Artificial Intelligence (AI) has existed since the invention of the first computers and will be relevant as long as computers exist. The ongoing development is not linear but has taken place in multiple waves. We are currently in such a wave again, in which incredibly rapid innovation and development is happening, especially in the field of Machine Learning (ML).
Until now Machine Learning as an integral part of AI served primarily the purpose of extracting information from data and thus supporting humans in their decision-making process. This is still highly relevant, but the real value in AI lies in aiming to not only support these decisions, but to completely automate them. This is still in its infancy but can already be implemented for certain use cases.
At paiqo, we develop AI solutions primarily in the following two abstract areas: people and machines.
People – Customer Analytics
People create an incredible number of digital footprints in the modern digital world. In business, this can happen in several different roles such as customer, supplier, employee, and many more. The role as a customer is the role that people take on most often in businesses and in their private life. Here, AI can support in a variety of ways. We call this area: Customer Analytics.
The focus is on your customers: Machine Learning methods help you get to know your customers better, understand them better and address them optimally. For example, data-driven customer segmentation can help you tailor campaigns better to your customers (campaign optimization), whereas recommendation systems help with cross- and up-selling. Customer churn prediction, on the other hand, allows you to proactively increase customer retention.
Customer analytics thus improves customer loyalty along the entire customer journey and allows you to tailor your business even more specifically to the needs of your customers to increase the overall customer satisfaction.
Machines – Machine Analytics
Not only humans leave digital footprints, but as the Internet of Things (IoT) evolves, more and more machines do so as well. In fact, the amount of data created by machines will soon by far exceed the sheer volume of data created by humans. This area is well suited for AI assistance. We call this area: Machine Analytics.
Machine Analytics typically deals with the analysis of machine data to increase Overall Equipment Effectiveness (OEE). We distinguish between machines that are used in production (Manufacturing Analytics) and machines that are the finished product in themselves and now operate somewhere on the planet (IoT Analytics).
Depending on the area, the use cases may differ: While Manufacturing Analytics is all about optimizing production by predicting and optimizing product quality (Predictive Quality & Parameter Recommender), IoT Analytics is concerned with applying machine learning to manage these intelligent devices better and more intelligently. Prediction of machine failures (Predictive Maintenance) is applied in both areas.
Using Machine Analytics, you can optimize the output quantity, quality, energy usage, environmental footprint, or production costs of your production. In the area of IoT and intelligent devices, you can respond even more specifically to the needs of your customers and offer added value.
Our paiqo AI experts are always up to date with the latest research and findings in the field of AI. They even publish their own work and also teach at various universities and colleges.
We have successfully implemented many AI research and implementation projects. Our approach has proven successful in many projects and is continuously optimized to your needs.
Our AI experts not only make the first “proof”, but also support you in the operationalization of AI and machine learning in your day-to-day business.