Business process change is among the hardest challenges in scaling AI systems. Capturing value from AI requires strong leadership and a flexible mindset.
Organizations may need to adapt their workforce to accept recommendations from AI systems and provide feedback to AI systems. This is often challenging. For example, maintenance practitioners who have been doing their jobs in a specific way for decades are often resistant to new recommendations and practices that AI algorithms may identify.
In practice, this means showing the model’s success through internal marketing and executive sponsorship, building AI interpretability into the program, growing data science knowledge in the organization, and tracking value captured so it is visible to the end users.
Tracking business value visibly in the application being developed or put into production helps to align stakeholders so that all parties agree on business objectives and the value unlocked and captured by machine learning models. These reporting numbers can be used for organizational marketing and internal evangelism. The following figure provides an example of a C3 AI application, where the business value captured is central to the application experience.
Figure 39 Example of how C3 AI Energy Management tracks and reports value created and captured with machine learning model recommendations directly within the application
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