In a very simple term, when a software development team develops software or system and puts it out there in front of users in the real environment, they call it deploying to production. When professionals in AI build a machine learning model and put it in the real environment, they call it Operationalizing AI.
And there is a good reason for calling it Operationalizing AI. Not only building AI and machine learning models requires different processes and mindsets, but also, their aims and objectives are very different. Software and systems are built for many different reasons, but AI and machine learning models are primarily built for one main reason, improving business operations of some sort in terms of decision making.
But here is a surprising factor, AI and machine learning model’s deployment rate is a big issue. Only less than 10% of the models are operationalized.
What are the challenges?
There are several challenges in operationalizing AI, but if we look at them categorically, they fall into two broader groups: Integration and Adaption challenges.
One of the main challenges is integration. AI and machine learning models do not bring values when they are isolated, it requires to interact with systems, becomes the part of multiple functions and business units.
The AI and machine learning results and decisions outcome should become a part of the day to day activities of the business, they should be reflected in information systems. And they must be part of operational decisions to have an impact on business operations.
The second big challenge is building the internal and external trust for automation. Many established companies have ways of doing things, they have invested in people and their staffs and they built a reputation and a reliable brand. Therefore, doing things differently requires the internal acceptance of experts. Requires establishing trusts with domain experts of the fields and proving the quality of the decisions made by an automated solution using AI and machine learning can be trusted by their customers and has the same or better quality of the decisions made by their own staffs. This is a very big cultural barrier in the adaption of decisions outcome as part of business operations.
Many businesses are regulated; insurance providers, financial institutions, healthcare providers and suppliers, and many more are operating in a regulated environment. Meaning there are regulators and auditors that also must trust in the decision outcomes. Therefore, the models and algorithms need to be transparent. The decision results must be explainable and traceable.
Digital Decisioning – Digital Intelligence Platform
To address the above challenges, a digital intelligence platform should bring multiple techniques, technologies together to ease the adaption and established trust with related parties i.e. internal and external stakeholders.
This means organizations need to make sure the machine learning algorithms are controlled, configured, and wrapped by business rules of environments they are operating, they are enforced to comply with internal and external policies and regulations. And their domain experts and staffs can manage and change the rules around the machine learning algorithms.
One of the best ways to accomplish this level of configuration is to make human workforce in control of automation and decisions outcomes. The digital intelligence platform should enable companies to utilize AI technologies including machine learning, rule-driven computation logic, robotics and process automation tools to deliver decision automation and meet the business objectives. Therefore, not all automation is based on machine learning algorithms, but companies will use the right tool from the digital decisioning platform’s toolbox. This will introduce a multi-disciplinary approach to decision automation.
To put this in practice, a methodological approach should be in place, to guide organizations on how to make every automation’s efforts around decisions. A broader automation initiative will enable a wider integration of any models i.e. probabilistic, deterministic, and orchestration within an organization and its systems.
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The aim of the digital decisioning using decision intelligence platform is to enable companies either for disrupting operations or to give them the flexibility to improve decision outcomes that deliver business values to meet the business objectives.
This multi-disciplinary approach covers the fully automated decisions where machines and algorithms make decisions and take actions upon the decision’s outcome. Also, enables organizations to implement semi-automated decisions, where the human workforce handles exceptions, unusual cases, reviews and approves the final outcomes.
Published November 12th, 2020 at 03:22 pm
CEO and the founder of FlexRule – He is an expert in architecture, design, and implementation of operational decisions, business rules, and process automation. Created Decision-Centric Approach, a methodology that brings People, Data, Rules, and Processes together to automate operational business decisions.
This approach is recognized by Gartner as the missing link to provide business value to organizations.