How can companies use AI suc­cess­ful­ly?

AI in companies is already part of everyday op­er­a­tions in many in­dus­tries. However, the tech­nol­o­gy can only deliver the desired results when it is properly trained, im­ple­ment­ed, and monitored. When these con­di­tions are met, companies can benefit sig­nif­i­cant­ly from ar­ti­fi­cial in­tel­li­gence.

The op­por­tu­ni­ties and benefits of AI in companies

Ar­ti­fi­cial in­tel­li­gence (AI) is used in companies to…

• optimize workflows,
• automate processes,
• minimize errors,
• support employees,
• operate in a more time- and cost-efficient way.

The tech­nol­o­gy can be used in many areas and make a valuable con­tri­bu­tion both in­ter­nal­ly and in in­ter­ac­tions with customers. One of the biggest ad­van­tages of AI in companies is the increase in pro­duc­tiv­i­ty. Time-consuming and error-prone tasks, in par­tic­u­lar, can be automated with the help of ap­pro­pri­ate AI tools for busi­ness­es. Ideally, the tech­nol­o­gy delivers results within seconds, allowing human experts to focus on more complex or strategic tasks.

AI in companies can also detect trends, cor­re­la­tions, or potential problems at an early stage, creating com­pet­i­tive ad­van­tages for busi­ness­es or helping them avoid dis­ad­van­tages. Through machine learning, AI in business can be trained and adapted to provide tailored solutions for specific chal­lenges.

Beyond these ap­pli­ca­tions, AI also provides sig­nif­i­cant benefits after processes have been im­ple­ment­ed. With automated and extensive AI data analysis, con­tin­u­ous mon­i­tor­ing of key processes becomes possible. This enables companies to identify op­por­tu­ni­ties for ad­just­ments and im­prove­ments in future projects. The accuracy of modern AI systems is already high and continues to improve as new training data, models, and tech­nolo­gies are developed.

What are the chal­lenges of im­ple­ment­ing AI in companies?

The use of ar­ti­fi­cial in­tel­li­gence offers many op­por­tu­ni­ties for companies, but it also in­tro­duces new re­quire­ments and risks. For AI in companies to be used reliably, securely, and in com­pli­ance with the law in day-to-day op­er­a­tions, potential chal­lenges must be iden­ti­fied and addressed at an early stage. In addition to technical con­sid­er­a­tions, data pro­tec­tion, legal frame­works, ethical aspects, and the avail­abil­i­ty of qualified pro­fes­sion­als play an important role. The following points highlight the most common chal­lenges as­so­ci­at­ed with the use of AI in busi­ness­es and what companies should consider when im­ple­ment­ing AI solutions.

Security and data pro­tec­tion

One of the biggest chal­lenges in the use of AI in companies is IT security and data pro­tec­tion. AI systems can become targets of cy­ber­at­tacks. At the same time, they often process sensitive in­for­ma­tion such as customer data, internal documents, ap­pli­ca­tion materials, or support requests. Companies should therefore clearly define which data may be entered into an AI tool and how this data is protected.

Technical and or­ga­ni­za­tion­al measures such as access controls, en­cryp­tion, logging and mon­i­tor­ing, as well as clear internal policies and training are essential to prevent con­fi­den­tial in­for­ma­tion from being shared un­in­ten­tion­al­ly. From a com­pli­ance per­spec­tive, companies must also ensure that the use of AI follows ap­plic­a­ble data pro­tec­tion laws and industry reg­u­la­tions in their region. For example, or­ga­ni­za­tions operating in the European Union must comply with the GDPR.

Gov­ern­ments around the world are in­creas­ing­ly in­tro­duc­ing rules for ar­ti­fi­cial in­tel­li­gence. In the European Union, the EU AI Act adopted in 2024 sets binding re­quire­ments and clas­si­fies AI systems according to different risk levels. Other regions, such as the United States, are also de­vel­op­ing reg­u­la­to­ry frame­works and guide­lines for the re­spon­si­ble use of AI. The goal of these ini­tia­tives is to ensure that AI systems are used safely, trans­par­ent­ly, and re­spon­si­bly.

The right data foun­da­tion

AI in companies is only ben­e­fi­cial when the systems are trained with large, high-quality, and com­pre­hen­sive datasets. AI systems learn from existing in­for­ma­tion such as customer, sales, pro­duc­tion, or service data and use it to identify patterns, generate forecasts, or provide rec­om­men­da­tions for action. If the un­der­ly­ing data is in­com­plete, outdated, or incorrect, these weak­ness­es will directly affect the quality of the results.

Companies should therefore invest early in struc­tured data prepa­ra­tion and main­te­nance. This includes clearly defined re­spon­si­bil­i­ties, regular quality checks, and processes for updating and expanding data in­ven­to­ries. Only with this foun­da­tion can AI tools for busi­ness­es deliver reliable results over the long term and support well-informed decisions in day-to-day op­er­a­tions.

Human oversight

Without adequate human oversight, AI in companies cannot produce de­pend­able results. While the tech­nol­o­gy is already highly capable, mistakes can still occur. Human experts therefore need to review AI-generated outputs, evaluate the results, and correct potential errors. This process helps ensure reliable outcomes and allows AI in business to improve over time. In sensitive fields such as medical di­ag­nos­tics or finance, careful human su­per­vi­sion is es­pe­cial­ly important.

