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A key aspect of the ''how'' question is the type of organization required to implement AIoT. For many smart, connected products there will be a small set of features with a high level of depth and maturity. Take, for example, a vacuum robot which will focus on functions related to navigation and cleaning. For such a product, usually a quite sizable team will collaborate on these key features, and continuously work on improving and enhancing them. However, as the "long tail" of AIoT below indicates, there are usually also many organizations which are looking at large numbers of much smaller individual opportunities for AIoT-enabled solutions. This is typical for the Digital Equipment Operator. For example, in a factory or an energy grid there will be many opportunities for smaller enhancements. Consequentially, this must be addressed by an AIoT organization which is focusing on "harvesting" these opportunities in an efficient way.
A key aspect of the ''how'' question is the type of organization required to implement AIoT. For many smart, connected products there will be a small set of features with a high level of depth and maturity. Take, for example, a vacuum robot which will focus on functions related to navigation and cleaning. For such a product, usually a quite sizable team will collaborate on these key features, and continuously work on improving and enhancing them. However, as the "long tail" of AIoT below indicates, there are usually also many organizations which are looking at large numbers of much smaller individual opportunities for AIoT-enabled solutions. This is typical for the Digital Equipment Operator. For example, in a factory or an energy grid there will be many opportunities for smaller enhancements. Consequentially, this must be addressed by an AIoT organization which is focusing on "harvesting" these opportunities in an efficient way.


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[[File:0.3.1 Long Tail.png|600px|frameless|center|The Long Tail of AIoT]]
[[File:0.3.1 Long Tail.png|600px|frameless|center|The Long Tail of AIoT]]



Revision as of 12:17, 25 July 2021

More...More...More...More...More...AIoT Framework

AIoT combines two of the most important technology paradigms of this decade: Artificial Intelligence (AI) and Internet of Things (IoT). In order to best understand AIoT from all relevant perspectives, we will start by looking at the why, what, who and how perspectives, inspired by the work of Simon Sinek [1] as well as the St. Gallen IoT Lab [2]:

  • Why: Better understand and articulate the purpose and AIoT-enabled business outcomes
  • What: What can be achieved with AIoT in terms of smart, connected products and solutions
  • Who: Roles and responsibilities in the context of an AIoT initiative
  • How: Project blueprint for AIoT execution and delivery

While Simon Sinek suggests to Start with Why, we will first look at the what to provide some context, before then discussing why you should consider it.

What: Smart, connected products and solutions with AIoT

The smartness of an AIoT-enabled product or solution is usually either related to an individual physical product/asset ("product/asset intelligence"), or to a group/fleet of assets ("swarm intelligence"). Technically, asset intelligence is enabled via edge computing, while swarm intelligence is enabled via cloud computing. Asset intelligence is applying AI-algorithms to data which is locally captured and processes (via sensors), while swarm intelligence is applying AI-algorithms to data which is captured from multiple assets via IoT-technologies in the cloud. Optionally, Digital Twin concepts can be used in order to create a digital representation of the physical entities.

The AIoT Framework is focusing on products and solutions which utilize the combination of AI and IoT. However, since this usually represents a significant investment, one should carefully evaluate if both are actually needed.

AIoT Intro

AI (or Machine Learning, one of the most important sub-sets of AI) makes use of different types of methods. The 3 main methods include supervised, unsupervised and reinforcement learning. The supervised learning method relies on manually labeled sample data, which is used to train a model so that it can then be applied to similar, but new and unlabeled data. The unsupervised method attempts to automatically detect structures and patterns in data. With reinforcement learning, a trial and errror approach is combined with rewards or penalties.

Different, highly specialized AI/ML methods support a wide range of use cases. AIoT is focusing on those use cases which are most relevant when dealing with physical products or assets. For example, supervised learning can be used for image classification, which plays an important role in optical inspection in manufacturing.

AIoT Use Case Patterns

An important differentiation which we are making in the AIoT framework is between smart connected products and smart connected solutions. Smart, connected products are often very highly standardized, feature-rich and well rounded. Smart, connected solutions on the other hand are often more custom, ad-hoc solutions. They are often designed to solve a specific problems, e.g. for a particular production site, a particular energy grid, etc. Obviously, this is not a black and white differentiation. There are also often cases which are a bit of both, product and solution.

