🚀 Scaling decision automation efforts in organizations cann🚫t happen with the traditional approach of decision management and business rules management software❗

⚠️ Allowing non-technical staff to model business rules as part of the decision-making process is a table stake for decision management software. However, this ability alone will not allow organizations to scale decision automation efforts.

Traditional decision management software still requires lots of effort from the software development and IT team for below which are the roadblocks of scaling decision automation efforts:
🔸Integrations: There is a big and demanding need to integrate new and growing 3rd party apps, online services, and databases as part of the decision-making process.
🔸Orchestration: business rules and decisions are not run in a vacuum; they require interaction with data and systems without building orchestration in codes.
🔸Integrated approach: business decisions require a composite approach to combine multiple techniques such as rules, machine learning, optimization, and so on.
🔸Adaptive control: The need to roll out a model gradually by creating experiments ensuring the outcome is as expected and testing out what works and what does not.
🔸Automated deployment: Once models are created, a very simple and easy way to deploy the model as a service or fully automate the change management into existing change processes

🤔Why are these roadblockers?
Modeling decisions, rules, and domain knowledge to an executable form by operation and SME teams is a very core requirement, do not get me wrong. Once that is done, lots should happen to operationalize those. If the bulk of the operationalization relies on highly skilled software developers, DevOps, and IT teams, then time-to-market still suffers for one reason or another (business agility suffers).

💣These capabilities of #decisionintelligence platform (DIP) not only means SME and operation teams can be hands-on in the operationalization of models and become DecisionOps champions. But also, it means organizations do need to rely less on highly skilled developers, IT and DevOps teams to operationalize the models. In short, it means making model operationalization accessible to everyone.

#decisioncentric #ai #data #decisionautomation

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Published September 10th, 2024 at 07:30 am