Modern technology and a structured approach can help small and medium-sized enterprises get ready for the future
As small and medium-sized enterprises (SMEs) in Germany face ever increasing pressure to optimize their business processes, reduce costs and leverage new business opportunities, digitalization is key to remaining competitive. There are several digital solutions that can help, from production process automation to the implementation of digital business models and the use of big data and artificial intelligence. In cooperation with Assecor GmbH, the Steinbeis Transfer Center for Network Planning and Evaluation in Stralsund developed an AI maturity model to assess/analyse a company‘s status quo.
Innovation is a key driver of competitiveness in German SMEs, which have traditionally been characterized by their in-depth market knowledge and direct contact with customers and suppliers. [1] But relying on these long-established strengths is no longer enough. Today’s SMEs need to combine traditional success factors with modern innovation methods. Artificial intelligence (AI) has a key role in the digital transformation. It encompasses fields like machine learning, deep learning and natural language processing, with different aspects currently at different stages in their development.
Machine learning uses algorithms to learn from data and make predictions. Deep learning is a special form of machine learning that employs neural networks to recognize and optimize complex patterns and relationships in large datasets. Natural language processing enables machines to understand and process human language. [2] These technologies and trends have a wide range of potential applications in SMEs, from the automation of production processes to the improvement of customer service through intelligent chatbots. Their adoption can strengthen innovation in SMEs and make them ready for the future.
Maturity model determines status quo
The maturity model describes what has been achieved once each level is attained (Source: Assecor GmbH)
But how can small and medium-sized enterprises manage the complexity of dealing with applications like ChatGPT and integrating AI into their own IT landscape? The first step is analysing the company‘s current situation and use cases for the technology. The team at the Steinbeis Transfer Center for Network Planning and Evaluation in Stralsund joined forces with Assecor GmbH to develop a structured model to help with this process. The maturity model measures progress in the adoption of artificial intelligence in different areas of the business. It starts with a questionnaire that establishes the company’s current technology readiness level (TRL) through a series of questions about its strategy, organizational structure, culture, technology and six other areas. The consolidated outcome (one of four possible levels) is not the only thing that matters here – the specific answers given to the set questions are equally important, since they provide an initial insight into the company’s status quo and help to identify areas where artificial intelligence can provide added value.
The maturity model enables significant cost and time savings compared to the extensive interviews that would otherwise be needed to assess a company’s progress in artificial intelligence. The primary objective of this methodology is to create a shared understanding and develop strategies tailored to each company’s specific value creation model and TRL.
AI-generated skills profiles
A company that has successfully used the maturity model is Growify. The skills-based employee development start-up faced the challenge that creating skills profiles for different roles cost a lot of time and resources. Drawing up the profiles manually required considerable human and financial resources to carry out the time-consuming research and interviews. The development of a prototype that integrated Google Sheets and GPT for Sheets allowed the process to be automated, speeding it up significantly. Now, the young company could produce 2,000 skills in just twelve minutes, a process that had previously taken 240 hours of manual work. This resulted in a impressive cost savings of around 97%.
The solution allowed Growify GmbH to significantly improve the efficiency and quality of skills profile creation while significantly reducing the costs and resources involved. This example shows how artificial intelligence can be applied in the field of employee development – without using proprietary information – and how it was able to raise the bar for efficiency and innovation at Growify.
A systematic, structured approach is key
Example of a Customer Value Chain (Source: Assecor GmbH)
The implementation of AI in businesses calls for a systematic, well-structured approach. The table on the previous page shows an example of a tried-and-tested approach comprising six levels, each building on the previous one. The model is tailored to each company’s specific circumstances. In the first level, the company’s employees learn the basics of AI and machine learning in training courses and seminars. In the next level, workshops or keynotes outline the importance of AI to the business. This is followed by a business analysis that identifies opportunities to use AI and creates prototypes. Level four analyzes and discusses the impacts of AI on data privacy and the company’s staff and organization. The next step is to carry out a requirements analysis and a consultation on the appropriate instruments for implementation. The final stage involves the development and implementation of an individual AI solution that is tailored to the company’s specific requirements and eflects the technology’s capabilities.
It is vital for SMEs to embrace these new technological opportunities to optimize their current value chains and potentially even develop new ones. The starting point is understanding the technology’s possible applications and test them based on a Proof of Concept (PoC). AI shouldn’t be adopted for the sake of it – it should always offer tangible benefits. But the true key to maintaining and strengthening a company’s innovative power is the management‘s willingness and curiosity to evaluate and trial the use of new technologies in a structured manner. Long-term collaborations with universities and research institutions can help with this. This is how strategic networks are created, knowledge is bundled and an exchange of practices is promoted, playing its part in strengthening the engine of the German economy.
Contact
Prof. Dr. Norbert Zdrowomyslaw (author)
Freelance project manager
Steinbeis Transfer Center for Network Planning and Evaluation (Stralsund)
Christian Wulf (author)
Site manager
Assecor GmbH (Stralsund)
Richard Kluth
AI specialist
Assecor GmbH (Stralsund)
References
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Zdrowomyslaw, Auerbach, Wulf; Von der Rolle der Innovationskultur und des Innovationsmanagements in Unternehmensinnovationssystemen; Accessed at https://transfermagazin.steinbeis.de/?p=15178 on 19.06.2024
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Gartner Incorporated, Was ist Künstliche Intelligenz? Accessed at https://www.gartner.de/de/themen/kuenstliche-intelligenz on 19.06.2024
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