What are the basics, tools and solutions for AI in companies?

AI in companies is part of everyday life in many in­dus­tries. However, the tech­no­logy can only achieve the desired results if it’s trained, deployed and monitored correctly. If this is done, companies will benefit massively from ar­ti­fi­cial in­tel­li­gence.

The op­por­tun­it­ies and ad­vant­ages of AI in companies

Ar­ti­fi­cial in­tel­li­gence (AI) is used in companies to…

• Optimise workflows
• Minimise errors
• Save time and money

The tech­no­logy can be used in many areas and can make a valuable con­tri­bu­tion both within the company and when dealing with customers. The biggest advantage of AI in companies is the increase in pro­ductiv­ity. Time-consuming and error-prone tasks in par­tic­u­lar can be automated by using ap­pro­pri­ate AI tools for companies. Ideally, the tech­no­logy delivers optimal results in fractions of a second, allowing human spe­cial­ists to con­cen­trate on other tasks.

AI in companies can identify trends, cor­rel­a­tions, or potential issues at an early stage, enabling them to secure an edge over com­pet­it­ors or prevent drawbacks. With the help of machine learning, AI can be cus­tom­ised to meet the unique needs of a company, de­liv­er­ing tailored solutions for specific chal­lenges. In addition to these tasks and op­por­tun­it­ies, AI also provides a valuable service to the company in ret­ro­spect. Automated and com­pre­hens­ive AI data analyses enable con­tinu­ous mon­it­or­ing of all important steps. Ad­just­ments and op­tim­isa­tions are thus iden­ti­fied and made possible for future projects. The accuracy of the tech­no­logy is already im­press­ive and is in­creas­ing day by day.

What chal­lenges arise from the use of AI in companies?

It’s important to consider the chal­lenges that arise if AI is to take a firm place in the company.

Security and data pro­tec­tion

The biggest chal­lenges lie in the area of security and data pro­tec­tion. Even AI systems are not safe from cy­ber­at­tacks without the necessary pro­tect­ive measures. Since ar­ti­fi­cial in­tel­li­gence may handle sensitive data, ap­pro­pri­ate pre­cau­tions must be taken here.

The right database

AI is only be­ne­fi­cial for your company if it has been pre-trained with extensive and diverse data sets. The more ef­fect­ively the AI learns, the more precise its results will be. It’s crucial that the data is current, accurate, and free from biases, as flawed or in­com­plete data sets can severely affect per­form­ance. Regular data main­ten­ance and updates are vital to guarantee con­sist­ent and reliable outcomes over time.

Human control

Without the necessary control, AI in companies cannot deliver sat­is­fact­ory results. While the tech­no­logy is highly advanced, mistakes can still happen. The results must be me­tic­u­lously reviewed by human experts, who can correct any errors to ensure a con­vin­cing outcome and enable the AI to deliver even more precise results in the future. This is es­pe­cially critical in sensitive fields like medical dia­gnostics or finance, where thorough val­id­a­tion is in­dis­pens­able.

Lack of spe­cial­ist staff

Not all AI tasks can be performed by non-experts, or at least experts from outside the field who are perfectly familiar with their industry but have had no contact with AI in companies. However, spe­cial­ists who train and monitor AI are still in short supply so it can be difficult to find the right pro­fes­sion­als. Companies should therefore invest in the further training of their existing employees or spe­cific­ally promote junior staff to close the gap. Col­lab­or­a­tions with uni­ver­sit­ies and research in­sti­tu­tions can also help to fa­cil­it­ate access to talented pro­fes­sion­als.

Ethical issues

The use of ar­ti­fi­cial in­tel­li­gence, par­tic­u­larly in companies, fre­quently brings up ethical concerns. One key aspect is the need for trans­par­ency in how the tech­no­logy is used. Decision-making processes must always be clearly un­der­stand­able. Ad­di­tion­ally, AI models can still produce incorrect or biased con­clu­sions if the training data is flawed. In the worst cases, this can result in dis­crim­in­at­ory outcomes or im­bal­anced decisions. To address these issues, companies should either prevent them through careful planning or com­mu­nic­ate them openly to maintain trust and ac­count­ab­il­ity.

Legal security

This is par­tic­u­larly important with regard to the last major challenge. As ar­ti­fi­cial in­tel­li­gence is involved, questions of re­spons­ib­il­ity and, ul­ti­mately, liability must be clarified in advance and also com­mu­nic­ated in a com­pre­hens­ible manner.

The most important fields of ap­plic­a­tion of AI solutions for companies

AI is used in numerous companies and improves a large number of work processes. The pos­sib­il­it­ies are almost unlimited and will increase sig­ni­fic­antly in the future. The following examples of AI in companies provide a first im­pres­sion.

• Customer service: Automated feedback analysis and AI chatbots enable customer needs to be met more quickly and ef­fi­ciently.
• Text and image creation: AI tools for companies enable the faster and more cost-effective creation of texts, images and videos - for example for marketing measures, news­let­ters, online presences or other content.
• Meetings: There are programs that record, tran­scribe and summarise video calls. AI can also be used to find ap­point­ments.
• Re­cruit­ing: Re­cruit­ment processes in large companies can be made more efficient for both parties using AI.
• Mon­it­or­ing: AI solutions for companies monitor processes, recognise (potential) sources of error and upcoming trends at an early stage or generally help with the eval­u­ation of campaigns and AI market research.
• Software de­vel­op­ment: When creating new software, databases and code modules can be created and main­tained using AI code gen­er­at­ors.
• Inventory control: The use of AI in companies with stock levels can optimise the entire pro­cure­ment process. The tech­no­logy monitors incoming and outgoing goods, detects impending shortages and ensures better ac­count­ing.
• Man­u­fac­tur­ing and main­ten­ance: AI is used to check products for faults during pro­duc­tion. Cor­res­pond­ing AI solutions for companies can predict machine failures and prevent them with ap­pro­pri­ate main­ten­ance re­com­mend­a­tions.
• Health­care: There are various possible ap­plic­a­tions for ar­ti­fi­cial in­tel­li­gence in the health­care sector. For example, it monitors patient data or makes diagnoses based on X-ray images. Of course, in this case, it’s only used to assist doctors.

What con­di­tions are required?

If you are planning to use AI in your company, you should first invest in the ground­work. Once you’ve created the right con­di­tions, the tech­no­logy can offer real added value to your company. The following steps are essential:

1. Define goals: First think about the specific work steps for which you would like to introduce AI in your company and what results you hope to achieve by using it. This is the only way to find the right solution.
2. Create legal certainty: Establish a binding framework in advance to ensure that issues of re­spons­ib­il­ity and liability are clarified. This applies in par­tic­u­lar to data pro­tec­tion.
3. Train the AI: AI for companies is only as good as the data set it has been trained on. Only with relevant data can the solution learn subtle nuances and deliver better results later on.
4. Monitor results: Make sure you hire pro­fes­sion­als with the necessary expertise to monitor the AI in your company on an ongoing basis. After all, as im­press­ive as the cap­ab­il­it­ies of ar­ti­fi­cial in­tel­li­gence are, it requires human su­per­vi­sion.