Lack of qualified personnel

Not all AI tasks can be im­ple­ment­ed without spe­cial­ized know-how. Even if employees know their own processes and industry very well, they often lack the expertise to select, integrate, and operate AI systems ef­fec­tive­ly over the long term. At the same time, pro­fes­sion­als who can train, monitor, and further develop AI solutions for busi­ness­es are still in short supply in many places. Finding suitable profiles is therefore often a challenge.

To address this gap, companies should invest in targeted training and build internal expertise. It can also be helpful to support junior talent and create new roles, for example in data quality man­age­ment or AI gov­er­nance. Part­ner­ships with uni­ver­si­ties, research in­sti­tu­tions, or spe­cial­ized service providers can further help bring expertise into the or­ga­ni­za­tion more quickly and improve access to qualified talent.

Ethical questions

The use of AI in companies also raises ethical questions. Trans­paren­cy is a key concern because users and affected in­di­vid­u­als should be able to recognize when AI is being used and un­der­stand the basis on which rec­om­men­da­tions or decisions are made. In sensitive ap­pli­ca­tions, it is es­pe­cial­ly important that results remain ex­plain­able and that re­spon­si­bil­i­ty continues to lie with humans rather than the AI.

AI models can also deliver biased or in­ac­cu­rate results when they are trained on un­suit­able or un­bal­anced datasets. This may dis­ad­van­tage certain groups or introduce hidden dis­tor­tions into processes such as re­cruit­ing, customer com­mu­ni­ca­tion, or risk as­sess­ment. To minimize these risks, companies should take action early by es­tab­lish­ing clear guide­lines, regularly testing AI systems for bias, main­tain­ing high data quality standards, and ensuring con­tin­u­ous human oversight.

Legal certainty

In addition to technical and or­ga­ni­za­tion­al issues, legal certainty also plays an important role. Before im­ple­ment­ing AI in companies, or­ga­ni­za­tions should clearly define re­spon­si­bil­i­ties, es­pe­cial­ly when AI systems support decision-making or automate processes. This includes trans­par­ent roles, internal approval and control pro­ce­dures, and clear rules that specify when human in­ter­ven­tion is required.

Another key aspect is liability. Companies must consider what happens if AI provides incorrect rec­om­men­da­tions, processes data in­ac­cu­rate­ly, or causes damage as a result. To reduce these risks, or­ga­ni­za­tions should review planned AI use cases from a legal per­spec­tive and establish ap­pro­pri­ate con­trac­tu­al agree­ments with tech­nol­o­gy providers.

Key areas where AI solutions for busi­ness­es are used

AI is already used in many companies to improve a wide range of work processes. The potential ap­pli­ca­tions are broad and will continue to expand as the tech­nol­o­gy evolves. The following examples show some of the most common areas where AI in companies can provide valuable support.

• Customer service: Automated feedback analysis, AI chatbots and smart AI phone as­sis­tants can help meet customer needs faster and more ef­fi­cient­ly.
• Text and image creation In­tel­li­gent AI as­sis­tants make it possible to create texts, images, and videos faster and more ef­fi­cient­ly. Companies can use them, for example, for marketing ac­tiv­i­ties, newslet­ters, websites, or other types of content.
• Meetings: There are programs that record video calls, tran­scribe them, and create summaries. AI can also be used to help schedule ap­point­ments.
• Re­cruit­ing: In large companies, re­cruit­ing processes can be made more efficient and time-saving for both sides through the use of AI in business.
• Mon­i­tor­ing: AI solutions for busi­ness­es monitor processes, detect (potential) sources of errors and emerging trends at an early stage, or generally help with eval­u­at­ing campaigns and AI market research.
• Software de­vel­op­ment: When creating new software, databases and code modules can be created and main­tained with the help of AI code gen­er­a­tors.
• Inventory man­age­ment AI can help companies with physical inventory stream­line their pro­cure­ment and stock man­age­ment processes. The tech­nol­o­gy tracks incoming and outgoing goods, iden­ti­fies potential shortages early, and improves the accuracy of inventory records.
• Man­u­fac­tur­ing and main­te­nance In pro­duc­tion en­vi­ron­ments, AI can be used to detect product defects during man­u­fac­tur­ing. In addition, AI solutions for busi­ness­es can predict potential machine failures and recommend pre­ven­tive main­te­nance to reduce downtime.
• Health­care Ar­ti­fi­cial in­tel­li­gence is also used in health­care in various ways. For example, it can monitor patient data or assist physi­cians in analyzing X-rays and other medical imaging data. In these cases, AI functions as a sup­port­ing tool that helps doctors make informed decisions.

What pre­req­ui­sites need to be in place?

If you plan to implement AI in companies, it is important to prepare carefully be­fore­hand. Once the right con­di­tions are in place, the tech­nol­o­gy can create real value for your business. The following steps are essential:

1. Define goals: Start by iden­ti­fy­ing which processes or tasks should be supported by AI and what results you expect to achieve. Clear ob­jec­tives make it easier to choose the right solution.

2. Ensure legal com­pli­ance: Establish clear rules and re­spon­si­bil­i­ties in advance to clarify issues such as ac­count­abil­i­ty and liability. Data pro­tec­tion re­quire­ments should be a central part of this framework.

3. Train the AI: AI in companies is only as effective as the data it is trained on. High-quality and relevant datasets enable the system to learn important patterns and deliver reliable results over time.

4. Monitor results: Ensure that qualified pro­fes­sion­als con­tin­u­ous­ly monitor and evaluate the per­for­mance of AI systems. Even highly advanced AI tech­nolo­gies require ongoing human oversight to maintain accuracy and re­li­a­bil­i­ty.