AI in use: what really works in SMEs and what doesn't
- The reality in the SME sector: between skepticism and buzzword bingo
- What (doesn't) work: Three observations from the field
- Use case: Automating quotation processes in technical sales
- What matters: data, clarity, integration
- Conclusion: Small steps, clear goals - and no AI theater
1. the reality in the SME sector: between skepticism and buzzword bingo
The topic of AI has long been on the radar of many decision-makers, but the technical possibilities rarely lead to a concrete use case. Why is that?
- Because many offers are too generic or too technical
- Because benefits and costs are not made tangible
- Because AI projects are often treated like innovation showcases instead of real business solutions.
👉 Tip: The next time you make an AI proposal, ask directly: What specific problem does it solve - and what does the business case look like? This separates show from substance.
2. what (doesn't) work: Three lessons learned from practice
❌ What does not work:
- "AI implementation projects" without a clear use case
- Isolated pilots in IT, without involving the specialist departments
- Tools that promise more than they deliver in everyday life and in existing structures
âś… What works:
- Specific problems from the operational business (e.g. quotation processes, lead prioritization, after-sales)
- Existing data + clear process + tangible goal
- Involvement of the people who are supposed to work with it and no black box AI
👉 Best case: A company tests an AI assistant in just one sales team - with a focus on pre-qualifying leads. The model is quickly improved because real feedback is incorporated. It is scaled up after 6 weeks.
3rd use case: Automating quotation processes in technical sales
A machine manufacturer receives inquiries about standard products with individual requirements on a daily basis. Sales and engineering create quotations manually, often under time pressure, with queries and media disruptions.
The process without the use of AI:
- Incoming requests (e.g. PDFs, emails) are read automatically
- The AI recognizes technical parameters, compares with existing orders
- Suggestions for configuration, price, delivery time are generated
- The sales department checks and finalizes the offer
The result:
- Offer time decreases from 3 days to less than 24 hours
- Standard requests are automated, special cases are handled in a more focused manner
- Sales gains time for consulting and closing
👉Tip: You don't need a huge tool. A targeted module or a low-code solution that complements existing processes - and really makes use of your data - is often enough.
4 What matters: data, focus, integration
AI is only as good as the setup. That means:
- Data access: Historical offers, product data, CRM - no benefit without a clean database
- Clear focus: Not: "We're doing something with AI". But rather: "We want to make XY more efficient"
- Process integration: AI must fit into everyday life. If employees bypass it, it's a waste of budget
What is often overlooked: It's not about automating as much as possible - it's about automating the right things. The best projects start with a clear question: Where are we currently wasting too much time on recurring tasks with clear rules?
👉 Best case: A sales team defines the three most common types of inquiry - and starts right there. After the test run, the system is refined and expanded. In this way, the solution grows with demand.
5 Conclusion: Small steps, clear goals and concrete use cases
AI can bring real added value, especially in SMEs, where capacities are limited and skilled workers are scarce. But only if you...
- start with real problems, not visions
- do the math, not just present it
- involve your teams, don't overburden them
You don't need an AI strategy. You need an initial project that works.
👉Tip: Take an hour with your sales or service team. Ask: What task is holding us up - even though it is actually standardized?
Your AI project starts there. Not a buzzword, but concrete use cases.
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