The answer is not that simple, which is also why we speak of "data science". Be wary of "specialists" who answer within five seconds that artificial intelligence will definitely add value in every company.
The term "data science" was not chosen at random. The word "science" matters, because the field belongs more to the domain of science than to the domain of IT. Using scientific methods ensures that insights obtained from data are reliable and reproducible. Although programming skills are important for data science, statistical knowledge and business knowledge, used to identify relationships and distil valuable insights, are at the core of the field. When an A.I. model is trained, the accuracy of its results is measured and verified using data that we know has been labelled correctly. That allows us to determine the accuracy of AI models. However, as a human being, you never know in advance whether a dataset will yield something valuable, which is why preliminary research is indispensable.
So how do you determine which use cases could potentially be useful? There are a number of steps you can follow:
1️⃣ It is important to map your business processes and analyse where the bottlenecks, opportunities and challenges lie (business analysis). Which tasks cost a lot of time, money or effort? Which processes could be made more efficient, effective or customer-friendly? Which goals do you want to achieve or which problems do you want to solve? Do you want to make predictions? Do you want to classify objects? Do you want to process texts or information?
2️⃣ It is essential to look at what data you have available and what its quality is (data science). Data is the fuel for artificial intelligence, so without sufficient, relevant and reliable data you cannot develop good use cases. Data comes in many different forms (text, numbers, images, ...) and can be very diverse (databases, Excel, emails, ...). How much data do you have? How structured is it? How current is it? How representative is it of the situation you want to improve?
3️⃣ It is advisable to involve specialised people who can help you identify, prioritise and validate use cases for artificial intelligence. Be critical and ask where their knowledge comes from. They have the experience and tools to support you throughout the entire journey, from idea to implementation. They can also advise you on the ethical aspects of artificial intelligence.
✉ Feel free to send me a message if you have questions about artificial intelligence.
