💢There is a big gap between business decisions and decisions executed and made in #ai #machinelearning #llms #genai.
Business decisions are complex and based on domain and operation knowledge in organizations. These are the explicit organizational knowledge that drives judgment based on policies, rules, and expertise in a particular subject.
⚠️The biggest issue with #machinelearning, whether generative or non-generative, is the fact that it is based on data.
Yes! You read it correctly. Data-driven decisions are issues.
There is a time and place for these types of automated decisions, but they should always be part of a higher-level decision model that represents a holistic view of business decisions.
How? By using Decision-Centric Approach® https://lnkd.in/gcWUy9eU
1️⃣ Breakdown a complex business decision into smaller and easier-to-understand components (called decision units)
2️⃣ Establish the relationship and dependencies between the decision units
3️⃣ Define the input data for the decision units
The above easy 3-step process enables you to create a multistep hierarchy-cal decision model (Decision Graph) representing a holistic view of a business decision.
🚀Now, you have an opportunity to apply different techniques and technologies, such as #rules #machinelearning #optimization, etc., to each individual decision unit based on its nature. A Decision Graph coordinates all of its subcomponents (decision units) and creates the final outcome using the right technology for the right decision unit.
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Published June 7th, 2024 at 07:30 am
