Published on: March 9, 2025 / update from: March 9, 2025 - Author: Konrad Wolfenstein
Cost reduction and optimization of efficiency dominant business principles-AI risk and the choice of the right AI model-Image: Xpert.digital
Avoid risks: How the right AI strategy ensures the competitive advantage
The economic dimension of AI investments: secure future viability through strategic model selection
At a time when cost reduction and optimization of efficiency are dominating business principles, investments in artificial intelligence (AI) are also subject to the same economic laws. The decision for or against certain AI models and business models is much more than a technological question -it can decide on the long -term success or failure of a company. Misors in this area weigh particularly heavily because they not only bind financial resources, but can also cause strategic disadvantages in competition. The rapid development of AI technology requires careful cost-benefit analysis to make future-proof decisions and to avoid economic shipping fracture.
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AI as a decisive future factor for companies
The relevance of AI for the future can hardly be overestimated. A survey shows that 72 percent of all respondents are convinced that the lack of investments in AI endanger the future viability. This becomes particularly clear in German industry, where 78 percent of companies are convinced that the use of AI will be decisive for competitiveness in the future. For 70 percent, AI is even the most important technology for the future viability of German industry.
These impressive figures make it clear that the decision for or against AI no longer represents an optional strategic course, but is increasingly gaining existential importance. In this context, experts from the platform led by Acatech emphasize the need for a clear AI vision and cross-sector cooperation in order to keep up with international competition. The German economy is in a profound change: traditional product -oriented business models are replaced in almost all industries of data -driven products and services that are increasingly based on AI.
Particularly noteworthy is the fact that German companies have an immense treasure of machine and operating data that can provide you with a potential competitive advantage- provided you make this data economically usable using AI and develop innovative business models from it. To misconce this potential or to gamble through incorrect investment decisions could have fatal effects in the long term.
The speed of technological change as a risk factor
A decisive factor in AI investments is the relentless speed of technological progress. Sam Altman, the CEO of Openaai, recently warned in an interview: "If you think as a start-up, the progress will remain the same, then we will definitely overflow!". This drastic statement underlines that business models based on the current AI generation could already be outdated in the near future.
The dynamics of the AI market can be illustrated using the so-called “deepseek effect”. In January 2025, the Chinese start-up Deepseek caused significant price falls to established tech companies by presenting a particularly cost-efficient AI model. The US chip group NVIDIA, whose graphics processors have so far been considered indispensable for the training of AI models, lost almost 20 percent of its stock market value on a single day of trading-a loss of value of more than $ 500 billion. This example impressively illustrates how quickly supposedly safe investments in AI technologies can be devalued through disruptive innovations.
The danger is not only for technology providers, but also for companies that as users rely on certain AI solutions. Anyone who invests in expensive hardware and proprietary AI models today could find out tomorrow that more cost-effective and more efficient alternatives are available. Such bad investments not only bind financial resources, but can also restrict the company's flexibility and adaptability.
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The need for a comprehensive cost-benefit analysis
In view of these challenges, a thorough cost-benefit analysis before the implementation of AI is essential. Companies must take into account both the flow costs and ongoing expenses associated with AI implementation. This includes the establishment of the infrastructure, data acquisition, system integration and maintenance.
At the same time, it must be evaluated which added value AI can create in the corporate processes - be it through productivity increase, cost savings or improvement in efficiency. The Return on Investment (ROI) plays a crucial role in this assessment and helps prioritize AI measures.
The complexity of the cost-benefit analysis is also increased by the variety of AI methods, applications and areas of application. A concrete cost-benefit analysis is particularly difficult in research projects, since often only assumptions about monetary costs and benefits can be taken. Nevertheless, a positive cost-benefit balance is crucial for the acceptance of new technologies and thus for the speed of digital transformation as a whole.
