We have spent three years clean-tagging our operational data to train a proprietary AI model that automates our scheduling, but the buyer's Quality of Earnings team is treating this data as a zero-value intangible asset. How do we prove the tangible economic value of our data pipeline to force a multiple adjustment?

Question

Tyler Smith · Accepted Answer

Traditional Quality of Earnings providers often struggle to value proprietary data pipelines because they lack a framework for modern, AI-driven operations. To force a valuation adjustment, you must quantify the tangible economic benefits your data model produces. Do not let them treat your training data as a vague intangible asset. Instead, show how this data directly drives your high operating leverage and EBITDA margin. Structure your argument around the cost savings and efficiency gains. Document the exact process of how your AI model automates workflows. Compare your current labor costs to the industry standard headcount requirements for a business of your size. This is where your EOS® Scorecard comes in. Pull your historical metrics to show the dramatic reduction in customer onboarding times and ticket resolution rates since the AI model was deployed. Prove that this data pipeline creates a defensible competitive moat that a buyer cannot easily replicate. By showing that your AI operations produce durable, higher-margin cash flows that require less working capital, you can leverage IVS 105 principles to argue for a premium multiple based on your superior operational efficiency.