AI use in companies: In which areas artificial intelligence is already transforming SMEs

From Theory to Practice: A Structured Look at the Areas of Application

To provide a clear overview, we look at the possible uses of AI across the most important business functions. This makes it clear that artificial intelligence is not an isolated topic of individual departments or the IT department, but a cross-cutting issue that affects virtually all areas. Leading figures from SMEs, research and start-ups regularly discuss how companies are successfully managing this change at data:unplugged — Germany's largest platform for data, AI and digital transformation.

A recent university survey from Karlsruhe shows: To a large or very large extent, AI is currently used most frequently in sales and marketing, at 15 percent. So let's start right where many companies have their first contact with AI technologies.

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Marketing & Communication: Where AI can increase reach and relevance

Marketing faces constant challenges: Which channels really work? Which target groups should be addressed and how? Which content is relevant? The use of AI technology can help in several areas.

Content creation and generative AI

In many companies, content production is completely manual. Generative AI models can speed up this process — not as a finished end product, but as a solid basis for blog articles, social media posts, or newsletters.

Systems such as ChatGPT create texts based on predefined parameters and enable teams to start with a structured design instead of starting from scratch. The time is then invested in strategic thinking and quality control, not in finding the right starting point. Anyone who wants to know how AI creates real impact in communication wants to get first-hand insights at the data:unplugged festival — from SMEs, experts and tool providers who show which technologies are working and how they have sustainably changed communication processes.

Personalization in email marketing

There is enormous potential for personalization in email marketing. Thousands of recipients are currently receiving the same newsletter. AI-powered marketing automation creates personalized content — based on previous behavior, interests, or phase in the customer journey.

The result: Higher opening rates, more clicks, better conversion. The AI systems analyze volumes of data that would hardly be possible to handle manually and ensure relevance for each individual recipient.

Social media monitoring and sentiment analysis

Social media monitoring eats up time when it's done manually. AI tools search social media channels, review portals and forums and analyze the mood about the company or products.

They provide early warning of emerging problems, show topics that concern the target group, and provide a better understanding of how the brand is perceived. The ability of AI to analyze large amounts of data in real time makes this a valuable area of application.

Optimizing advertising budgets

Advertising budgets could be used more efficiently in many companies. AI systems analyze which ads, target groups, and channels deliver the best performance and automatically optimize budget distribution.

Less wasted budget, more qualified leads, better ROI — the algorithms continuously learn from the results and adjust the strategy accordingly.

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Sales: How AI can increase completion rates and efficiency

While well-thought-out marketing attracts the attention of potential customers, sales translates this interest into concrete business transactions. Here too, AI is becoming increasingly important in many areas in order to increase speed and precision.

Lead scoring and prioritization

Lead scoring is a classic use case. Companies receive inquiries every day, but not all are equally promising. AI analyzes which leads are most likely to close based on historical data, website behavior, and demographics.

As a result, sales teams can focus on qualified leads, sales cycles are shorter and closing rates increase. Artificial intelligence recognizes patterns that would be difficult for humans to identify.

Automated quotation preparation

The preparation of offers is often still manual, although many offers are very similar. AI-based systems automatically generate offer templates based on product combinations, customer history and price logic.

This saves time, reduces errors due to manual entry, and teams can send more offers in less time. This area of application of AI shows how routine tasks can be efficiently automated.

Sales forecasting and forecasting

Sales forecasting is often based on good feeling and experience. AI provides more accurate predictions of when and which deals are likely to close. This supports better resource planning, more realistic sales forecasts and early countermeasures if targets are missed.

The AI models take several factors into account at the same time and provide a sound basis for strategic decisions.

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Customer Service: Where AI Shortens Response Times and Increases Quality

In customer service, artificial intelligence enables faster and more efficient processing of inquiries and improves individual customer care. In this way, AI supports communication channels and redesigns service processes.

AI chatbots for initial inquiries

AI chatbots answer standard queries automatically — around the clock, without waiting time. Chatbots can solve most routine inquiries independently and thus massively relieve service teams.

The time saved can be invested in complex cases that require human expertise. It is important to be able to transfer to employees when AI reaches its limits.

Intelligent Routing of Requests

Not every request can or should be answered automatically. AI analyses incoming inquiries and automatically forwards them to the right department or experts. This reduces processing times and increases customer satisfaction.

