There is no doubt that 2020 will be one of the most tumultuous years in recent history. Technology has been a driving factor in supporting as much working from home as possible, keeping in touch with loved ones and keeping businesses running.
AI played an important part in this much-needed digitization boost and not only allowed for further automation but also played a crucial role in gaining insights into the evolution of the epidemic and was even used in the diagnosis of patients. The number of jobs and use cases with AI at their core is still on the rise: so let’s take a look at the most important AI trends for 2021.
1. More investments in AI-driven decision making and forecasting
The World Economic Forum (WEF) estimates that the COVID-19 pandemic is likely to cost between 8.1 and 15.8 trillion U.S. dollars worldwide.
This crisis once again reminds us of how strong everything is intertwined. One small mistake can mean a disaster on the other side of the world. The need for smart, timely and AI-driven decision-making cannot be greater than it is now. Through the use of Machine Learning and AI, forecasts can accurately predict various sets of data. The current trend of investing in AI-driven decision making and forecasting is expected to grow even stronger in 2021.
Some aspects of AI-driven decision making will be even more prominent, for example:
- Fraud detection
- Medical apps for early detection and diagnosis
(e.g. please cough in your smartphone)
- Virtual personal assistants (e.g. Alexa, Siri)
- Price optimization
- Stock optimization
- Personnel planning (working from home)
Identifying and making smart use of these data streams will enable companies to make better decisions based on meaningful and AI-driven information. COVID-19 has made many companies vulnerable and many will be in a recovery phase in the coming years. As a result, AI technology will be able to make a difference to stay competitive. From SMEs to multinationals, from little data to a lot of data, there is always a solution possible with Machine Learning.
With small interventions, we can realize big changes. For example, with a minimal input of data at Brabo, ML2Grow has optimized the pilotage of ships. The planning horizon was improved from 20 minutes to 8 hours. But there are also many possibilities for the medical sector, such as a prediction algorithm that we have developed for use on the ICU of hospitals. In this way medical data can be analyzed in no time at all, saving valuable time.
2. Detection and prevention of cybercrime
Artificial Intelligence and Machine Learning are increasingly finding their way into the world of cybersecurity for both business systems and home security.
Developers of cybersecurity systems are engaged in a never-ending race to update their technology to keep pace with constantly evolving threats from malware, ransomware, DDOS attacks and more. AI and Machine Learning technology can be used to help identify threats, including variants of previous threats.
For example, AI-powered cybersecurity tools can monitor the data from a company’s transaction systems, communication networks, digital activities and websites at a very fast pace. AI algorithms can be used to recognize patterns and identify threatening activities – such as detecting suspicious IP addresses and potential data breaches.
For ‘Flanders Innovation & Entrepreneurship’ (VLAIO), bridging the gap between AI and cybersecurity is one of its most important tasks for the coming years.
3. Advanced chatbots
Chatbots are more and more an important part of a company. Organizations are increasingly embracing the benefits that chatbots offer, for example in supporting customers and in sales and marketing. Although chatbots are becoming more and more present in our daily lives, their performance is still extremely remote. Chatbots can help you book a hotel or a flight but their functionality is often very limited.
AI can take these conversational bots to a higher level. The so-called conversational AI will quickly replace standard chatbots. This advanced form will be able to talk to customers back and forth as if they were human. These chatbots will deal much more efficiently and intelligently with the conversation history and context of the conversations. This will be a breakthrough in optimizing many business processes.
4. Predicting Customer Churn
The way we live, work and connect has been enormously influenced by the spread of Covid-19. Although there had been a strong upward movement towards more digitization for some time, this year we have witnessed a real (r)evolution. Even those who have so far avoided online retail were now forced to reconsider their opinions.
Machine Learning can help companies adapt to this new reality. More and more organizations, which until now have been lagging with digitization, now recognize the urgency of the situation. Many companies are now quickly getting started with concepts such as behavioural analytics and churn prediction. With this technology it is possible to predict when customers will drop out, marketing can then use these signals to quickly and automatically adapt to the needs of the customer. But also in the planning of the delivery of goods, it is possible to plan well in advance to avoid shortages or surpluses. We predict that in 2021 even more small and medium-sized companies will want to increase their competitive advantage by implementing these technologies.
Feel free to take a look at our HLS use case, before we developed a churn predictive platform. We are always ready for a free call to identify the needs of your company or organization.
5. More synergy between AI and IoT
The use of AI and ML is increasingly connected with the ‘Internet of Things’. For example, AI, Machine Learning and deep learning are already being used to make IoT devices and services smarter and safer. But the benefits go both ways since AI and ML require a lot of data to work successfully – exactly what networks of IoT sensors and devices offer.
For example, in an industrial environment, IoT networks in a production plant can collect operational and performance data, which is then analyzed by AI systems to improve the performance of the production system, increase efficiency and predict when machines need maintenance.