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Why Real Architecture is needed for Artificial Intelligence Solutions


In recent years AI or Artificial Intelligence has been shaping up as a collection of Technologies that can be very effectively used in the Digital Transformation journey of Enterprises. As the AI Technologies mature, we can see that there are a lot of use cases for it across many Industries. During the recent Global Architecture Ratings & Awards Summit at New York in November 2019, we had a Panel of Experts discussing “Why Real Architecture is needed for Artificial Intelligence Solutions”.

Realising the importance of AI in today’s economy, we at ICMG felt that there is a real need to Assess and provide Ratings for Architecture work of AI Projects around the world. The Assessment & Ratings Process is carried out in Four stages –Three Off Site and a Final one On-Site at the Summit. Off Site assessments include a Video Interaction with the Jury Members. On-Site assessment involves a Conference style Presentation where Jury Members conduct the On-Site assessment with audience participation. The Ratings criteria include the following:


  • Problem Complexity

  • Stakeholder Concerns Management

  • Well defined Taxonomy (Domain, Business, Technology)

  • Perceived Business Benefits

  • Well defined Architecture Centric Process

  • AI Strategy Models showing Strategic Business Initiatives that contribute to Enterprise Goals and Objectives, Products and Services impacted by AI and the Business Processes that support them etc.,

  • AI Process Models showing Business Processes that are impacted by AI vs the Process Operational Objectives, Business Processes vs the Applications that support them etc.,

  • AI System / Application Models showing Logical Models of Applications impacted by AI vs Application Design / Performance Objectives, Applications vs Logical Data, Applications vs User Roles etc.,

  • AI Technology / Infrastructure Models showing Technology Strategy, Application Components related to AI vs QoS, Application Components vs Infrastructure components, Data Components etc.,

  • AI Implementation Models showing Physical Application vs Physical Network, Storage, Server components, Security Architecture, Physical Database Design etc.,

  • AI Operational Models showing actual instances of Business Operations using AI vs the Operational Data they consume/update, actual locations, performance metrics etc.,

  • Performance Indicators / Measures and Metrics relating to AI

  • AI Architecture Governance

  • Innovativeness of the AI Architecture Work

Some of these Projects may be about how AI is helping to take Business Strategy forward towards Implementation. Examples include using AI to boost efficiency, automate processes, create new products and services, or improve the customer experience, Predicting Customer Churn, Processing of Online Reviews etc.,

If we look at Industry by Industry usage of AI, we can see some examples as below:

  • Healthcare: Help Diagnose patients more accurately and speedily than medical experts, allowing doctors to treat their patients before their condition worsens.

  • Travel and Tourism: Using AI to help human guards at airports enhances passenger safety, reduces security risks and decreases their current workload to focus on truly suspicious suspects, providing another layer of defence against threats.

  • Hospitality: Facial recognition system that puts customers in an intelligently virtual queue, helping the bartenders ensure that they serve drinks on a first come first serve basis.

The greatest potential values of AI are both in top-line-oriented functions, such as Marketing and Sales, and in bottom- line-oriented Operational functions, including Supply-chain management and Manufacturing.

Some of the Technologies that are part of the AI eco System include:


  • Static Image Recognition, Classification, and Tagging

  • Algorithms in Trading Strategy Performance Improvement

  • Efficient, Scalable Processing of Patient Data

  • Object Identification, Detection, Classification, and Tracking from Geospatial Images

  • Text Query of Images

  • Content Distribution on Social Media

  • Text-Based Automated Bots

  • Sensor Data Analysis (Internet of Things)

  • Human Emotion Analysis

In the Panel Discussion “Why Real Architecture is needed for Artificial Intelligence Solutions” referred above, Experts concluded that three areas of Architecture need focus for a successful implementation of AI - Cloud Architecture, Data Architecture (Big Data) and Security Architecture. The essential first step in developing these Architectures is the Framework that is Enterprise Architecture – Ontological structure that will be used by Methodologies to develop these Architecture Views.

If your Enterprise has embarked on AI Journey with use cases similar to the above, it is the right time for you to be part of our AI Architecture Ratings & Awards Summits that will be announced in 2020 to take place across India, US, Australia, Singapore and Europe. You can get more information about this exciting Program at:

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