Artificial intelligence for companies

What does artificial intelligence mean for companies?

Artificial intelligence for companies refers to the targeted use of AI technologies to automate business processes, improve decisions and tap into new value creation potential. AI is no longer a topic that only concerns technology companies or research institutions. Medium-sized companies, industrial companies and service providers in all sectors are now using AI in a targeted manner to work more efficiently, reduce costs and secure competitive advantages.

The term artificial intelligence covers a broad spectrum of methods and technologies: machine learning, deep learning, natural language processing, computer vision and generative AI are just a few of them. What they have in common is the ability to learn from data, recognize patterns and make predictions on this basis or take on tasks that previously required human judgment.

prodot has been developing customized software solutions for SMEs and corporations for over 25 years. AI has become a central component of our service portfolio. With more than 80 IT experts and a modern, AI-ready technology stack, we support companies in using artificial intelligence not as an experiment, but as a productive component of their digital infrastructure.

Why artificial intelligence is crucial for companies today

The pressure to become more efficient is increasing in almost all industries. At the same time, the amount of data that companies generate every day is growing faster than the ability to analyze this data manually. This is precisely where the potential of artificial intelligence lies: it processes large amounts of data in real time, recognizes correlations that would be invisible to humans and derives usable insights from them.

Companies that use artificial intelligence consistently benefit in several areas:

• Routine tasks are automated and employees can concentrate on value-adding activities

• Decisions are made on a better, data-based foundation

• Risks are identified at an early stage before they become problems

• Customers are addressed more individually and receive better support

• New business models are created that would not be feasible without AI

The decisive difference between companies that use AI successfully and those that fail rarely lies in the technology itself. It lies in whether AI is consistently aligned with specific business objectives and whether the resulting solutions are integrated into the day-to-day work of the people who are to work with them.

AI strategy: the right start is crucial

Many companies start AI projects with high expectations and fail because they think too big or start from the wrong place. A well thought-out AI strategy is therefore the most important prerequisite for the successful use of artificial intelligence in a company.

A good AI strategy answers the following questions. Which business problems should AI solve? Not every problem is suitable for AI. The most promising use cases are those where large amounts of data are available, patterns recur and the effort required for manual processing is high. What data is available? AI models are only as good as the data on which they are trained. An honest inventory of the existing database is therefore essential. How is AI integrated into existing systems? AI that works in isolation but is not embedded in daily work processes does not generate any added value. How is success measured? Clear KPIs ensure that AI investments can be evaluated and further developed.

prodot supports companies in developing a practical AI strategy: realistic, geared towards specific business goals and with a clear roadmap for implementation.

Fields of application for artificial intelligence in companies

Artificial intelligence for companies is not an abstract concept. It unfolds its added value in specific fields of application that are relevant across all industries and company divisions.

Predictive maintenance and condition monitoring. In production and plant operation, AI models analyze continuous sensor data and calculate failure probabilities for components and machines. Maintenance measures are planned as required, unplanned downtimes are avoided and maintenance costs are measurably reduced.

Intelligent process automation. Recurring, rule-based tasks such as processing incoming invoices, classifying customer inquiries or checking documents are fully automated using AI. Employees are relieved of routine activities and can concentrate on more complex tasks.

Predictive analytics and demand forecasts. AI models analyze historical sales, production and market data and provide precise predictions for demand trends, stock requirements and resource planning. Overstocks and bottlenecks are reduced at the same time.

Natural language processing. Texts, customer reviews, support requests, contracts and other documents are automatically analyzed, classified and summarized. Customer service teams are relieved, processing times are reduced and the quality of responses increases.

Computer vision. AI-supported image processing takes over tasks such as automatic quality control in production, the detection of defects in components or the evaluation of camera images in security and monitoring applications.

Anomaly detection. In financial, production or IT processes, AI models recognize unusual patterns in real time and automatically trigger alarms or follow-up processes. Fraud, quality deviations or security incidents are identified at an early stage.

Generative AI: new opportunities for companies

Generative AI, i.e. AI systems that generate text, images, code or other content independently, has opened up a new dimension of AI use in companies in recent years. Large language models such as ChatGPT, Claude and Gemini are just the best-known representatives of a growing field of technology.

