I’ve spent 25 years fixing finance from the inside
I'm Jessica Green. I've led finance, FP&A and commercial finance in organisations from $20 million in revenue to multi-billion-dollar global businesses, across pharma, FMCG, banking, property technology and SaaS.
Different industries, different sizes, mostly the same problems.
The thing I kept noticing
Capable people losing days to reporting nobody reads. New systems layered over broken processes. Nobody trusting the data, but everyone too busy producing next month's numbers to go back and fix the reason it keeps happening.
Finance teams who wanted to be more commercial and couldn't get there, because month-end had already taken the time and the energy required to do it.
These weren't teams short on intelligence or effort. They were trying to run the function and repair it at the same time, usually without the mix of finance, systems, data and change capability that job needs.
I've seen and tackled versions of this inside listed companies, private-equity-backed businesses and a Big Four bank, and the pattern held every time.
That's what jessic.ai is for.
How I got here
I started in investment management in New York, which gave me a strong commercial grounding, an unreasonable work ethic and more jokes than any finance biography requires.
When I moved to Australia I wanted to work inside businesses rather than analyse them from the outside. An early role dropped me straight into a full ERP and CRM implementation, which taught me two things that have held up ever since: a unique identifier is sacred, and a cross-functional project without finance in the room is asking for trouble.
Everything since has sat at roughly the same point, where finance, systems, people and commercial decisions run into each other.
My full career history is on LinkedIn.
I bring executive judgment and the technical capability to build the solution.
The judgment stays close to the work
I've worked with CEOs, CFOs, boards and executive teams on pricing, investment, product mix, customer profitability, planning and transformation.
I've also built the models, designed the Power BI reporting, repaired the data, improved ERP processes, led migrations and assessed automation.
It means less translation between the people who understand the business and the people who understand the technology, and fewer handovers where the context quietly disappears.
It also means I don't think a solution is finished when the model works or the system goes live. It's finished when the people using it can run the process, trust the output and make a better decision with it.
Where AI fits
I've spent the past two years working out what AI can do in practice, testing where it helps and paying close attention to where the claims are running ahead of the evidence.
Finance doesn't need AI inserted into every process. It needs people who understand the work well enough to know when AI extends the team's capacity, when simple automation is enough, and when the process underneath should be fixed before anyone buys anything.
That's why I built Confidence with Claude. Handing people access to enterprise AI doesn't hand them the judgment to use it well, and leaving everyone to work it out alone is a fairly expensive adoption strategy.
What I'm like to work with
I ask a lot of questions, because the obvious problem isn't always the one doing the damage.
I'll tell you when a tool is worth buying, and I'll tell you when it's an expensive way to move the work somewhere else.
I work with the people who use the process, because they know where it breaks, which workarounds have quietly become normal and which very impressive proposal will collapse in week two.
I document everything, because the fix shouldn't live in my head, and I don't count it as finished if the team is left afraid to touch what I built.