Can you share some strategies for measuring the value and impact of AI initiatives in your organization?
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We're also seeing opportunities for top-line growth through improved customer experience and differentiated capabilities enabled by generative AI. Companies are investing in data monetization opportunities, although it's still early days for capturing the full value of these initiatives. We're seeing early wins in experience and personalization, particularly in the manufacturing sector. However, these opportunities come with risks, particularly around the security and safety of these models.<br>
A specific example from my experience would be increasing the auto acceptance rate for our matching algorithm. By increasing this rate by 1.2%, we could bring in $1.1 million in incremental revenue annually. We first tested this in a lab and then in the wild through A/B testing. We found that it has the potential to bring in significant revenue, assuming the business landscape remains stable.
We focus on solutions that are meaningful to the board of directors, which usually involve moving the needle on EBITDA. This typically means either revenue growth or cost reduction and operational improvements. AI can also be used internally for marketing, brand reputation, and compliance. However, measuring the ROI of these accelerators and productivity measures can be challenging. For instance, how do you measure the financial impact of avoiding part-time help because you have AI? Despite these challenges, there are use cases for generative AI algorithms that displace major portions of work, resulting in extreme benefits on a company's bottom line. These savings can be easily demonstrated by either cost reduction or increased productivity.