Phase 1: Laying the Foundation – AI Strategy & Opportunity Identification (Weeks 1-4)

Every Swiss SME knows the scenario: Machines are running at full speed in production, order books are filling up, but back in the office, employees are still wrestling with Excel spreadsheets that have grown over years. Meanwhile, 95% of manufacturing industry leaders recognise the crucial role of Artificial Intelligence (AI) for future success. A striking discrepancy that brings us face-to-face with reality: Awareness is there, but implementation often lags behind.

Especially in Switzerland's manufacturing sector, we see that almost all companies – 98% to be precise – are already exploring or actively pursuing AI-driven automation. However, the reality for many SMEs is different. They are stuck in theory, while competitors, perhaps even from abroad, are already taking the first steps to optimise processes and reduce costs. The risk of falling behind is real.

Therefore, it's no longer enough for Swiss SMEs to just talk about AI. It's about laying a solid foundation to identify strategic opportunities and set the course for successful, sustainable implementation. The first four weeks – Phase 1 of strategic AI work – are crucial for building this bridge between insight and action.

How can Swiss SMEs identify and assess the strategic importance of AI for their business?

You can identify the strategic importance of AI for your SME by conducting a thorough environmental analysis and critically examining your core processes. Many managing directors see AI as a black box, something only accessible to large corporations. This is a fallacy. The first and most important step is a systematic engagement with your own business environment and internal operations. As Lukas Huber, founder of schnellstart.ai, has repeatedly experienced in my practice, the greatest potential often lies where the pain is greatest – in repetitive, time-consuming tasks that are still done manually today.

Start with an environmental analysis that goes beyond simply observing trends. A proven framework like PESTEL (Political, Economic, Sociocultural, Technological, Environmental, Legal) helps you structure and assess external influences on your business. What political frameworks, such as Swiss data protection legislation (DSG), do you need to consider? How are labour costs (CHF) developing and what impact does this have on your competitiveness? Technological advancements, such as the availability of Swiss hosting solutions for AI applications, offer new opportunities you should be aware of. Such analyses are the basis for recognising opportunities and minimising risks.

Internally, you need to put your business processes to the test. Where are the bottlenecks? Which tasks tie up too many hours of your valuable employees? Think about customer service administration, manual data entry in logistics, or the recurring analysis of production data. Each of these tasks is a potential candidate for AI-driven optimisation. It's not about automating everything immediately, but about identifying the areas where efficiency gains have the greatest leverage and deliver measurable results. A close look at the value chain, from procurement and production to sales and customer service, will reveal these weaknesses.

Merely exploring AI possibilities, as 98% of manufacturing companies do, is not enough. You need to assess its relevance for your specific business model. An SME in mechanical engineering has different requirements and potentials than a service provider in the financial sector. Strategic AI work in Phase 1 aims to find these individual points of connection. This means not blindly following every hype, but specifically examining which AI application creates real added value for your customers or significantly improves your internal processes. Focus on those areas where AI can make a direct contribution to your value creation – be it through cost reduction, revenue increase, or risk mitigation.

Assessing strategic importance is also a question of resources. What can you achieve internally, and where do you need external support? It often becomes apparent here that SMEs benefit from the expertise of specialised partners who bring an external perspective and know the best practices from various industries. It's about developing a clear vision of how AI can transform your company in the next 3 to 5 years, without getting lost in the countless possibilities.

What concrete steps are necessary to successfully implement AI-driven automation in Swiss SMEs?

Successful AI implementation in Swiss SMEs first requires a clear problem definition, followed by the identification of relevant use cases and small-scale piloting. The mistake many companies make is starting with the technology instead of the problem. The result is often expensive but useless projects that quickly fizzle out. So, before you think about algorithms or data volumes, precisely define which business problem you want to solve. Is it about reducing production lead times by 15%, reducing the error rate in quality control by 20%, or automating customer support inquiries by 10 hours per week?

After defining the problem, detailed identification of use cases follows. This involves finding concrete processes or tasks where AI can make a measurable difference. Consider the areas identified as potential pain points or efficiency bottlenecks in your environmental analysis. For a Swiss SME in the manufacturing sector still working with outdated spreadsheets, a use case could be automated production planning or predictive maintenance of machinery. Instead of manual data entry and estimation, an AI system could predict the optimal maintenance time based on historical data and real-time sensor data, minimising downtime and thus increasing productivity.

