Why your AI programs will fail: Data
Everyone is investing in AI. The ability to automate and improve so many business processes makes AI a game changer for most businesses. Advancements in generative AI have simplified the process to experiment and many firms are running pilot programs, proof of concepts, and full-blown initiatives to roll out AI applications to their business unit or enterprise.
A problem is lurking though: reference data. While the most useful predictive and generative AI models will leverage whatever data you feed them, if the reference data within those data sets is not fit for purpose, the models simply won’t perform.
Consider this. A bank or insurance company wants to develop a next best recommendation model to offer clients products or services to enhance their experience with the institution. If the products and services fed from multiple systems are inconsistent, leveraged different descriptive attributes, or even different terms, conditions, and language, the recommendations will be sub-optimal. Can the AI models provide value? Yes, with large volumes of transaction data, historic decisions, customer behavior and risk models, positive recommendations can be made. However, the accuracy of the models will be limited from their true potential for value based upon fundamental discrepancies in the core reference data of the data sets.
Limitations in reference data are nothing new to regulated industries. Programs have been around for years to address weaknesses and inconsistencies in reference data required by risk and regulatory standards. However, those regulatory standards don’t require institutions to address all data, which is where careful considerations must be made for each AI initiative.
The limitations in reference data are endless. Here are three data problems that are lurking in the reference data and are impacting AI programs today.
If you would like to learn more about Kingland's approach to data refinement for AI, please contact us at outreach@kingland.com.