engage in collaborations. “Rarely does a single company possess all the necessary expertise, infrastructure, technologies, and customer access for an AI-based solution. Often, technologically strong companies lack the knowledge in areas such as digital business model definition, software development, and, above all, marketing. Therefore, companies should forge suitable alliances within their digital ecosystem to, for example, acquire the necessary expertise and share data and infrastructure.”.
Another strategy is the use of “AI as a Service” providers, who sell AI-related services and can be used as partners. This allows companies to remain flexible and benefit from advances in AI without having to commit to a specific technology long-term.
Furthermore, a crucial element for a successful AI-based business model is its continuous maintenance and development. The quality of AI applications can decline over time, for example, due to changes in customer behavior. Companies often lack such maintenance strategies for their AI solutions, which can lead to problems in the long run.
The consequences of incorrect AI decisions
The consequences of poor decisions in the field of AI can be far-reaching and extend well beyond financial losses from misinvestments. A missed opportunity to leverage AI potential can lead to a significant competitive disadvantage. Companies that hesitate too long or invest in the wrong AI technology risk falling behind more innovative competitors.
The history of the technology industry is marked by companies that have missed the boat on technological advancements. A recent example is Intel, which has lost market share to competitors like AMD and NVIDIA in recent years, particularly in the AI and gaming segments. Although Intel was once a leader in the semiconductor industry, the company partially missed the AI boom and now faces significant challenges in catching up.
In addition to economic risks, there are also legal and ethical challenges. When AI decisions lead to harm, the question of liability arises. Because AI systems operate on the basis of large datasets and are trained through machine learning, it is often difficult to clearly assign responsibility for erroneous decisions. This can lead to legal uncertainties, which in turn can undermine trust in AI solutions.
AI as a strategic investment for the future
The decision for or against specific AI models and business models is a strategic investment in a company's future viability. Poor decisions in this area can not only lead to financial losses but also cause long-term competitive disadvantages. Therefore, the cost-benefit analysis of AI investments must extend far beyond short-term financial aspects and consider strategic dimensions.
The challenge lies in making the right decisions in a rapidly evolving technological landscape. Companies must distinguish between short-term trends and long-term developments to avoid backing a "dead horse." A clear AI vision, cross-industry collaborations, and the continuous evaluation and adaptation of chosen AI solutions are crucial for success in this dynamic environment.
Ultimately, the question isn't whether a company should invest in AI – given AI's overwhelming importance for future viability, that question has already been answered. The crucial question is how these investments should be structured to ensure long-term economic success and avoid failure on the path to a digital future. Careful cost-benefit analysis, consideration of future trends, and the flexibility to adapt to changing technological landscapes are the key success factors.
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