Recession, demographics, skilled labor shortages, pressure to transform, and the advance of AI are presenting new challenges to SMEs. A popular response: leaner structures, fewer people, more technology. But is it really that simple? Companies that primarily focus on streamlining and fail to retain future-relevant skills within their organizations are not necessarily well-positioned for the future.
The wave of layoffs and (early) retirements is underway – this is the impression one gets when following economic news. The magic word: cost reduction. The hope: compensation through robots, sleek software solutions, and artificial intelligence. But not every routine task is truly automatable right now. And not all experiential knowledge can be reconstructed retrospectively once the people who accumulated it during their careers are retired or have been laid off.
Of course, companies need to become more efficient. Medium-sized industrial companies, in particular, are under pressure. But workforce reduction alone is not a future strategy, because while it cuts costs in the short term, it doesn't answer the crucial question: What skills will the company need in three, five, or ten years?
Many companies know exactly how many employees they have. Far less often do they know precisely what future-relevant knowledge their respective employees possess. Who knows the peculiarities of certain systems? Who understands the history of important client projects? Who knows why a process works differently in practice than in the documentation? Who identifies quality problems before they become costly? Who has built robust supplier relationships? Who can fix malfunctions because they've seen them ten times before? And who might even have developed additional skills in their free time that could become relevant in the future?
This knowledge is rarely fully documented in manuals. It lies in experience, routines, relationships, and informal shortcuts. When employees leave and positions are not refilled, problem-solving capabilities often disappear as well.
AI cannot (yet) be fully considered a direct replacement for vacant positions. It replaces or accelerates the execution of tasks, can prepare texts, analyze data, recognize patterns, speed up documentation, support planning processes, or pre-sort service requests. But it also needs people to review, categorize, and take responsibility for the results. Those who reduce skilled staff before processes and AI applications reliably function weaken these important competencies.
This was recently highlighted in a report about AI offering image-based diagnostics. This AI immensely accelerates the work of doctors. But only if the doctors also know what to look for.
From my perspective, it is crucial for SMEs to distinguish between necessary efficiency gains and dangerous streamlining (not to say: depletion). Let me list some risks:
▸ Experiential knowledge is lost faster than new knowledge is built, for example, when several experienced specialists and managers leave within a short period;
▸ AI potentials are overestimated, and expected automation effects do not materialize or require greater human oversight;
▸ The remaining workforce loses trust in job security due to staff reductions, and demotivation spreads. High-performers, in particular, then readily move to other companies.
▸ Future competencies are also reduced: Transformations require people who can bring together products, markets, processes, data, and technology. If these competencies are too severely reduced, the company loses its adaptability.
The question should therefore not be where positions can be reduced as quickly as possible. The decisive question is: What skills will we need in the future, which ones will we lose through staff reductions or retirement, which ones can we develop, and which ones must we secure? The solution is an age structure and competency map that identifies critical functions, experiential knowledge, deputy arrangements, bottlenecks, and risks, especially for areas where knowledge is concentrated among a few individuals.
Also important is targeted knowledge transfer, for example, through tandem models, structured experience interviews, process documentation, mentoring, age-diverse teams, and planned dual staffing. Even if this costs time and money, it is still cheaper than trying to rebuild lost operational capacity afterward. For some of these activities, there is even – AI.
A critical and realistic check of AI applications is also worthwhile: What works how well, can productivity gains and relief actually be measured, and where do new demands arise for data quality, control, IT security, or process responsibility? AI changes work. Therefore, employees must learn through training and further education to use new tools productively, safely, and responsibly. This applies not only to IT departments but also to specialist departments, managers, and operational functions.
Leadership communication is also evolving. Those who expect employees to embrace change must clearly articulate the direction. People are more likely to accept change if they understand their role and their future prospects.
SMEs therefore don't need a knee-jerk downsizing strategy, but primarily a skills strategy. Staff reductions can be part of a transformation. Decision-makers should assess which people, skills, and experience they will need for their future. Because what's crucial is whether companies can continue to grow with fewer, different, or differently qualified people.
Do you want to know which competencies in your company are at risk due to retirement, AI implementation, or reorganization? I can help you identify critical knowledge, prioritize future-proof skills, and develop a sustainable future strategy. Contact me before necessary streamlining turns into a dangerous loss of substance.
