I recently had the opportunity to talk to Christian Hubbs and Muhammed Shuaibi from Artificially Intelligent Podcast about the value AI and analytics generate for businesses. We reviewed how a growing number of businesses are seeing a lot of value added in terms of problem-solving when they bring in more sophisticated machine learning models and technology.
The conversation quickly pivoted towards how to determine the analytics and AI that better suit your business needs, as well as understanding what is required to operationalize those promising models.
Think of performance, scalability, adoption and trust before embarking on your AI journey
Ensuring that AI is right for your business requires a holistic approach, which is fundamentally based on four components:
- AI Performance – selecting and framing problems, with a view to demonstrate that what you build outperform traditional methods.
- AI Scalability – what starts as an experiment conducted by data scientists needs to be turned into a scalable system that truly impacts the business.
- AI Adoption – ensuring that your AI and analytics are embraced and used by consumers and businesses and, ultimately, change the way they make decisions.
- AI Trust – explaining decisions in a transparent way so the models and systems you build can be trusted, explainable and stand the test and scrutiny of regulators.
Leveraging an outcome-based approach to solve COVID-19 related business challenges
At Experian, we are applying this holistic approach to identify and address the most pressing concerns our clients are dealing within the context of COVID-19. The first is helping our clients understand what’s currently happening with different customer segments. We’re creating tools that bring together a series of early warnings and indicators and portraying how different customer segments are seeing various patterns in credit. We’re also identifying those most affected or needing concessions around lending, and understanding what banks are doing in terms of forbearance. Our priority is identifying these needs and quickly get the relevant AI and analytical solutions to our clients.
We are expecting to see a later urge in the industry to recalibrate existing models and to expand the type and volume of decisions they can make. Updating and monitoring them will be also a big area of focus over the next couple of years.
