In this talk, we will present three different strategies for finding concrete use cases at medium to large companies. These strategies are: finding problems and see if they can be solved with Machine Learning, mapping known solutions to your company, and targeting processes that are currently driven by business rules.
These use cases should be evaluated by their inherent potential, the risk involved and the estimated work needed. Typically you lose 75% of the potential use cases after simply checking basic requirements like availability of data, potential of business process integration, technology readiness …
Next we will present the Machine Learning Maturity Assessment: an evaluation framework for evaluating an organisation on their ML use. In that framework, four dimensions are important: strategy, people, data and legal. These dimensions are then scored on objective subcriteria according to fixed maturity levels. This tool helps companies to find their weaknesses, strengths and source of uniqueness.
Finally, we will show you why you need to integrate AI and Machine Learning consistently as a process, instead of just as a one off. We will show real life examples of how these techniques were applied to large companies that are active in telco, automotive and finance industries
However, throughout all these experiences, money still needs to change hands. Will we use the same old swipe or chip credit card, or will payments innovate as well? In this session, we explore how payments can move from being a hurdle, to a customer experience driver.
#keynote #AI #machinelearning
Jos is the VP of Applied AI at Faktion, an AI engineering company that works for some of the biggest and most innovative companies in the world. Originally a mathematician, he has lead Machine Learning and AI implementation all over the world. Over the last years, Jos lead the team that was responsible for developing the NLP models that power Chatlayer.ai, the most popular enterprise-ready chatbot platform in Europe.