A structured approach to use case prioritisation is essential. Not every identified use case is equally valuable or equally easy to implement. Evaluate the potential use cases based on two criteria: expected business value (e.g., cost savings, revenue increase) and technical feasibility (e.g., data availability, implementation complexity). Focus on those projects that promise high business value and, at the same time, offer a realistic chance of quick implementation. These "quick wins" build trust and demonstrate the value of AI within the company.

Following prioritisation comes piloting. Choose a use case with high potential and manageable risk. Implement the AI solution in a small, controlled environment. The goal is to learn quickly, iterate, and prove the value of AI in practice. Such a pilot phase is crucial for testing the technology, fostering employee acceptance, and gaining valuable insights for broader scaling. It's about not waiting for the perfect start, but making a good start and improving step by step. This is the core of the "Foundation" phase (Weeks 1-4), where we lay the groundwork for further progress and identify the first projects.

Another important aspect is data availability and quality. No AI is better than the data it's trained on. Before you start implementation, you must ensure you have sufficient, high-quality data. This often involves cleaning and structuring existing data. The issue of data security and compliance with Swiss DSG must also not be underestimated. Swiss hosting solutions are a clear advantage here to ensure data sovereignty and security.

Why are the development of AI competencies and the adaptation of workflows crucial for the future viability of Swiss companies?

The development of AI competencies and the adaptation of workflows are crucial because AI is not a technology you simply "switch on," but one that enhances human capabilities and requires new forms of collaboration. Many SME managing directors fear that AI will destroy jobs. However, the reality is that AI is changing the way we work. Those who do not actively shape this change will be overwhelmed by it. OpenAI, for example, is doubling its workforce to accelerate innovation and address ethical considerations in AI development – a clear sign that human expertise remains indispensable in the AI field.

It's not about every employee becoming a data scientist. Rather, key individuals in the company – from management and department heads to specialists – need to develop a fundamental understanding of AI's possibilities and limitations. This understanding enables them to recognise potential, formulate requirements for AI solutions, and critically evaluate the results. We are talking about "Working Strategically with AI" – a core competency that needs to be built in the first phase of the AI journey. It is an investment in human capital that pays off in the long run.

Adapting workflows is the logical consequence of competency development. When AI takes over repetitive tasks, employees must be trained and deployed for more complex, creative, and strategic activities. This often requires a re-design of processes and roles. For example: If an AI system automates invoice verification, finance employees can use their time for analysing financial data, optimising cash flows, or strategic planning. This transformation must be actively managed to reduce resistance and win employees over as partners in this change.

This is particularly relevant for Swiss companies, as the shortage of skilled workers is noticeable in many sectors. AI can help to use existing resources more efficiently and increase the company's attractiveness as an employer. Employees who work with modern tools and can develop further are more motivated and loyal. Investing in AI competencies is therefore also an investment in the retention and development of your workforce.

The future viability of a Swiss company depends significantly on how agilely it adapts to new technological realities. This means not only adopting new technologies but establishing a culture of continuous learning and adaptation. Phase 1 of the AI strategy, the "Foundation" phase, lays the groundwork precisely here. It's about preparing the organisation to seize the opportunities of AI and proactively address the associated challenges. This also includes understanding AI governance frameworks, which ensure that AI is used ethically and in compliance with the law – a must in the Swiss context, especially with regard to the DSG.

Conclusion: The First Steps Count

The transformation through Artificial Intelligence is no longer an option for Swiss SMEs, but a necessity to remain competitive. The first weeks of strategic engagement with AI – laying the foundation – are crucial not only for following trends but for actively shaping your company's future.

You should take away three key insights:

• ✅ Analyse your environment and processes: Understand precisely where AI can create the greatest added value for your specific Swiss SME, based on a sound environmental and process analysis.
• ✅ Define clear use cases and pilot: Start with concrete problems, identify realistic use cases, and test them in small, controlled pilot projects.
• ✅ Invest in competence and adaptation: Train your employees and adapt your workflows to fully leverage AI's potential and make your organisation future-proof.

The path to successful AI implementation may seem complex, but with a structured approach and the right partners, it is achievable for any Swiss SME. It's time to move beyond talking and take the first, crucial steps.

Would you like to thoroughly assess the strategic relevance of AI for your company and plan the first concrete steps? Contact us for a no-obligation initial consultation.