Built by someone who felt the problem
AI is transforming businesses everywhere. But almost nobody can show what it returned in a way their CFO will accept.
After five years at McKinsey, I built and scaled a data platform for financial advisors. When AI started reshaping how we worked there, I was excited — new tools, new workflows, new possibilities, moving fast. But when it came time to answer the question everyone eventually asks — "Is this actually paying off?" — I didn't have a good answer.
We had spreadsheets. We had gut feel. We had anecdotes. What we didn't have was a clear, defensible picture of what AI was costing us, what it was saving us, and where it was actually moving the needle on revenue.
I started talking to other operators — heads of technology, finance leaders, innovation teams at mid-market companies and large financial services firms. The story was the same everywhere. Companies were running five, ten, twenty AI initiatives simultaneously. Almost none of them had a structured way to know what was working.
The tools to run AI projects exist. What was missing was a financial record of them — one place that connects spend to value, tracks realized ROI over time, and gives executives a defensible basis for deciding where to invest next.
That's why I built Roiva.
Luke Wilcox
Founder, Roiva · Ex-McKinsey
A former McKinsey consultant, Luke founded Ethos in 2019 — a data, analytics, and reporting platform serving financial advisors and institutions. Ethos was acquired by ACA Group in 2022, where Luke led the Ethos platform as a Partner.
The experience of building and scaling a B2B data platform — and then watching AI fundamentally reshape how that platform works and how clients use it — gave Luke a front-row seat to both the promise and the measurement gap of enterprise AI adoption.
What we believe
AI needs a number you can defend
Visibility beats instinct
Good decisions about AI require real data. Gut feel and anecdote aren't sufficient when CFOs start asking questions.
ROI is not a vanity metric
We care about realized value — actual dollars saved or earned — not usage stats, adoption rates, or AI maturity scores.
Measure, learn, reinvest
Knowing what's working tells you where to invest next. A shared record of what each initiative cost and returned is what turns AI experiments into compounding advantage.
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