The Nymbral Blog
Field notes on measuring AI ROI.
Essays, research, and methodology pieces from the team defining how enterprises account for AI investment.
Self-Reported Versus Independently Verified: Two Studies, One Year, Two Very Different Realities
Why Everything We Build Is Named After an Eclipse
The Vendor Grading Its Own Homework
Everyone Is Selling AI ROI Now. Almost No One Can Define It.
Gartner Keeps Publishing Abandonment Numbers. We Think They’re All Describing the Same Problem.
The 95% Problem: What MIT’s GenAI Divide Study Actually Means for Your P&L
The AI Subsidy Is Ending. Know What You’re Paying For.
The AI ROI Score, explained: how we grade investments from 0 to 150
Measuring What Your AI Is Actually Worth
The Five Questions Every CFO Is Asking About AI
Adoption Is Not Usage (And Why It Matters)
The Eclipse Method: From Research to ROI
Building an AI Renewal Review That Isn’t Theater
A Practical Framework for Attrition Value Modeling
From LinkedIn
Short-form notes from the feed.
Quick takes on AI ROI methodology and vendor evaluation, posted as Nymbral by Nimbus Minds on LinkedIn.
Six criteria we use to separate a real AI ROI solution from a dashboard wearing the name: full-stack coverage across every vendor and contract, a defensible per-vendor methodology, and results that hold up in front of the board and an auditor.
Before a renewal conversation starts, you need numbers the vendor didn't produce. Defensibility comes from an independent methodology, research-backed baselines, per-vendor attribution, and an audit trail every figure can be traced back to.
The real AI ROI question isn't just cost saved — it's capacity unlocked. Research-adjusted baselines put the AI-recoverable share of a role's time in a conservative 20–35% range, depending on function and adoption maturity.
A dashboard tells you what happened. A real AI ROI solution tells you what to do next — expand, renegotiate, cut, or pilot — with a strategic playbook a CFO can act on, not another chart to interpret.
If a vendor's answer to “how do you measure ROI” is “we track usage and attribute it to value,” that's not a methodology — it's a marketing sentence. Here's the question set we use to pressure-test AI ROI claims before trusting them.
The first test for any AI ROI solution is scope. If it only covers one vendor or one use case, it isn't measuring your AI investment — it's measuring a slice of it and calling that the whole picture.
Most of what's sold as “AI ROI” today is an activity dashboard, a single-tool estimate, or usage analytics with a new label. Kicking off a series on what a genuine AI ROI solution actually has to do.
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