4 AI trends that will shape digital roadmaps in 2022
In 2020, digitalization experienced enormous prioritization as many companies were no longer able to operate without it. However, the digitized aspects were often basic topics such as virtual communication and home office. For 2021, future topics and innovations will be back on the agenda for many companies. Investments were often postponed, particularly in the AI and data science areas, as they were usually about automating and optimizing processes that were already functioning. So optional in the middle of the crisis. However, the following 4 points are the focus for 2021:
1. Sales optimization
This trend is particularly characterized by the fact that almost every company can use it for their own benefit. Essentially, it's about finding out which customer you have to offer which product at which time via which channel in order to sell as much as possible. As a further step, you can also think about optimizing margins and inventory once the basis is in place. With the exception of companies that generate extremely few sales but in high volumes and have highly complex products (usually B2B solution sales), almost all companies from medium-sized companies up can benefit from sales optimization. Artificial intelligence uses historical and future generated data to make predictions about optimal sales processes and learns from mistakes and feedback.
2. Data management
After basic digital topics such as the introduction of Microsoft Teams or similar were dealt with in 2020, many companies want to put future and innovation topics on the agenda. It often turns out that data quality, recording, warehousing and many other construction sites are insufficiently prepared for future technologies such as artificial intelligence. Accordingly, the first steps for enabling future projects include optimizing data collection processes, automating the exchange of data with partners via interfaces, setting up a central and high-quality data collection point and much more.
3. QA automation
Automated testing has become an integral part of software development and no company that wants to maintain a stable and scalable platform ignores this trend. AI is currently creating many start-ups that automate the generation of these automated tests. Proof-of-concepts are also increasingly being developed in industry and production, which use high-resolution video cameras to automate QA processes that are currently carried out manually by employees.
4. Trustable AI and data science decision making
Due to legal initiatives and various publicly discussed incidents in the past (AI that allegedly makes discriminatory decisions based on origin), the traceability of these complex decision-making processes is increasingly coming to the fore. Especially when an AI human's decision means that insurance, funding or medicine is denied or lives can be at stake in automated driving. However, even in the general development of less polarizing topics, traceability is becoming more and more important in order to discover errors and optimize quality.