AI Strengthens Competitiveness
With the help of artificial intelligence, companies can usefully analyze a large amount of data. Intelligent algorithms show specific patterns and relationships between data that classic tools for business intelligence cannot recognize due to the abundance of data.
Using machine learning, mere algorithms become self-learning systems in the next step. These manage to get better and better based on experience and deliver even more precise analysis results. The use of AI systems has already proven itself in medium-sized companies. Nine percent of the companies surveyed – an increase of two percentage points compared to the previous year – use applications based on AI. The most commonly used solutions are aimed at process automation and optimization. Eighty-three percent of companies rely on technology to save time. Just over three-quarters of those surveyed achieve an improvement in service and product quality through AI and thus strengthen their competitiveness.
Know The Value Of Company Data
Every day more and more digital data accumulates in companies. To leave them unnoticed would be grossly negligent. Without reason, data is considered the oil of the 21st century and essential raw material for innovative, data-driven business models.
In the Digitization Index 2020/2021, you can read how medium-sized companies are already getting the most out of their data with the help of analytics and artificial intelligence.
Use The Opportunities Of Big Data
Several thousand emails, classic business documents such as orders, order confirmations and invoices or information from collaboration platforms: the daily flood of data in small and medium-sized enterprises (SMEs) is enormous, but on the other hand, it also harbors many opportunities. For example, valuable information can be derived to improve customer service or product development.
The majority of medium-sized companies have already recognized that pure spreadsheets quickly reach their limits. Instead, the companies rely on digital technologies to manage, aggregate and evaluate the data. Seventy-six percent of companies already carry out regular data analyses—the goal: to gain new insights for a more individual customer approach. Or find out which new business areas can be opened up in a meaningful way.
Forward-looking analyzes are becoming increasingly important. Paired with artificial intelligence, such methods based on machine learning can make accurate forecasts. This helps medium-sized companies to set the right course for the future today. In this way, decision-makers know about specific trends in customer behavior or their production machines’ future condition and can react early and respond proactively to changes.
A quarter of the companies surveyed are already using the technology, especially in the financial sector. Predictive analyzes support risk management here. Modelling and simulations can determine indicators that make it easier to estimate and assess possible risks. According to the digitization index, there is also great potential for such predictions in other sectors, which every second company will use in the future.
Prepared For The Future With Digital Processes
Digitization is worth it. Medium-sized companies have recognized this and are digitizing more and more processes. This helps them, especially in turbulent times like the corona pandemic. Because digital pioneers, i.e. companies with a high degree of digitization, are more resilient to crises, react more flexibly to changing requirements and implement new business models faster.
Data Analysis As A Guarantee Of Success
Based on the results determined by data analytics, medium-sized companies want to reduce time and costs, improve relationships with their customers or develop groundbreaking innovations. To this end, six out of ten companies regularly analyze their general business data such as customer information, material and product data or data from the IT infrastructure. In the future, there will also be log data from IT systems, sensor data from the Internet of Things and content from social networks.
Also Read: Application Examples Of Machine Learning In Cyber Security
