Our previous artificial intelligence (AI) series provided some broad context into common AI terms and theoretical use cases. Our next series will provide a beginning-to-end map of how AI can be piloted, scaled and tested in a financial setting.
In our next series on AI, each article will present AI development through the lens of a financial company planning to test and deploy AI applications in their systems.
As a refresher, let’s define the main pillars of data-driven, automated intelligence systems:
Our prior series also covered how these pillars can be used for more predictive actions, allowing businesses to better estimate customer responses.
Perhaps you’ve decided to test an AI application with a small-scale pilot. What steps are usually taken in to a piloting phase? What questions should you ask before starting your own pilot program?
Based on what we experienced in our own internal pilots, we recommend answering the following questions:
Congratulations, you successfully piloted your AI application. Now, you want to scale that performance across the business. What steps usually go in to this scaling phase? What are the lingering questions that arise during a scaling process?
Here’s what we recommend asking at this stage:
Now that you piloted and scaled an AI application for customer-facing use, how will you track results and map back to program objectives? Aligning results to goals is typical of any use case, and AI pilots are no different.
Here’s what we recommend asking at this stage:
Testing, scaling and analyzing an AI application can position your organization for more efficient workflows, while helping manage risk factors from emerging cybersecurity threats.
If you’re interested in learning more about AI applications in finance, check out this series of AI articles.