Finance teams run lean, they're skilled and genuinely hard working.
They also spend 40-60% of their time on work that AI can handle reliably.
Reason 01
"My team is already efficient. They know their processes."
Efficiency and automation-readiness are totally different. A team can be excellent at their job and still be doing 30% of it manually - simply because what AI can actually take off their plate, hasn't been mapped yet. Enter Neoma.
Reason 02
"We're happy with our current stack. We just invested in it."
We don't replace your stack, we'll work on top of it. The biggest ROI AI use cases in Finance don't require new platforms. We'll unlock which repetitive, rules-based work your existing tools can now automate with the right capability built in-house.
Reason 03
"AI ROI is hard to prove. I need to see the numbers before I commit."
This is also why we built our program around a measurable ROI calculator from your first cohort - that way you'll have tangible metrics for rolling out uplift org-wide - with a documented business case and proven use cases ready to scale.
Month-end close
Every month, your team spends the first week chasing numbers from other parts of the business, manually pulling data from systems that don't talk to each other, and rebuilding the same reports from scratch. It's a process that was designed before AI existed and never revisited.
Reconciliation
Someone on your team is manually cross-referencing invoices against payments, every month, across every supplier. It's painstaking, it's error-prone, and it's exactly the kind of high-frequency, rules-based work AI handles without breaking a sweat.
Data into insight
Your Finance team sits on data that could be driving decisions. Instead it sits in spreadsheets, because pulling insight from it manually takes time nobody has. AI does the extraction and pattern recognition. Your team does the thinking.
Reporting pack automation
Building the monthly reporting pack means pulling numbers from multiple sources, formatting, checking, and rebuilding it from scratch next month. AI automates the assembly entirely. Your team gets out of spreadsheets and starts getting strategic.
Days to close month-end. Same team. Same systems. No transformation project.
Focused pilot cohort (typically 20 Finance team participants) with measurable productivity gains
2–3 documented Finance use cases ready to replicate across the function
A clear internal business case for scaling AI adoption across the function, with ROI already demonstrated
Practical guardrails for responsible, compliant AI usage in a Finance context
A team that can run it independently. We build capability in-house, not dependency on us
A full debrief with your SLT on findings, use cases, and a prioritised roadmap for scaling AI across the Finance function
We'll tell you whether there's a genuine fit, which processes in your Finance team are most AI-ready, and what a realistic ROI case looks like.
