Serial, Generative Artificial Intelligence
The wave of enthusiasm for generative AI is gigantic. Hardly any other technology is currently electrifying companies as much as the possibility of using AI to create creative content and solutions. Managers are recognizing the enormous potential to achieve efficiency gains in areas that were previously difficult to automate. However, there is a gap between vision and reality: Generative AI is still not very widespread in practice.
Why is that? Our experience from numerous projects with DAX-listed companies and SMEs shows that many companies shy away from the perceived high costs and complexity associated with AI projects. There is a lack of concrete, quickly implementable use cases that deliver a clearly defined business value.
In our projects, we have learned that an agile, step-by-step approach is the key to success. Instead of getting bogged down in complex large-scale projects, we focus on specific use cases that can be implemented quickly. With this focus on AI quick wins, tangible improvements can be achieved with manageable effort - and a comprehensive AI strategy can be developed step by step.
Complexity and lack of experts
Despite the enormous potential of generative AI, many companies find it difficult to use the technology profitably. One of the main obstacles is the complexity of the subject matter. Without a sound understanding, it is difficult to identify meaningful use cases and realistically assess the possibilities. Many decision-makers mistakenly consider AI to be immature and underestimate the maturity level of current systems. However, solutions have long been available for most common AI-associated problems.
The lack of AI experts makes things even more difficult. Only a few companies have dedicated teams that are familiar with the implementation of generative AI. This knowledge is essential in order to use the technology strategically and tailor it to the specific requirements of your own business. The rapid progress of the technology also presents companies with the challenge of constantly keeping their expertise up to date.
Due to these hurdles, the reluctance of many organizations is understandable. However, this should not be a reason to put the topic of AI on the back burner. After all, those who find pragmatic solutions now will gain a decisive competitive advantage.
Use cases instead of strategy
Our experience shows that many companies are still discussing the topic of generative AI in a very abstract way. There is a lack of concrete, practical use cases. Although most companies are aware of their pain points, only a few know that many of them can be solved with AI.
To close this gap, we have developed standardized AI modules for a large number of recurring use cases. These are ready for immediate use and can be integrated into existing IT landscapes with little effort. Our focus is on quickly implementable, robust solutions with clearly defined added value. No abstract visions of the future, but tangible tools that deliver measurable improvements within manageable timeframes and budgets. We focus specifically on resilient use cases and start with small, low-threshold pilot projects. These are evaluated according to clearly defined criteria and scaled up quickly if successful. In this way, we ensure that business operations are not jeopardized and that only concepts that have proven themselves are used on a larger scale.
AI use cases
Here are three practical examples of common pain points and how we create measurable added value with AI.
- Intelligent test management: Creating test cases manually is complex, time-consuming and requires a lot of expertise. In collaboration with a DAX-listed company, we have developed an AI-supported solution that automatically generates a large number of test cases for a wide range of scenarios based on processes and master data. Output in common formats or transfer to test automation included. The result: significantly reduced effort and increased test coverage and speed.
- Automated documentation creation: We automate the time-consuming and error-prone creation and maintenance of documentation. Process sequences and relevant data are recorded and our AI module uses them to generate standardized, always up-to-date documentation. This saves valuable resources while ensuring a consistent quality standard.
- AI-supported order entry: Entering orders manually from various sources costs time and money, but is common practice, especially in SMEs. We can fully and intelligently automate this process: Our AI module processes incoming orders from emails, documents or phone calls, extracts all relevant information, creates the appropriate processes independently and even requests missing data by email if necessary. This massively speeds up and streamlines data entry processes and frees up resources for more value-adding tasks.
Automated documentation creation, intelligent test management and AI-supported order entry are just three examples of such turnkey solutions. In all of these cases, AI specifically addresses inefficient, error-prone processes that were previously difficult to automate. The result is massive efficiency gains, cost savings and quality improvements - with minimal implementation effort and risk.
AI-supported automation of such specific use cases enables rapid value creation with manageable effort and low entry barriers. This allows companies to gradually tap into the potential of generative AI without having to tackle a complex large-scale project straight away. Incidentally, these and many other use cases are integrated into our holistic ProcessBridge platform. Linking the various AI applications creates valuable synergies: process knowledge from the documentation flows into test case creation, while findings from order entry optimize document processing. This creates a central knowledge base that grows with each use case and continuously increases the added value of the platform.
Collaboration and agility
In numerous AI projects with companies of various sizes and industries, some key success factors have emerged. Start small, scale fast: Start with a manageable AI pilot project in close collaboration between technical experts and AI specialists. After successful validation, the solution can be quickly rolled out and scaled to other areas; agile, iterative approach: Long development cycles without feedback are the wrong approach. Instead, agile methods have proven their worth: Solutions are developed incrementally in short sprints, continuously validated and adapted to the findings. This keeps costs and risks manageable; realistic expectation management: AI projects need a start-up time. It often takes several weeks for models to reach their full potential and for new processes to become established. This introductory phase must be planned for from the outset to avoid frustration.
With this pragmatic approach, many of the typical hurdles in AI projects can be avoided. Of course, there is no guarantee of success - but there is a good chance of making concrete progress quickly and developing a robust AI strategy step by step.
Small steps, big transformation
The potential of generative AI is enormous, but many companies find it difficult to exploit it. The hurdles seem too great, and getting started with complex lighthouse projects too risky. Yet the key to success lies in pragmatic quick wins: manageable AI solutions that specifically address everyday problems and quickly deliver concrete improvements. With such projects, a sustainable AI strategy can be built up step by step that is geared towards the actual needs of the company. Instead of getting lost in abstract visions of the future, facts are created and measurable values are generated. Turnkey AI modules are an ideal starting point for this: they are quickly ready for use, tried and tested in practice and tailored to specific use cases.
Our appeal to decision-makers is therefore: don't be afraid to take the first step, even if it seems small. Because it can be the start of a major transformation. With manageable effort, valuable experience can be gained that lays the foundation for a future-proof, AI-supported organization. The road may be long, but it starts with a pragmatic initiative. And there is a good chance of achieving tangible success from the very first few meters.