For companies, this results in concrete application possibilities:

• Internal knowledge databases become much more accessible thanks to AI-supported search and summarization

• Employees receive AI assistants that provide contextualized answers to technical questions and support the creation of documents, reports or emails

• Customer service bots answer queries at a qualitatively new level because they understand natural language and respond contextually

• Code is automatically generated, checked and documented, which speeds up development cycles

• Product descriptions, marketing texts and other recurring content are created more efficiently

Using generative AI responsibly

As the opportunities grow, so do the risks. Companies that use generative AI uncritically can quickly get into trouble. Every company should answer three questions before using AI systems productively.

What happens to the data? Many generative AI services process entered data on external servers. Anyone who enters sensitive business data, customer data or internal documents into public AI systems risks losing data control and possibly violating the GDPR. Careful examination of data protection regulations and the use of private, company-owned AI instances are therefore not optional measures, but mandatory.

What happens if models are used incorrectly? AI models produce plausible-sounding results, even if they are wrong. This phenomenon, known as hallucination, can cause considerable damage in companies: incorrect information in customer responses, faulty analyses as a basis for decision-making or faulty code in productive systems. Without clear processes for human review of AI results, risks arise that quickly outweigh the benefits.

What happens if too much control is relinquished? Amazon's automated recruiting system, which systematically downgraded applications from women, is one of the best-known examples of what happens when AI systems make decisions without sufficient human oversight. AI learns from historical data and also reproduces historical errors, biases and prejudices. Those who allow AI to make blind decisions not only relinquish control, but also assume responsibility for decisions that they can no longer fully comprehend.

The crucial point is therefore not to use generative AI as a toy, but also not as an autonomous decision-maker, but as a productive tool with clear guidelines. prodot develops individual AI solutions that are safe, comprehensible and tailored to the specific requirements of a company. We also advise on how to introduce, monitor and further develop AI systems responsibly so that companies can benefit from the advantages of AI without losing control of their processes and data.

AI integration: Seamlessly embedded in existing systems

Artificial intelligence only unfolds its full value when it does not function in isolation, but is deeply integrated into a company's existing IT landscape. An AI model that calculates predictions but does not automatically transfer them to SAP, the CRM or the ticket system generates additional work instead of efficiency.

Several factors play a central role in the integration of AI into company systems. Data availability is the basic prerequisite: AI models need access to the relevant data from ERP, MES, IoT platforms or other source systems. Interfaces and APIs ensure that AI results automatically flow back into the systems in which employees work on a daily basis. Security and data protection must be considered from the outset, especially when personal data is processed. Explainability is becoming increasingly important: decision-makers must be able to understand the basis on which an AI model makes a recommendation, especially in regulated industries.

prodot develops AI solutions that are designed for integration from the outset. Our technology stack adapts to our customers' system landscape and includes Machine Learning, Azure AI Services and Azure OpenAI Service, so that AI functions are embedded into existing system landscapes in a scalable, secure and seamless way.

Modular AI solutions: Step by step to an AI-ready organization

Getting started with artificial intelligence does not have to begin with a major transformation project. A modular approach makes it possible to start with a clearly defined use case, achieve measurable results quickly and build on this foundation step by step.

A typical introduction to artificial intelligence for companies could look like this

• Identification of a specific use case with high automation potential and an existing database

• Development of an initial AI model or pilot project that is used productively in a defined area

• Measuring the results using clear KPIs and deriving optimization measures

• Gradual expansion to other use cases, processes or areas of the company

• Establishment of a central AI infrastructure that can efficiently accommodate new models and applications

This approach keeps the investment risk manageable, delivers visible results early on and makes it possible to build organizational trust in AI before major investments are made.

AI in various industries: Relevance across all industries

Artificial intelligence for companies is not a topic for a specific industry. It adds value wherever data is available, processes are repeated and decisions can be made on a better basis.

In industry and production, the focus is on predictive maintenance: machine statuses are continuously monitored, failures are predicted and maintenance measures are planned as required. In logistics and transport, AI models optimize routes, forecast demand patterns and detect anomalies in supply chains at an early stage. In retail and e-commerce, AI systems personalize product recommendations, optimize pricing strategies and analyse customer behaviour in real time. In the financial and insurance sector, AI models are used for fraud detection, automatic document verification and risk assessment. In the healthcare sector, AI solutions support the evaluation of medical data, the optimization of care processes and personalized patient communication.