Recently during a roundtable in a pricing conference in Stockholm, one of the B2B OEM participants was explaining how servitization of their product and service offering helped them to go through the hard time of the previous economic downturn by having reoccurring revenue stream in place. This experience created a vibe in their organization globally and resulted in higher attention to subscription business model or outcome-based service offerings. While such success stories

#ArtificialIntelligence, nowadays, is one the biggest buzz words in the business world. It is being used by many companies to show that they are, like everyone else, utilizing the cutting-edge technologies to provide better value for their customers and thus boost their profitability. However, despite of all the efforts, still many organizations are failing to implement successful #AI implementation projects. A recent article in #HBR by three McKinsey partners (Tim Fountaine,

No matter if you want to use #AI (#Artificialintelligence), #machinelearning, or #businessanalytics, you need to find and define the right problem. A well-established problem that clearly defines why and how any or couple of these big data management tools are to be used is the first and most critical step in the whole process of improving the decision making, by digesting big amount of data. Considering above mentioned criticality Henke, Levin and McInerney in an article on