In January 2020, a handful of senior IT experts and enthusiasts from different companies met at the Bosch Connectory in Stuttgart to exchange their experiences and views on AI and IoT. AI was at the peak of a new hype, fueled by Alpha Go, advancements in autonomous driving, and not to forget about Cambridge Analytica. The general feeling was that -- with the exception of autonomous driving -- AI had not truly arrived in the world of IoT. Every IoT article or presentation in the last ten years mentioned predictive maintenance, but in reality, many IoT applications were still much more basic. How could AI be better utilized in the world of physical products, manufacturing, and equipment operations? The workshop was organized as an open exchange, with a mixture of presentations and group discussions. After three days, there was so much excitement about the topic and the way collaboration in the group worked, that it was decided to make this a regular thing. The result was the formation of the AIoT User Group, a loosely coupled, nonprofit network of AI and IoT practitioners, who work together to exchange experiences and best practices on the application of AI in the IoT. Throughout 2021, local chapters were set up in Singapore (special thanks to CK and Thomas!), Shanghai (Nǐ hǎo, Gene and Cherry!) and Chicago (hi Fermin and Hans!).
Photo: First AIoT User Group meeting, with practitioners from Accenture, Bosch, Clariba, Deutsche Post, Evaco, mm1, Opitz, Recogizer, TH Köln and Tomorrow Labs, at the Bosch Connectory in Stuttgart.
Over time, it became clear that it would make sense to document the collected wisdom in a good practice framework: this is how the Digital Playbook started. In fact, it first started as the AIoT Playbook, with a more technical focus. Over time, the business strategy and execution perspectives were added. The result is now a holistic Digital Playbook, including the technical AIoT Framework. Content creation for the Digital Playbook and AIoT Framework is driven by experts in different domains (see the AIoT Expert Network). The AIoT Editorial Board provides strategic guidance and management support. The basic working modes are so-called Unplugged-Sessions, where the real work on the Playbook is happening. All the material is developed as open source content (using CC BY 4.0) and is also used as a foundation for different AIoT-related training courses.
How to get involved
If you are interested in joining the AIoT User Group, good starting points are the website aiot.rocks, as well as the AIoT User Group on LinkedIn. The main site for the Digital Playbook is simply aiotplaybook.org.
Activities of the AIoT User Group involve:
- Unplugged Sessions: These hands-on sessions bring together participants to work on different topics related to AIoT. The results are consolidated and eventually captured in the Digital Playbook.
- Training: Participants of the AIoT User Group are organizing AIoT trainings in different regions, utilizing the content from the Digital Playbook.
- AIoT Lab: The AIoT Lab is a virtual lab with an increasingly global footprint. Here, experts and practitioners work together to find practical solutions to different AIoT-related problems.
The AIoT User Group has worked on developing a common vision for digital business with smart connected products and solutions, enabled by AIoT. What does this vision look like? Heiko Löffler and David Monzel are Senior Consultants at mm1 consulting and frequent contributors to the Digital Playbook. Their explanation is as follows: AIoT combines AI and connectivity for physical products. These can be new product categories (smart, connected products - short SPCs), or retrofit solutions for existing assets and equipment in the field. The general idea is summarized in the figure below: physical products (e.g., a forklift) are end points with physical components and on-board computing (combining hardware, software and AI). The physical component has a unique identifier and continuously captures status and process-critical data, as well as data about its environment. These data are both processed on the device (e.g., via AI) and transmitted to the cloud/backend via IoT connectivity. The product is integrated with business processes (e.g., warehousing), and solves specific customer problems (e.g., optimizing warehousing tasks). The product is exchanging data in a closed loop with a backend (e.g., cloud or on-premises systems). In the cloud, the data are processed to create a Digital Twin of the physical component. This is then continuously analyzed by AI to derive and communicate measures or predictions for both the individual physical components and the entire fleet of products. Furthermore, both the physical and cloud components are integrated with the customer environment to ensure customer-centric value delivery. SCPs become part of the customer’s physical and business processes: they sense and interact with their physical environment and are connected to the customer’s IT infrastructure (e.g., ERP systems). Finally, the product provides its manufacturer with information about the performance of the product in the field, and how customers are using the product. In all of this, AI can enable new functionality either onboard the product or in the backend. IoT provides the required connectivity between the product and the backend. Digital Twins are a digital representation of the real, physical product -- providing abstraction, standardization and a rich, semantic view of the AIoT data. AI can be used to help create Digital Twins, or to build applications that utilize them. Of course, this is a big vision, which will not become a reality for each product category overnight. However, it shows the potential of AIoT. Additionally, not all projects might look at such a high level of productization and deep integration -- AIoT can also support more basic retrofit approaches (referred to as solutions throughout the playbook).