Built by someone who felt the problem
AI is transforming businesses everywhere. But almost nobody can prove it's actually working.
When AI started reshaping how we work at ACA Ethos, I was excited. We were moving fast — new tools, new workflows, new possibilities. 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 measure what was working.
The tools to run AI projects exist. What was missing was the infrastructure to measure them — to connect spend to value, track realised ROI over time, and give executives the visibility to make better decisions about where to invest next.
That's why I built Roiva.
Luke Wilcox
Partner, Head of Ethos · ACA Group
Luke spent five years at McKinsey before founding Ethos ESG in 2019 — an ESG data, analytics, and reporting platform serving financial advisors and institutions. Ethos was acquired by ACA Group in 2022, where Luke leads 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
Measurement is the missing layer
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 realised 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. The measurement loop is what turns AI experiments into compounding advantage.
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