Real-Time Sentiment Analysis

AI recognizes in customer inquiries whether the person is frustrated, neutral, or satisfied — and adjusts the prioritization accordingly. In critical cases, action is taken immediately, while routine requests are processed in the normal workflow.

You can find out how to trade in other specific examples on the data:unplugged festival. Here, companies share their experiences with AI in customer service and show, in master classes, what works and how in practice.

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Production & Operations: Where AI can improve efficiency and quality

Production and operations are about precision, predictability and cost efficiency — areas where AI systems can be a major relief.

Predictive maintenance in industry

Predictive maintenance is one of the most valuable use cases in manufacturing and industry. Machine failures usually come at an inopportune moment and cost a lot of money. Based on sensor data, AI predicts when maintenance will be necessary — before expensive failures occur.

A case study shows: Companies that rely on predictive maintenance have been able to reduce their downtime by up to 50 percent and at the same time reduce their maintenance costs by 30 percent.

The algorithms detect anomalies in the data that indicate upcoming problems. The result: Fewer unplanned downtimes, longer machine life, easier planning.

Automated Quality Control with Image Analysis

Manual quality controls are time-consuming and prone to errors. Image recognition AIs check products or components in real time and identify deviations that the human eye may miss.

The camera-based systems offer continuity and consistent quality. These results in lower reject rates, higher quality consistency and fewer complaints.

Optimizing production planning

In many companies, there is considerable potential for optimization in production planning. AI systems optimize complex production processes along the entire value chain — from order sequence to machine allocation to demand-based material provision.

The results are measurable: shorter turnaround times, higher capacity utilization and reduced waste. AI algorithms calculate optimal solutions for parallel planning problems that would be very complex for manual processes.

Supply chain management

In supply chain management, AI systems help to predict demands more precisely, to identify optimal order times and to identify delivery risks at an early stage. These results in benefits such as lower inventory costs, fewer shortages and more resilient supply chains.

The use of AI makes it possible to better manage complex global supply chains and react more quickly to disruptions.

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Human Resources: Where AI Supports Recruiting and Personnel Development

The human resources sector is facing a wide range of challenges — from a shortage of skilled workers to efficient recruiting and targeted continuing education. AI can also provide valuable support here.

CV screening and pre-selection

If there are several hundred applications, recruiters lose a lot of time reviewing them. AI systems analysis CVs, compare them with requirement profiles and create an initial pre-selection.

This speeds up the process and allows HR teams to focus on personal discussions with the most promising candidates.

Skill gap analysis

AI analyses existing competencies in the company and identifies gaps compared to future requirements. This helps with targeted personnel development and shows where continuing education measures have the biggest impact.

Automated routine tasks

From vacation management to time recording to payroll — many HR processes still run manually. AI-based systems automate these routine tasks and create capacities for strategic HR work.

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Finance & Controlling: Where AI Speeds Up Processes and Reduces Errors

In the financial sector, precision and efficiency are crucial. AI systems can save time, minimize errors and create a better basis for financial decisions.

Automated invoice processing

Automated invoice processing is a classic use case with a quick return on investment. AI systems automatically collect billing data, compare it with orders, check plausibility and prepare payment approval.

These results in faster payment processes, reduced error rates, optimized use of discounts and more resources for strategic financial issues. The AI systems extract relevant information from a wide variety of document formats — regardless of layout or format

Fraud Detection and Anomaly Detection

When detecting fraud, AI analyses patterns and immediately reports anomalies. Unusual transactions, potential cases of fraud or errors in cash flows are often only noticed late — the AI protects against financial losses and compliance violations. The algorithms detect deviations from normal patterns that could indicate problems.

Cash flow forecasts

Cash flow forecasts become more accurate when AI takes multiple factors into account — from payment terms to seasonal fluctuations to economic indicators. This helps with better liquidity planning and more well-founded investment decisions.

The AI models can also incorporate external data sources and run through complex scenarios.

Automated reporting

Reports and Analyses tie up resources when monthly and quarterly reports are created manually. AI-based tools automatically generate reports, visualize key figures and explain discrepancies.

This guarantees more time for analyses instead of having to collect data, and enables faster decisions. The systems can also show relevant relationships between different KPIs.

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IT & Cybersecurity: Where AI systems can protect and optimize

A functioning IT infrastructure forms the basis for all areas of application mentioned so far. The IT infrastructure must run stably, securely and efficiently — and this is where the use of AI in companies is one of the most critical areas of application.

Proactive system monitoring

Network and system monitoring is becoming proactive rather than reactive through AI. The systems monitor the IT infrastructure, recognize unusual patterns and warn before real problems occur.

