Decision Intelligence Architecture enables organizations to gradually and incrementally achieve higher degrees of intelligence that improve and influence business operations and change organizational behaviors.
The Decision Intelligence (DI) movement is gaining traction for several reasons. At its most fundamental level, Decision intelligence is the only chance for dynamic and regulated organizations to create trustworthy machine intelligence around decision-making that can serve their internal stakeholders, clients, and partners.
With Decision Intelligence, we aim to apply AI in the context of business decisions and create intelligence that not only can support humans in making decisions but also can make, execute, and automate decisions on their behalf in a controlled and reliable manner.
However, one of the challenges in business decisions is that they are deep inside many different applications and processes or may be deep in the heads of people in operation and SME teams. They are in various levels of maturity all over the place. In addition to that, business decisions are very different in nature. Some decisions are rules-driven, and some are based on domain knowledge. At the same time, we can use multiple techniques, such as Business Rules, Machine Learning, Optimization, Data processing, etc., to automate them.
From the outset, there are many arguments on what machine intelligence is and how it is comparable to human intelligence. Is creativity a type of intelligence that machines can or should accomplish? Where does emotional intelligence fit into this spectrum? Then, there are the more philosophical and fear-driven questions about intelligence.
However, when it comes to intelligence related to decision-making in a dynamic and regulated environment, only a few of those arguments remain relevant because not all of those types of intelligence are relevant in that context.
Then the question becomes, what types of intelligence are relevant to decision-making for dynamic organizations operating in a regulated environment?
When we put all of these parameters and options together, it will become very confusing and overwhelming for many organization leaders to look at Decision Intelligence from a practical lens and understand what type of intelligence they need to build across the organizations for all possible use cases with different levels of decision maturity that allows them to consistently deliver business values by leveraging decision automation using DI platforms.
Decision Intelligence Architecture is the answer to this problem. It defines what intelligence looks like and what you can expect from it. Then, you can overlay the decision maturity model on top of it and work towards achieving the relevant intelligence for business decisions that are appropriate for it.
Decision Intelligence Architecture
Decision Intelligence Architecture defines five layers of intelligence in organizations, each of which is a prerequisite of the next layer.
- Knowledge Core: This is the core foundation of organizational knowledge. It captures the domain terminologies, language, definitions, concepts, facts, and, more importantly, business decisions that organizations make and execute.
- Intelligence Core: This layer is a Symbolic AI that allows the buildup of core intelligence that is 100% explainable, reliable, and safe to deploy to critical operations. It is based on the open standard Decision Model and Notation (DMN).
- Expert Intelligence: On top of the previous layer, it enables organizations to build intelligence that interacts with their environment, senses events, captures data, and processes them through the Intelligence Core while ensuring the decisions' outcomes are optimized based on business objectives.
- Cognitive Intelligence: It is based on Neuro-symbolic AI, which allows organizations to build a composite AI model based on a previous layer that uses machine learning, deep learning, and symbolic reasoning to take advantage of the best of both worlds.
- Autonomous Intelligence: This enables organizations to build intelligence on top of the previous layer, enabling systems to learn and adapt to changing and dynamic environments with a full capacity of reasoning, planning, etc., inherited from previous layers.
Decision Intelligence Architecture introduces different layers of intelligence allows organizations to develop AI for Decision-Making
Conclusion
The data-driven approach to building intelligence using Machine Learning alone will not create the intelligence needed for decision-making in a dynamic and regulated environment.
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The intelligence for decision-making requires a combination of multiple techniques based on domain knowledge explicitly defined in organizations. Additionally, depending on the maturity of the business decisions, the pathway to create the intelligence for a specific use case varies. Therefore, the prerequisite nature of intelligence layers in Decision Intelligence Architecture ensures business decisions with the right maturity will go toward more complex intelligence as they progress to a higher level of maturity based on their Maturity Model.
Last updated November 14th, 2024 at 12:59 pm Published August 29th, 2024 at 11:14 am