As will be discussed in more detail later, smart connected products are manufactured and sold by a Digital OEM, while smart connected solutions are usually acquired and operated by a Digital Equipment Operator.

What: Product vs. Solution

Why: Purpose and AIoT-enabled business outcomes

While AI and IoT are exciting technical enablers, anybody embarking on the AIoT journey should always start by looking at the why: What is the purpose? And what are the expected business outcomes?

From a strategic (and emotional) point of view, the purpose of the AIoT initiative should be clearly articulated: What is the belief? The mission? Why is this really done?

For the business sponsors, the expected business outcomes must be clearly defined as well. As was discussed in the what section, most AIoT initiatives are either focusing on products or solutions. Depending on the nature of your initiative, the KPIs will differ: For AIoT-enabled products, they tend to focus more on the customer acceptance and revenue side, while for AIoT-enabled solutions they tend to focus more on efficiency and optimization.

Why: Product vs Solution

How: Getting things (and AI) done

Smart connected products and solutions usually make use of AI and IoT in different ways. This must be taken into consideration when looking at the how. Smart products often rely on AI that was specifically developed for them ("deep Data Science"). The goal often is to create new Intellectual Property which helps differentiating the product. For solutions, this often looks different: here the goal is to minimze development costs, e.g. by re-using existing AI algorithms and models. From the IoT point of view, products and solutions also differ: products usually have built-in connectivity capabilities (line fit), while solutions usually have this capability retrofitted. This is especially important for operators looking at heterogeneous fleets of assets or equipment.

How: Product vs. Solution

A key aspect of the how question is the type of organization required to implement AIoT. For many smart, connected products there will be a small set of features with a high level of depth and maturity. Take, for example, a vacuum robot which will focus on functions related to navigation and cleaning. For such a product, usually a quite sizable team will collaborate on these key features, and continuously work on improving and enhancing them. However, as the "long tail" of AIoT below indicates, there are usually also many organizations which are looking at large numbers of much smaller individual opportunities for AIoT-enabled solutions. This is typical for the Digital Equipment Operator. For example, in a factory or an energy grid there will be many opportunities for smaller enhancements. Consequentially, this must be addressed by an AIoT organization which is focusing on "harvesting" these opportunities in an efficient way.

The Long Tail of AIoT

Finally, it is important to understand which capabilities are required for implementing AIoT. The AI side usually requires Data Science and AI Engineering capabilities, as well as AI/ML ops capabilities (required for managing the AI/ML development process).

The IoT side usually requires generic cloud and edge development capabilities, as well as DevOps supporting both, cloud and edge (which usually means support for OTA, or Over-the-Air-Updates of software deployed to assets in the field).

The third key element is the phyiscal product or asset. For the Digital OEM, it will be vital to manage the combination of physical and digital features and their individual life cycles. For the physical product, this will also need to include manufacturing, as well as field support services.

AIoT Overview

Who: AIoT roles and responsibilities

The "Who" perspective must address the roles and responsibilities required for successfully delivering your AIoT initiative. These will partially be different for product- vs. solution-centric initiatives, as we will discuss later. It is important to have a holistic view on stakeholder management, including internal and external stakeholders.

AIoT - Who?

External stakeholders can include investors, users of the product of solution, partner and suppliers. In a larger organization, internal stakeholders will include business sponsors, senior management, compliance and auditing, legal and tax, global procurement, central IT security, central IT operations, HR, marketing, communication and sales. And finally, one should not forget about the stakeholders within its own organization, including developers, technology experts, AI experts, and potentially HW/manufacturing (in the case of the Digital OEM).

As indicated by the diagram below, the AIoT framework is primarily addressing the middle management, including product/solution managers, project/program managers, development/engineering managers, product/solution architects, security/safety managers, and procurement managers. Ideally, the framework should enable these key people to create a common vision and language which enabled them to integrate all the other stakeholders.

Who: Roles and Reponsibilities

References

  1. Start with why: how great leaders inspire everyone to take action, Simon Sinek, 2010
  2. The Business Model Navigator: 55 Models That Will Revolutionise Your Business, Oliver Gassmann, Karolin Frankenberger, Michaela Csik, 2014