4 AI trends that will shape digital roadmaps in 2022

In 2020, digitization experienced a huge prioritization, as many companies were no longer operational without it. However, the digitized aspects were often basic topics such as virtual communication and home office. For 2021, future topics and innovations will again be on the agenda of many companies. Particularly in the AI and data science area, investments were often put on hold because they were usually about automating and optimizing processes that were already working. So in the middle of the crisis optional. However, the following 4 points are in focus for 2021:

1. sales optimization

This trend is characterized in particular by the fact that almost every company can use it for itself. In essence, it is about finding out which customer has to be offered which product at which time via which channel in order to sell as much as possible. In a further step, we can also think in terms of optimizing margins and inventories as soon as 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 the mid-market upwards can benefit from sales optimization. Here, artificial intelligence uses historical and future-generated data to make predictions about optimal sales processes and learns from mistakes and feedback.

2. data budget

After having dealt with basic digital topics such as the introduction of Microsoft Teams or similar in 2020, many companies want to put future and innovation topics on the agenda. It often turns out that data quality, capture, 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, establishing a centralized and high-quality data collection point, and much more.

3. QA automation

It is hard to imagine software development without automated testing and no company that wants to maintain a stable and scalable platform ignores this trend. AI is currently creating many startups that automate the generation of these automated tests. Proof-of-concepts are also increasingly being developed in industry and production, using high-resolution video cameras to automate QA processes that are currently still performed manually by employees.

4. comprehensible AI and data science decision making

Due to legislative initiatives and various publicly discussed incidents in the past (AI allegedly making discriminatory decisions based on origin), the traceability of these complex decision-making processes is increasingly coming to the fore. Especially when an AI person’s decision may deny them insurance, funding, or medicine, or when lives may be at stake in automated driving. However, even in the general development of less polarizing topics, people are increasingly concerned with traceability in order to detect errors and optimize quality.

Author

This article was written by Aleksander Fegel. Aleksander has been managing director at Ailio for over 5 years. He advises our partners and customers on topics related to AI, data science, agility and digitalization. Alex also loves golden retrievers.