AI System Architecture

Artificial Intelligence System Architecture

Artificial Intelligence System Architecture

What is it?

An Artificial Intelligence System Architecture is a common basis for the design, development, implementation and management of artificial intelligence systems. It speaks to the form, appearance, function and location of artificial intelligence systems in a common language that can be understood and used by management, developers and users alike. As such, it is a mode of communication and also a set of processes for establishing a record and reference. An architecture-based artificial intelligence (AI) systems approach, as the name suggests, has architecture as its centrepiece.

What is its Business Benefit?

An artificial intelligence systems architecture establishes a more comprehensive framework for producing high-level artificial intelligence system policy statements and strategies, detailed specifications, guidelines, standards and job descriptions. It further provides the basis for measurement, feedback and modification of the process.

How is it used?

The artificial intelligence systems architecture approach establishes a set of functional activities that, once established, have a permanent role in the continued development and deployment of AI systems. Any enterprise using artificial intelligence systems has some elements of each of these components already in use. The approach sharpens, formalizes and coordinates these activities and makes them more mutually effective. The approach consists of the following integrated and non-linear components:

  • The Data Protection Impact Analysis looks at the data protection imperatives of the organization and concentrates on the consequences of the compromise or loss of information on data subject rights. Instead of focusing exclusively on risk, the DPIA establishes priorities based on potential impact on individuals. Two or more identical technical environments may have sharply different data protection impact profiles. The level of formality and comprehensiveness of the DPIA depends on the confidence the organization has in its current perception of risk and data subject impact.
  • An AI Policy is a high-level statement of the governing body’s expectations for AI. It is generally a short, directive and enabling document. It should not contain detailed, prescriptive information. These belong in standards, procedures and guidelines.
  • AI Strategy defines at a high level how the use of artificial intelligence systems will evolve in the environments in question. It deals with priorities, timing, responsibilities, constraints, dependencies and alternatives. Strategies are usually developed in several time frames - short, medium and long-range.
  • The combined direction of the policy and strategy creates a series of Technical and Operational Requirements which in turn form the basis for the development of the architecture. While directive in nature, they do not go into the same level of detail as product specifications, standards, procedures and job descriptions. These are products of the architecture.


The AI systems architecture deals with management as well as technical and organisational content, and the relations between these elements. AI systems architectures must have these three important characteristics if it is to result in realistic and useful implementations.

The end products of the AI systems architecture are reflected in Technical documentation containing technical standards and specifications as well as operational and administrative procedures, standards, guidelines, roles and job descriptions. The distinction is based primarily on the priorities developed in the requirements and architecture phases.

The final elements in the AI systems architecture development process include actual execution, measurement, feedback and refinement over time.


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