How to translate business problems into correct AI questions
In recent years, the term Artificial Intelligence has often been the focus of attention outside of technology-oriented organizations. Although most companies claim to use artificial intelligence technologies on a daily basis, in reality the implementation of these methods into the business process is mostly still in its infancy phase. The cause lies “lost in translation moment” between business problems/needs and AI related question and solution.
We will present real cases (pharmacy, banking, tourism) how we defined a business problem and “translated” it to data scientists in AI terms to select the right AI technique to solve the problem cleanly and efficiently. Furthermore, some proven methods for evaluating the performance of AI models and validating the Use Case and ROI of the solution “on-the-job” will be presented for each case.
All presented cases embedded analytics solutions in the business and overcame implementation difficulties.
- Business Problems
- How to address correct AI Questions
- How to achieve Value after all
Andrej Gustin has more than 20 years’ of experience in large-scale project management, business process management, consulting and training. His activities are in general in management business consulting on strategy, process optimization and process innovation. As a “representative from business”, he regularly holds lectures and Case Study presentations at the different university programs in Slovenia. Andrej is a vice president of IIBA CHAPTER SLOVENIA and holds a Science Master Degree. In his spare time, Andrej enjoys cycling, cross-country skiing and diving.
Igor Smirnov has more than 20 years experience in implementing Business Intelligence (BI) projects, consulting, coaching and training activities in the fields of business analysis, data science and data analysis in telecommunications, retail, production and pharmaceuticals for global companies like Novartis, Toyota, A1, Pernod Ricard among many others. He is also an expert in customer focused solutions, particularly in big data analysis for the support of loyalty programs and customer insights with use of predictive analysis and machine learning. Igor’s passion is understanding the power of data and releasing its potential in digital-driven companies and industry 4.0 initiatives. Igor has been a member of the IIBA Chapter Slovenia Board since 2009.
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