The road to truly AI-native BSS

It is widely recognized that 5G and the IoT represent the main growth opportunities for communication service providers (CSPs) in the coming decade. To support emerging use cases in these areas, CSPs require business support systems (BSS) that can handle complex business situations and optimize outcomes with minimal manual intervention. Artificial intelligence (AI) is the obvious answer, but introducing it into existing BSS is problematic for a number of reasons. The Ericsson Technology Review article* about AI-native BSS, proposes solutions to these problems.

In the control room

For the most part, AI capabilities today are simply bolted onto telco BSS one by one. But this is inefficient in terms of life-cycle costs, because the BSS must be repeatedly upgraded to benefit from the AI algorithms. Further, as they are separate systems, the BSS information must be transformed to fit AI systems, and vice versa. A much more efficient alternative is AI-native BSS – that is, BSS with intrinsic AI capabilities where the AI logic is a natural part of BSS logic in terms of both design and operation. This approach results in a system that can handle more complex business situations, generating more optimized business outcomes.

Ericsson recommends an architectural change to traditional BSS to create AI-native BSS. One of the key differences between traditional BSS and AI-native BSS is the fact that AI-native BSS enable the various applications within the BSS to share business information with each other in an efficient and secure manner – a critical capability in the emerging 5G-IoT world. Most significantly, this evolution requires a layered view of an enterprise's strategic, tactical and operational levels in the BSS, together with the introduction of two new business logic elements (intents and events). Each layer is responsible for different business intents which can be fanned out on the layer below. A business intent states the desired or optimal outcome of a given situation, and the different AI capabilities are used to support the realization of the business intents. 

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The different layers can be viewed as different levels of uncertainty. If we regard the world as deterministic we can use a simple event, condition, action logic (ECA) in every situation to obtain the desired outcome. In this case AI capabilities can be used to understand how the ECA logic shall be realized, but the AI capabilities are not part of the execution logic. In other situations, we need to take uncertainty into consideration when we take decisions and perform actions based on our intents. AI capabilities can support us in the process of making the business intents crisper by lowering the uncertainty. Several different techniques use for decision making under uncertainty are described in a book by Kochenderfer, et al. (ISBN:978-0262029254).

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The OODA loop
was initially developed in the 1970s as an in-combat decision tool of the U.S. Air Force. OODA stands for observe, orient, decide and act. Many of the OODA loop's basic concepts are found in today's software agent systems. In the AI-native BSS, the business intent, together with evaluation and learning, has been integrated in the OODA loop. The OODA loop can have various depths of reasoning, from deterministic to deep learning – that is, the same OODA loop engine can be used to observe, decide and select the proper actions for all event types, regardless of complexity. It can also be used recursively to build layered structures with arbitrary depth – that is, it can support multilayered business processes and interactions. This makes it possible to use the extended OODA loop pattern as a reusable pattern on any of the layers.

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Understanding the data and its usefulness are critical aspects in order to utilize AI capabilities in an effective and efficient way. In many cases one person does not have the required knowledge in data analysis, and the business the AI capabilities shall support. Tools used to present the data must support both the data science view and the business view. An example of a tool which might be able to support both views is Facets. Why not take some time and play around with it?

Sometimes the decisions made by AI capabilities are not easy to understand, and there might be legal aspects which requires a human to justify a decision made by AI capabilities. In other cases, a rapid prototyping and visualization support is needed during creativity processes. It is critical that the people working in AI-enabled processes are able to understand the reasoning behind AI-generated results. All of the steps in our modified OODA loop can be understood and executed by both humans and machines.

How can humans and machines support each other in a more efficient way?

In order to make it possible for humans and machines to work together in the most efficient way possible based on the particular circumstances of the organization, intelligence augmentation (IA) is needed. IA has its roots from the paper 'Augmenting Human Intellect: A Conceptual Framework' by Engelbart in 1962. Another name for IA is AI-centaur, which was coined by Kasparov. Kasparov was convinced that IBM used Deep Blue together with a human chess player when Deep Blue defeated him in 1997.

AIA is the combination of AI and IA. The interactive paper by Carter and Nielsen shows how only brush strikes in different colors will guide the user towards a generated picture. Imagine this interactivity used on a tactical level to express the business intents. The extended OODA loop helps the human and the machine to work together and the machine will present the available activities depending on the position, color and shape of the "brush strike".

For more down to earth ideas and background of how AI can be used in a BSS I encourage you to read the AI-native BSS ETR article.

After reading the AI-native BSS paper, go back to the interactive paper and, who knows, you might find the ideas in this blog too basic and narrow minded, because you already know how they can be implemented or have another way of looking at the problems at hand. Please, let us know about your ideas and realizations. We are more than happy to learn and to get engaged.

*The Ericsson Technology Review article about AI-native BSS

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