In an ideal world, businesses would know exactly how much they can sell, when and where. But building an effective demand prediction model is easier said than done, especially when the retail sector is getting increasingly fragmented.
Artefact has adapted Lean Manufacturing methodologies in order to remedy the seven sources of waste traditionally encountered in artificial intelligence projects.
It’s a Wrap! It has been an inspiring and exhilarating last three days at CogX 2019. We’ve thoroughly enjoyed being at the global epicenter of AI and Data, speaking to future and current industry titans at our stand and taking in an immense depth of knowledge on both highly exciting prospects and deeply pressing issues that are associated with our collective future.
Data deluge Day two of CogEx (or #SogEx as some wag has christened the event on Twitter in the aftermath of yesterday’s downpours), thankfully the skies have remained clear and positivity has shone through. This was apparent in today’s opening session on the main stage, ‘Transformation in the age of machines’ from Salim Ismail of OpenEXO.
Inspired by the seven « wastes » popularised in lean manufacturing, Artefact has adapted this concept to the field of artificial intelligence. This study is based on more than 30 artificial intelligence projects over the last three years.
The increasing understanding of and experience with artificial intelligence by organisations might seem contradictory given the lack of significant AI implementation within most enterprises.
Artefact’s Top Tips for establishing an AI lab
Artefact combines the dynamism and innovative spirit of a start-up with the professionalism of a globally active company, with a clear focus on the core topics of data and AI. We help to initiate the change process, evaluate benefits and build knowledge with our customers to create clear competitive advantages.
Fantasies are commonplace when it comes to AI. However, it is necessary to find the right environment for implementation in order to fully embed AI within the healthcare field.
