Wanted:
AI for medium-sized companies

For over 15 years, I’ve been working on advancing digitalisation and automation in the economy. Companies across nearly every sector are facing significant challenges. Increasing competition is driving the need for ever more complex service offerings, yet resources are becoming scarcer – less staff, fewer raw materials, less capital. Demo-graphic shifts are intensifying the strain on corporate structures, affecting nearly every department, from production processes and administrative tasks to recruitment. The results:

Boomers Retire – and Their Expertise Does Too

Many companies rely on Boomer-generation employees in their administrative departments, and as they near retirement, firms risk losing not only manpower but also decades of valuable expertise. This knowledge is essential for maintaining daily quality standards and training new employees. As a result, tasks like data entry, order processing in ERP systems, or basic customer management take longer, slowing down workflows in customer service, which can lead to dissatisfaction and even loss of clients.

Lack of Young Talent

So, what’s the solution? Recruiting skilled talent, both domestically and internationally, is becoming increasingly difficult. This may be because the company’s location is not attractive enough for young talent, or because its core business does not allow for flexible working hours or remote work – two factors highly valued by Gen Z. Additionally, today’s graduates are understandably not keen on performing repetitive tasks.

Rising Workloads

On top of this, many recurring processes within businesses are still not adequately standardised, let alone digitised. When special cases arise (e.g. enquiries, complaints, custom orders), which often result from insufficient standardisation, manual intervention is required. This adds pressure on employees and results in significant delays and inefficiencies. When the expertise within specific departments is concentrated in just a few key individuals, these people become bottlenecks, leading to increased stress, excessive workloads, and unnecessary dependencies.

This is particularly evident in HR and legal departments. Companies with hundreds or thousands of employees must deal with new labour regulations and legal queries daily. Despite largely standardised processes, handling these tasks requires highly skilled staff with up-to-date legal knowledge – for repetitive tasks that can quickly become a bottleneck.

"Digital Debt" Accumulates

Even when companies are finally ready to adopt new technologies, outdated structures often get in the way: a lack of data, missing interfaces, fragmented IT architectures. The feeling of having to "catch up on homework" can dampen the excitement for change. But the longer you wait, the further ahead the competition gets, and the mountain of homework grows. A proven antidote to this paralysis: just get started. But don’t jump in blindly. Using AI as a buzzword isn't enough. A well-thought-out plan and a scalable IT architecture are essential. The better aligned the foundation is with your company’s goals, the more long-term, efficient, and scalable the results will be.

Conclusion

Regardless of industry, companies face these common challenges:

• Retaining and quickly accessing in-house knowledge
• A shortage of skilled labour
• Automating repetitive tasks
• Fragmented, outdated IT infrastructures

The good news: AI can address all these issues and significantly ease workloads. How? Through knowledge management, process automation, and complex data processing. Precisely:

• AI preserves valuable knowledge within the company and makes it accessible for all necessary purposes. Imagine taking all the knowledge that currently exists in people’s heads or in scattered documents and consolidating it into a centralised knowledge repository. Data from various sources is read, analysed, and retrieved automatically in milliseconds. Complex, time-consuming research or review processes are standardised and seamlessly integrated into new, highly efficient workflows.
• AI performs repetitive tasks, both simple and complex, faster and more accurately than humans. For example, the automatic reading and transferring of numerical and text values from business documents into ERP systems can be fully standardised with AI.
• AI takes over repetitive, non-value-adding tasks, freeing up staff to focus on more meaningful work. For example, AI can analyse, evaluate, and select text documents or calculate optimal machine utilisation in production. The possibilities can be tailored to each company’s specific needs.

In short: AI enhances efficiency in almost every area relevant to SMEs. However, the key is to use AI not as an end in itself, but in a strategic, purposeful way. The use case must be built around the company’s specific needs and data. While this requires an upfront investment, it’s one that pays off. The savings in manual resources, time, and costs are immense.

In my 15 years of experience, I’ve translated the needs of various companies, industries, and markets into a wide range of AI projects and applications. One of the great benefits of overseeing projects from start to finish is learning how change processes truly take root and what’s needed for AI implementation to deliver the desired outcomes.

Over the years, I’ve developed a few “favourites” – projects I’ve found to be particularly impactful. Not only do they make a significant difference, but I’ve also seen firsthand how they consistently deliver tangible results and solutions in different areas.

1. Knowledge Management

Relevant for almost any company of a certain size, knowledge management tools powered by AI are incredibly efficient. AI language models enable the automated analysis of large amounts of text. By reading, understanding, and tagging historical company data, businesses can build vast knowledge databases, making them accessible to all users via semantic search. The entire body of a company’s expertise is digitised and made available to clients and/or employees as needed. This is particularly in demand for customer service applications.

Example from the construction industry:

Construction projects are becoming increasingly complex. Whether it's a new building, insulation, or sustainable modernisation, specific DIN standards and industry regulations must be considered based on the project. This is especially challenging for construction projects. Construction firms and tradespeople use knowledge databases to automatically pull up the correct standards during project planning. This saves a lot of time, reduces costs, and delivers fast, error-free, and reliable results – benefiting clients, employees, and the company’s bottom line.

2. Price Forecasting

Cost estimates, value calculations, pricing – when it comes to working out monetary values, numerous factors come into play. Researching all these elements, balancing them correctly, and calculating them accurately takes time, expertise, and personnel. What takes humans days to accomplish, AI can do quickly and reliably at the push of a button.

Example from the utility sector:

How do you forecast the efficiency and cost of installing a heat pump? Regional weather patterns, geographic conditions, and building data all impact the economic feasibility of such a project. AI can aggregate weather data, satellite images, building insulation data, and material prices to calculate a cost-benefit analysis – faster and more accurately than humans ever could.

3. Predictive Maintenance

Process optimisation is essentially AI’s core competency, and it’s highly sought after in manufacturing industries. AI applications use algorithms to turn data into actionable insights on how companies can make various functions and business processes more efficient. This, in turn, allows for predictive maintenance scheduling – not just for machines, but also for services and staff.

Example from an airport:

Intelligently analysed data can help optimise resource use almost anywhere. Take airport cleaning staff and equipment, for example: When should specific areas be cleaned? By using data on passenger volume, flight schedules, and floor space, AI can generate highly efficient cleaning schedules. Just one example of how AI can literally help keep things running smoothly.

From climate protection to heavy industry, in every sector we’re seeing more tasks being supported or even taken over by self-learning digital systems to save resources. When, how, and where AI should be applied varies from company to company. Business model, structure, employment contracts, capital, long-term strategy – all these factors influence the profitability of AI applications.

But that’s the exciting part: AI is not one-size-fits-all. It can be customised to the specific needs of any business. I firmly believe that every company can benefit from the efficiency boost provided by AI. Every single one.

The key is aligning AI’s technological potential with a company’s specific conditions, strategic goals, and industry context. One thing, however, remains constant: doing nothing means falling behind the competition that uses AI. On the other hand, adopting AI now creates new opportunities. Those who start now will enter the race with a significant head start – a race that has already begun.