These results in higher availability and less downtime. Artificial intelligence learns what normal operating patterns are and when deviations become alarming.

Cybersecurity and threat detection

Cybersecurity is particularly benefiting from AI because attacks are becoming increasingly sophisticated. AI systems identify threats by analyzing behavioral patterns, anomalies, and known attack patterns — often faster and more reliably than traditional security tools.

This protects against data loss, ransomware, reputation damage and financial losses. At the same time, it should be noted that AI technologies are also available to attackers. Data protection and IT security are therefore becoming even more important.

IT Support Automation

IT support is often flooded with standard questions. AI-based support systems automatically solve common IT problems or provide suggested solutions. This saves support resources and reduces problem resolution time.

Similar to chatbots in customer service, these systems can process many inquiries independently.

Code quality in software development

In software development, AI tools carry out code reviews, identify bugs, uncover security gaps or even make code suggestions. These results in higher code quality, faster development and reduction of technical errors.

The AI technologies analyze code patterns and compare them with best practices from a large base of examples.

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Strategy & Business Intelligence: How AI can substantiate decisions

Now that we've looked at the operational and supporting areas, perhaps the most important question remains: How can AI help with strategically important decisions? This is where the circle comes full circle — because well-founded strategic decisions have an impact on all areas mentioned so far.

Market and competition analysis

Market and competition analysis is becoming more comprehensive and up-to-date thanks to AI. The systems analyze large amounts of market data, news, social media signals, and competitive information from various industries.

This enables early identification of trends, a better understanding of the competitive landscape and well-thought-out strategy decisions. The ability of AI to extract relevant information from various sources is particularly valuable here.

Accurate customer segmentation

Customer segmentation becomes more precise when AI performs complex analyses — based on behavior, preferences, purchase history, and numerous other factors. This enables more targeted customer contact, higher conversion rates and data-based product development. The algorithms identify customer segments that would remain hidden using traditional methods.

Scenario planning and simulation

Scenario planning and simulation helps to better manage uncertainties. AI plays out various scenarios and simulates the effects on day-to-day business.

This supports strategic decisions, risk assessments and preparation for uncertainties. The systems can take into account complex interactions between various factors.

KPI analysis and performance tracking

Performance tracking and KPI analysis are becoming more meaningful thanks to AI. The systems uncover complex relationships between KPIs and explain which factors really drive performance.

A better understanding of the business, more targeted measures and a higher impact are the benefits. Artificial intelligence can also uncover unexpected connections and thus open up new potential for the company.

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From overview to implementation: The path to your own AI strategy

After this comprehensive overview of the various areas of application, it is clear that AI is now penetrating almost all corporate functions. From customer-oriented areas such as marketing and service to operational processes in production to supporting functions such as HR and finance — AI systems are becoming increasingly established in all areas of business.

AI has the greatest potential where repetitive tasks, large amounts of data and complex decision-making processes come together. A focused start in a selected area can already achieve measurable improvements.

SMEs expect a return on AI investments of a factor of four within one year. Expectations are high — and the technology is generally available. It is now a matter of strategic implementation.

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Conclusion: AI use in companies as a cross-cutting issue

The use of AI in companies has been around for a long time — even in medium-sized companies. In virtually every functional area, there are specific areas of application in which artificial intelligence works productively and delivers measurable results: in marketing and customer service, in production, as well as finance and IT. The focus everywhere is on increasing efficiency. By automating routine tasks and analyzing large amounts of data, you can make faster and more precise decisions that significantly move your business forward.

Targeted implementation in individual areas of the company is already bringing initial success and paving the way for sustainable optimization. From our experience in working with decision makers, we know that AI projects can be successfully implemented with the right strategy and competent support. The technology is available and gives you a wide range of opportunities to improve processes and future-proof your company.

You can find out how AI is being used by other SMEs at data:unplugged 2026 on March 26 & 27 in Münster. Here, companies from the areas of e-commerce, industry, trade, production and logistics share their self-implemented use cases: from finance to marketing, from IT to legal. At Mittelstandtage and a further four stages, we create space for honest exchange about real practical examples and the courage not only to understand AI, but to actively shape it.

For effective AI implementation, it is crucial to involve, train and positively prepare all areas of your company for deployment. data:unplugged stands for a broad and well-founded transfer of knowledge — from which entire business teams benefit. Get tickets for yourself and your team now!