AI, AI use cases and data 2025: the key to competitiveness and future security
#AI2025 #AgenticAI #GenAI #ResponsibleAI #AI-TOM
The year 2025 has started with a bang: 500 billion US dollars for Stargate (the AI initiative, not the science fiction series from the 90s), China's “Deepseek” and the associated hysteria, which (briefly) cost NVIDIA 600 billion in stock market value in January alone.
What do these headlines mean for decision-makers in German companies? 2025 is a year of setting the course. The key question is no longer whether AI is relevant, but how it can create targeted added value. The technology is developing rapidly, but the decisive success factor is not algorithms or computing power, but people who use AI boldly and intelligently.
Success factors for future-proof business models and broad use of AI use cases
Companies are not only securing efficiency gains with AI, but also sustainable competitive advantages. Technological development remains rapid: agentic AI, responsible AI and generative AI are important transmission belts for business models. There is no universal “one-size-fits-all” solution - every company has to find its own way. However, a proven approach can help to steer the transformation in a targeted manner:
- Successfully implement and scale AI use cases - Further develop and scale existing and new use cases to generate tangible benefits.
- Ensure AI delivery capability - A robust AI operating model is crucial not only to initiate innovations, but also to implement them successfully in the long term.
- Make generated benefits visible and tangible - AI must not remain an abstract strategy. It must be reflected in real added value for customers, more efficient processes and new business models.
This year will be groundbreaking - for companies that strategically and responsibly integrate AI into their business models.
Continue to successfully implement AI use cases
Use cases will extend across almost all areas of the company - from planning and control, fraud and risk detection, pricing and forecasting to predictive maintenance. However, three focus topics will be particularly relevant in 2025: Agentic AI, Responsible AI and GenAI.
Agentic AI: These are AI systems that make decisions independently and act in real time - with or without human intervention. In 2025, companies will increasingly face the hurdle of integrating this technology into complex business processes such as planning, control and automation.
Agentic AI promises greater efficiency and agility as it reacts in real time and makes proactive decisions. This advantage comes with challenges: companies must ensure that they retain control over these technologies and define clear responsibilities and control mechanisms. All in line with the AI Act that has come into force. The integration of Agentic AI into business processes requires a well thought-out governance model that maximizes benefits while minimizing potential risks.
Responsible AI: With the increasing spread of AI, the pressure to make decisions transparent, comprehensible and ethically justifiable is growing. Especially in highly regulated industries such as the financial services sector, it will become increasingly important in 2025 that companies can present their AI-based decision-making clearly and comprehensibly. Explainability requirements are crucial to ensure the trust of AI users - and thus the acceptance of AI systems.
Generative AI: And yes - Generative AI (GenAI) will continue its triumphal march. This technology has already demonstrated its impressive automation potential in 2024. In 2025, successful PoCs and use cases will need to be professionalized, scaled and, of course, expanded. Willingness to take risks will be rewarded. Text-heavy, internal company processes are particularly well suited to GenAI and offer an ideal basis for gaining experience with the technology. But the potential is far from exhausted. And acceptance of reality is the order of the day: GenAI is being used by many employees across all sectors. Either via a system offered internally or, for example, via a ChatGPT “brought to work”.
2025 will be the year in which companies will have to actively manage the use of GenAI - not just prohibit or tolerate it. A well thought-out concept ensures that innovation and security go hand in hand.
Ensuring AI delivery capability - the key to success
Many companies are currently only using AI selectively, for example in individual use cases such as chatbots or selected automated processes. A proof of concept (PoC) can be technically successful, but without the right structure it remains an isolated experiment - without sustainable benefits. What is missing? A well thought-out AI operating model that ensures that AI is strategically integrated in the long term. Such a model ensures that AI systems are seamlessly integrated into the business strategy.
- Technologies and data: Every successful AI strategy starts with a robust data architecture and powerful infrastructure - this is essential for reliable AI models.
- Structures and processes: Clear governance and defined responsibilities are critical to efficiently integrating AI into business processes.
- Expectations and goals: Only when companies define clear KPIs (key performance indicators) can the actual benefits of AI for the company be made measurable.
- Change management and acceptance: sensible, effective and well-dosed process changes through AI systems with an appropriate degree of explainability are the key to acceptance among the workforce and beyond.
AI can deliver far more than just selective increases in efficiency - provided that companies create the right framework conditions. The decisive factor is a strong AI operating model, which determines whether an organization successfully scales its AI strategy or remains stuck in the experimental phase.
Making generated benefits measurable and realizing them
Why is benefit measurement crucial? On the one hand, significant investments are required for the use of artificial intelligence. This raises the question of when this is justified. Secondly, a solid measurement of benefits creates trust in the technology - internally among managers and employees, and externally among investors, partners and customers. It shows where AI makes a real difference, where adjustments need to be made and where investments would be better spent on other technologies.
What are the benefits of AI in day-to-day business? A distinction must be made here between “multi-purpose” AI such as ChatGPT and specific AI applications (sometimes as add-ons in the original systems). Since the positive effect of a multi-purpose AI diffuses very quickly within an organization, a general approach to assessing and realizing benefits is practicable. For example, the reduction in operating costs or the increase in efficiency - possibly differentiated by business unit - can serve as an initial indicator.
For specific AI applications, the benefits can be considered in a more differentiated way with the help of key performance indicators (KPIs). Three KPI dimensions play a key role here:
- Financial KPIs: “Output” becomes a directly measurable “outcome”. Financial benefits are probably the strongest argument for the effectiveness of AI - as obvious as this may seem.
- Operational KPIs: Efficiency increases and process improvements through AI, for example in the form of faster processing times through automation or error reduction, are important key figures - relief is clearly measurable!
- Customer-centric KPIs: The acceptance of AI from the end customer's perspective can be seen, for example, in improved conversion rates or a higher customer lifetime value (CLV). Combined with qualitative feedback, a multidimensional view is possible.
In the end, one thing is clear: beyond the hype, AI costs money and initially requires a corresponding financial commitment from the company. Especially in economically strained times, this must be offset by a measurable benefit. There is no one-size-fits-all solution for this, but it requires sufficient tact and determination to avoid nipping every innovation in the bud.
Conclusion: 2025 - decisive action counts now
The course has been set for AI - worldwide. Whether in the USA, China or Europe, politicians have long recognized the importance of artificial intelligence and have adapted to the changed framework conditions. For companies, this means that the rules of the game are changing. Those who see AI not just as a trend, but as a strategic growth driver, can secure decisive competitive advantages. It will also become clear which companies are losing out because they hesitated too long.
Isolated pilot projects, as was previously the norm, are no longer enough. Now it's all about scaling, efficiency and the measurable added value of AI in everyday business. The targeted identification of AI use cases is the decisive lever for fully exploiting potential. Companies that have already invested consistently in 2024 will further extend their lead. For those who have only tested AI selectively so far, 2025 will be a test: how quickly can they catch up? How can insights from other companies be used? And above all - how will AI become a real value driver?
This transformation cannot be achieved single-handedly and certainly not in an ivory tower. The introduction and scaling of AI requires a strong network of partners who cover all facets of the AI transformation - from technology and data infrastructure to governance and change management through to ethical and regulatory requirements.
What is needed is not actionism and blind haste, but a well thought-out strategy. Companies that focus on the right AI use cases, prioritize based on data and consistently measure success will not only catch up in 2025 - they will benefit in the long term.
The time for procrastination is over. 2025 will be the year of decisive implementation. What counts now is who not only tests AI, but also uses it as a real game changer - with courage, strategy and the right partners.