Criteria for sustainable AI models and business models
In order not to rely on a “dead horse”, companies have to take several key factors into account when choosing AI models and business models. A AI business model consists of strategies and applications to make the AI commercially usable and integrate into the product portfolio. The future viability of such models depends on various factors.
First of all, seamless integration into existing systems is of crucial importance. AI systems should easily be inserted into the existing infrastructure and production systems. Even in the planning phase, it must be checked whether the desired system is compatible with the current hardware and software as well as the existing databases. Factors such as data formats, communication protocols and API compatibility play an important role here.
Another critical success factor is data quality and availability. The quality of the data ultimately decides on the quality of the entire AI project-poor data inevitably lead to inadequate models and false conclusions. This aspect is often underestimated, but is of crucial importance for the future viability of a AI solution.
The scalability of a AI solution must also be guaranteed. Many AI initiatives do not fail because of the initial implementation, but because of the successful scaling beyond pilot projects. A survey shows that three out of four decision-makers on C-levels are convinced that the company existence is at stake if they cannot successfully scale artificial intelligence in the next five years.
Last but not least, ethical and legal aspects must also be taken into account. The most advanced generative AI models currently come from the USA and China and often do not meet the ethical and legal requirements discussed in Europe. This can lead to significant problems in the long term, especially if there are questions of liability for AI decisions.
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Strategies for minimizing investment risks in AI projects
In order to minimize the risks of AI investments, experts recommend various strategies. One possibility is not to rely on a single AI product, but to enter into cooperation. “Rarely does a company have all the necessary competencies, the infrastructure, technologies and customer access for a AI-based solution. Technologically strong companies often lack knowledge in the areas of digital business model definition, software development and especially in marketing. Companies should therefore forge suitable alliances in their digital ecosystem, for example to maintain the required skills, but also to share data and infrastructure ”.
Another strategy is the use of “Ai as a Service” providers who sell services related to AI and can be used as a partner. This enables companies to remain flexible and benefit from progress in the AI area without having to bind to a certain technology in the long term.
In addition, an important element for a successful AI-based business model is its continuous care and further development. The quality of AI applications can decrease over time, for example because customer behavior changes. Such maintenance strategies for their AI solutions are often lacking, which can lead to problems in the long term.
The consequences of false AI decisions
The consequences of false decisions in the AI area can be far -reaching and far beyond financial losses due to misinvestments. A missed opportunity to use AI potential can lead to a significant competitive disadvantage. Companies that hesitate too long or rely on the wrong AI technology risk losing connection to more innovative competitors.
The history of the technology industry is characterized by companies that have missed the connection to technological developments. A current example is Intel, which has lost market shares in competitors such as AMD and Nvidia in recent years, especially in the AI and Gaming segment. Although Intel was once a leader in the semiconductor industry, the company partially missed the AI boom and must now make considerable efforts to catch up.
In addition to the economic risks, there are also legal and ethical challenges. The question of liability arises in the case of AI decisions that lead to damage. Since AI systems work based on large amounts of data and are trained by machine learning, it is often difficult to clearly assign responsibility for incorrect 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 certain AI models and business models is a strategic investment in the future viability of a company. Much decisions in this area can not only lead to financial losses, but also cause long -term competitive disadvantages. The cost-benefit calculation for AI investments must therefore go far beyond short-term financial aspects and take into account strategic dimensions.
The challenge is to make the right decisions in a rapidly developing technology environment. Companies have to differentiate between short -term trends and long -term developments so as not to rely on a “dead horse”. A clear AI vision, cross-sector cooperation and the continuous evaluation and adaptation of the chosen AI solutions are crucial in order to be successful in this dynamic environment.
Ultimately, it is not a question of whether a company should invest in AI - this question is already answered in view of the overwhelming meaning of AI for the future viability. Rather, the crucial question is how these investments should be designed in order to secure long -term economic success and not to suffer shipwreck on the way to the digital future. The careful consideration of costs and benefits, taking into account future trends and the flexibility to adapt to changed technology landscapes are the most important success factors.
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