Artificial intelligence (AI) is increasingly permeating the German business sector, with a significant increase in its adoption, as highlighted in a study by the digital industry association Bitkom. The study shows that the number of companies using AI has jumped from 9% to 15% in companies utilizing AI within a year. This happened notably due to the boom in generative AI.
Therefore, in this text, we show the need for requirements based mainly on the technology trend of generative AI respectively LLM. However, despite this increase and the subsequent recognition of AI’s importance, there is a noticeable gap between perception and implementation, with many companies feeling that AI is not relevant to their operations. “Generative AI looks spectacular but brings little benefit to the company,” is a sentiment shared by half of the companies surveyed.
Mapping business needs to technical capabilities: Artificial intelligence, which is seeing increased confidence and use in German businesses, particularly in text and language processing, is fundamentally a tool that needs to be precisely aligned with its intended purpose to reach its full potential. As German companies recognize the potential of AI in report generation and translation (82%), followed by marketing and communications (59%), it is important to tailor AI’s capabilities to these specific needs to avoid costly misdirection.
Stakeholders in these organizations can clarify the exact problems AI must solve by defining precise requirements, helping developers gain a clear understanding, and avoiding unnecessary complexity or oversimplification.
- Optimize costs: Because AI projects are resource-intensive, they can lead to cost escalation if not precisely defined. This is evidenced by the experience of German companies, where unclear and unregulated implementations have led to perceptions of limited value. Clear requirements can streamline processes and guide execution, ensuring optimal use of resources and staying within budget – a critical consideration given the resource constraints cited by many German companies.
- Avoid data overhead: Data, which is critical to effective AI implementation, must be relevant and accurate. German companies, especially those in the initial stages of AI adoption, must ensure the collection of relevant data to strengthen the accuracy of the model. This means focusing on collecting and processing only relevant data that directly contributes to the accuracy of the AI model, thus ensuring data integrity.
- Ensure scalability and flexibility: In a landscape where only 2% of organizations are centrally implementing generative AI and 13% are planning to do so, it is critical that AI solutions not only meet immediate needs but also are scalable and adaptable to future requirements. With a clear understanding of the scope, solutions are more likely to be scalable through flexible architecture and design, and highly adaptable to future business changes or expansions.
- Legal and ethical considerations: With many German companies advocating for balanced AI regulation and clear rules, the integration of AI into business operations overlaps significantly with legal and ethical considerations. This is particularly important in sectors such as healthcare, finance, and public services. Properly defining requirements can help address regulatory concerns and ensure that AI solutions meet ethical standards, reducing liabilities and ensuring compliance.
- Ensure stakeholder alignment: Given that the transformative potential of AI requires the alignment of all stakeholders, precise requirements definition provides a clear vision of the end goal and fosters understanding among different stakeholders. This is particularly important in the German context, where many companies let AI operate unregulated and without clear guidelines, with only 1% having clear guidelines for the implementation of generative AI.
- Robust test and validation approach: Against the backdrop of increasing AI implementation in Germany, the performance and reliability of AI solutions depend heavily on rigorous testing and validation against clear evaluation metrics and benchmarks. A well-defined set of requirements allows developers to perform focused and meaningful testing, ensuring that the validation phase is aligned with the intended business objectives.
Integrating AI solutions is not just an act of technology integration, but a strategic step towards redefining operational capabilities.
Sharp vision and precise requirements become the beacon that guides this journey, ensuring successful AI adoption amidst varying perceptions and the rising recognition of AI’s importance in the business sector.