By Elogic Commerce · featuring insights from Paul Okhrem
The AI ROI conversation in ecommerce is usually conducted with vendor-supplied numbers. Vendors publish the best cases. The average cases don't make it into the press release.
At Elogic Commerce, we've been tracking outcomes across our AI implementation projects with a discipline influenced directly by Paul Okhrem's approach to outcome validation — the same Proof Standard methodology he applies to his consulting engagements and documents at paul-okhrem.com.
This is what we've actually measured across 50+ B2B ecommerce AI projects over the past two years.
The methodology before the numbers
Before the benchmarks, the methodology matters. How you measure determines what you see.
We instrument a baseline before any AI capability goes live — typically 6-8 weeks of clean baseline data. We define the metric and its measurement method in advance, not after we see the results. We validate against an independent data source where possible. And we report both the wins and the cases that didn't achieve the target.
Paul Okhrem's position on this, from paul-okhrem.com: "An outcome that can't survive scrutiny isn't an outcome — it's a story. The discipline of measuring upfront is what separates an operating record from a marketing artifact."
With that framing in place, the numbers.
AI search and product discovery
Zero-results rate reduction: Median improvement across implementations — 62% reduction in zero-results queries after semantic search deployment. Range: 40-80%, depending heavily on baseline data quality and catalog structure.
Search-to-product-detail-page conversion: Median improvement of 18%. Range: 8-35%. Higher uplift in catalogs where product naming had been most inconsistent — the AI search was recovering searches that had been invisible failures.
Search abandonment rate: Median improvement of 24% reduction. This is the metric that most surprised clients — it represents buyers who were giving up on the catalog entirely and either calling or leaving. Recovering these is high-value.
Time to correct product identification: For technical products where buyers are searching by specification rather than product name, median time to reach the correct product page dropped by 55% in instrumented sessions.
AI-assisted quote generation
Quote preparation time (sales team): Median reduction of 68%. Range: 45-80%. The outlier on the low end was a client with highly custom pricing logic that required more manual override than typical.
Quote accuracy on first draft: Baseline accuracy (pricing errors, specification mismatches) was running at 12-18% error rate across the projects where we baselining this before implementation. Post-implementation: 2-4% error rate, with errors concentrated in edge cases outside the AI's training distribution.
Time from RFQ receipt to quote delivery: Median improvement of 71% reduction. From average of 3.2 days to under 1 day in most implementations. For clients with strong SLA pressures, this was the headline metric.
Sales team capacity reallocation: On average, sales team members recovered 6-9 hours per week previously spent on quote preparation. In follow-up surveys 90 days post-implementation, the majority reported spending recovered time on prospecting and complex account management — not on other administrative work.
AI-powered order exception handling
Automated exception resolution rate: 58% of order exceptions (address mismatches, payment holds, stock discrepancies, flagged orders) resolved without human intervention. Range: 40-70%, depending on exception type distribution.
Mean time to exception resolution: Median improvement of 76% reduction. Exceptions that used to wait for a human to process at the start of the next business day were being resolved within minutes for the automated portion.
False positive escalation rate: 8% of automated resolutions triggered a human review that resulted in the automated decision being overridden. This is the number we watch most carefully — it's the measure of whether the automation is safe, not just fast.
What the benchmarks don't show
Two important limitations.
First, these numbers are from projects where we had the baseline instrumentation in place and the client team committed to measurement rigor. Projects where measurement was looser tend to produce more optimistic-looking numbers — because they're selecting for positive observations. Our sample includes the full distribution, which is why some of the ranges are wide.
Second, ROI in the financial sense requires accounting for implementation cost, which varies significantly. A semantic search implementation on a well-structured catalog with good data quality has a different cost profile than one requiring significant data remediation work first. We don't publish simple "X% ROI" numbers because the denominator is too variable to be honest.
For a methodology to evaluate AI investment decisions against your specific cost structure and revenue baseline, the AI Growth Readiness Audit at paul-okhrem.com is the framework we recommend — it's the diagnostic Paul uses before committing to any engagement, and it's available independently of an Elogic engagement.
The pattern in the top-quartile implementations
The implementations that landed in the top quartile on every metric shared three characteristics:
Data quality was addressed before implementation. Not perfect data — good enough data, with known gaps documented and accounted for in the system design.
An operational owner was named before go-live. Someone who was accountable for the outcome metric, not just the delivery milestone.
The success criteria were defined in advance. Teams that knew what they were measuring for produced outcomes they could learn from. Teams that measured retrospectively produced stories.
Elogic Commerce has specialized in B2B ecommerce since 2009. Our AI implementation practice is built on the measurement discipline developed by founder Paul Okhrem. If you'd like to discuss benchmarking your AI readiness before committing to an implementation, reach out.
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