4 Opportunities for Value Creation with AI in Industry

How can AI help your company be more successful? For anyone looking for answers, we have summarized four interrelated opportunities for achieving tangible added value with AI in a compact overview. Every AI application increases the added value for your company. Read on to find out what these four specific AI value creation stages are and what added value you can expect.

Stage 1: AI recognition

The starting point for any AI project is an AI-compatible database. In an industrial context, this database is usually generated by one or more monitoring systems. The database contains all past and current information on hardware, software, process, and operating states. Intelligent evaluation and visualization of this data creates transparency and keeps responsible employees up to date with objective information.

A recognition AI can be based on such a database and discover common patterns or deviations from common patterns, known as anomalies. The big advantage of AI over static testing of tolerance ranges is that AI does not require a predefined set of rules. It finds these rules independently and thus also recognizes the many exceptional cases that cannot be formulated as rules manually, or only inadequately. The bottom line is that detection AI offers significantly higher reliability in detecting irregularities that could potentially lead to problems.

Stage 2: AI diagnosis

The next step in the value chain is the data-based diagnosis of problems that have occurred. The aim here is to quickly and reliably find the causes of a problem that has occurred and to remedy it permanently. For human experts, this process is often very time-consuming, as there are many possible causes of problems and a large amount of monitoring data has to be analyzed.

Diagnostic AI, on the other hand, can automatically sift through enormous amounts of data, discover possible causes of problems, and even prioritize them according to their probability of influence. With a suitable presentation of the AI diagnostic results, human experts are then able to quickly and easily identify the causal irregularities. Diagnostic AI not only saves a lot of time in root cause analysis, but may also discover causes that humans alone would not have found.

Stage 3: AI prediction

The next stage of value creation goes far beyond the previous stages, which are sometimes already implemented manually. In stage 3, predictive AI links the irregularities detected by detection AI with the problems and causes diagnosed in stage 2. Trained on a sufficient amount of historical data, predictive AI acquires the almost magical ability to foresee impending problems before they actually occur. And in conjunction with the findings of diagnostic AI, the presumed causes of problems can be identified and eliminated in advance. 

AI predictions can be used to prevent machine failures, proactively reorder spare parts, or rule out bottlenecks at an early stage. Every problem that is avoided saves time, money, and stress that would otherwise have been required to fix the problem. AI at level 3 thus contributes significantly to conserving resources and enables smooth operational processes.

Level 4: AI control

At value creation level 4, AI performs operational tasks independently for the first time. Control AI proactively makes adjustments to hardware, software, or process variables to optimize operational processes according to specified target criteria. The control AI has access to the information and insights from detection, diagnosis, and prediction AI, as well as past control decisions and results. 

The added value of autonomous AI control is obvious. The AI reacts in real time to emerging problems and continuously ensures smooth and highly efficient operations. It is able to keep an eye on all operational aspects for which data is available at the same time. Efficiency gains can be directly translated into higher productivity, lower costs, or less waste.

4 AI value creation stages for your success

AI opens up numerous opportunities to increase the productivity and efficiency of your daily operations. The higher the value creation level of the AI used, the greater the return on investment you can expect from your AI project. However, to ensure that the calculation really pays off, you should pursue a suitable data strategy and have access to the necessary expertise.