
"Lenders are under unrelenting pressure to cut costs, accelerate processes and enhance borrower experiences while navigating a less-than-certain originations environment. Artificial Intelligence (AI) has emerged as a tool to meet these demands, yet quantifying its business value can be elusive. In a 2024 Gartner survey of more than 600 organizations, nearly half said their number-one barrier to adopting AI was the difficulty of estimating and demonstrating its business value. This outweighed concerns over a lack of talent, data or even trust in AI."
"Gen AI charges per use, using tokens to measure use. Roughly translating to four characters of English text, token use can vary wildly based on use case, and even within a use case. Using a chat use case as an example, even a short input from a user can generate thousands of tokens once you add in all the extra components of the process needed to ensure a reliable and relevant response like system prompts, context, guardrails and the response itself."
"One reason it's hard to pin down AI's ROI is the way AI costs are structured. Lenders are used to working with Software as a Service (SaaS) offerings, which tend to have predictable pricing, often aligned to a price per loan or, like in the case of Dark Matter's Empower loan origination system (LOS), a price per closed loan. Loan-based pricing is easy to estimate and align to Gain-on-Sale calculations."
Lenders face pressure to cut costs, accelerate processes and improve borrower experience amid uncertain originations. AI offers potential benefits but delivering measurable business value remains challenging for many organizations. A 2024 Gartner survey found nearly half of respondents cited difficulty estimating and demonstrating AI's business value as the top barrier. Lenders are accustomed to predictable SaaS pricing tied to loans, which simplifies ROI and gain-on-sale calculations. Generative AI instead uses per-use token pricing, causing wide variance by use case and user behavior. Chat applications can incur thousands of tokens due to prompts, context, guardrails and responses, making costs unpredictable.
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