3 Key Aspects for Successful Predictive AI Deployment in Business Operations

Predictive AI is gaining importance in business operations despite being overshadowed by the current popularity of generative AI. Embracing predictive AI requires a shift towards probabilistic thinking, which often faces cultural resistance. However, understanding predictive AI is accessible and can be distilled down to three key aspects:

Defining the prediction target: Stakeholders must work with data professionals to establish the outcome or behavior to predict, such as customer actions or fraud.

• This involves identifying the specific event or behavior that the predictive model should assign probabilities to.
• Examples include predicting if a customer will click, buy, lie, cancel their subscription, or commit fraud.

Evaluating model performance: Establishing the right metrics is crucial for determining if a machine learning model is ready for production use.

• Stakeholders should help define business metrics, such as the expected improvement in profit or savings, to assess the model’s value.
• Accuracy is often a misleading metric and may not be the most relevant for evaluating a model’s practical utility.

Operationalizing predictions: Determining how to act upon the model’s predictions is a critical aspect of successful predictive AI deployment.

• Stakeholders must be involved in deciding how to use the probabilities generated by the model to drive actions and decisions.
• For example, if a customer is predicted to be likely to buy, they should be included in a marketing campaign. If a transaction is flagged as potentially fraudulent, it should be blocked or audited.

Collaboration is key to success: Most predictive AI projects fail to reach deployment, largely due to a lack of deep collaboration between business stakeholders and data professionals.

• Business professionals must actively engage in the details of the three key aspects of predictive AI projects to provide informed input and keep the project on track.
• Without hands-on involvement, stakeholders may hesitate to authorize the deployment of predictive AI solutions.

By understanding and embracing these three essential aspects of predictive AI, business leaders can effectively collaborate with data scientists to harness the power of probabilistic thinking and drive operational improvements across various domains.