Please see below as inspiration 10 use cases from different small and medium sized companies and from various company departments.
1. Marketing – Mechanical engineering company – 120 employees
USE CASE: Automated content creation & SEO. PROBLEM: The company had little capacity to produce website and social media content on a regular basis. SOLUTION: Introduction of an AI tool for the automatic creation of product texts, blog posts, and LinkedIn updates. RESULTS: +70% more website content per month; +35% more organic leads after 6 months; Marketing team saves approx. 10 hours per week.
2. Sales – Construction company – 85 employees
USE CASE: Quote automation in B2B sales. PROBLEM: Quoting processes often took 2–3 days because technical details had to be researched manually. SOLUTION: AI-supported quotation generation that automatically combines CRM data, price lists, and product configurations. RESULTS: Quotation time reduced from 48 hours to 4 hours; +22% closing rate as customers received responses faster; Less errors in pricing and product mix.
3. Customer service – Online retailer – 35 employees
USE CASE: AI chatbot & automatic ticket creation. PROBLEM: The small support team was overloaded, with response times of 1–2 days. SOLUTION: AI chatbot to answer standard questions + automatic ticket categorization. RESULTS: 60% of all inquiries answered automaticall; Response time reduced from 24 hours to 2 hours; Support costs reduced by 30%.
4. HR & Recruiting – Bakery – 140 employees, chain of stores
USE CASE: Applicant screening & automated scheduling. PROBLEM: Many applications for store jobs had to be reviewed manually. Solution: AI filters suitable candidates, analyzes CVs, and automatically suggests interview dates. RESULTS: Screening effort reduced by 75%; Time to fill a position reduced from 28 days to 12 days; Store managers relieved of workload, HR works more strategically.
5. Operations & Logistics – Food wholesaler – 60 employees
USE CASE: AI-supported demand forecasting. PROBLEM: High spoilage rate for fresh goods due to incorrect quantity planning. SOLUTION: Machine learning model analyzes historical sales, weather, holidays, and regional trends. RESULTS: Spoilage rate reduced by 18%; Margin increased by 7%; Higher product availability during peak times.
6. Production – Manufacturing company – 95 employees
USE CASE: Quality control via computer vision. PROBLEM: High reject rate due to human error in visual inspection. SOLUTION: AI camera system scans each component and detects micro-defects in real time. RESULTS: Reject rate reduced by 30%; Inspection speed +140%; ROI in less than 9 months.
7. Finance & Controlling – Cleaning company – 40 employees
USE CASE: Automated document recognition & posting workflow. PROBLEM: Monthly document posting took 2–3 days and led to errors. SOLUTION: AI automatically extracts document data, sorts cost centers, and creates posting records. RESULTS: Time per month-end closing reduced by 70%; Error rate reduced to almost 0; Finance team now has capacity for planning & analysis again.
8. Product development – Manufacturing company – 110 employees
USE CASE: AI-supported prototyping & design variants. PROBLEM: Product designers needed weeks to develop new lighting designs. SOLUTION: AI automatically generates 3D designs based on trends, customer data, and existing models. RESULTS: Development time reduced from 6 weeks to 10 days; 4 new product lines in one year instead of 1; Higher success rate in terms of market acceptance.
9. IT & back office – Advertising agency – 25 employees
USE CASE: Automated project & resource management. PROBLEM: Project planning was done using Excel – often too late to recognize when capacities were running low. SOLUTION: AI predicts project workloads, suggests resource allocations, and generates reports automatically. RESULTS: Project delays reduced by 40%; Significantly better capacity planning; 5 hours less admin time per week per team leader.
10. Management/Strategy – Metal construction company – 80 employees
USE CASE: Predictive analytics for business planning. PROBLEM: Decisions were based on gut feeling, forecasts were inaccurate. SOLUTION: AI dashboard analyzes orders, market prices, delivery times, and competitive trends. RESULTS: Forecast accuracy +25%; Faster decisions in purchasing and sales; Greater transparency for management and investors.
Let's get started and produce your own use cases with strong results?
