Your AI chat prompt is just step 1.
A forecast is only as good as its slowest input, and yours is still a spreadsheet someone hasn't updated since Tuesday.
10 years in business. All work guaranteed.
By now, most finance teams have the close down to a routine. That was never really the question.
Say you're the CFO. The board wants the Q3 forecast by Friday. Half your inputs are still sitting in a colleague's spreadsheet, another chunk lives in the ERP, and the pipeline number depends on whichever rep last updated the CRM.
Every forecast starts with the same scramble: pulling numbers from five systems that were never built to talk to each other.
Which means you're not forecasting off real-time data. You're forecasting off whatever was current when someone last exported it.
And a forecast built on stale inputs is already wrong before anyone reads it.
By the time the board sees the number, it's already a snapshot of last month, not a picture of what's next.
An AFP and APQC study found finance professionals spend just 25% of their time on real analysis. The rest goes to gathering data and administering processes. That's the gap between a team that reports the numbers and one that explains them.
Average pace
0
Extra days added to every close, versus top-performing finance orgs closing in 10 days or less
0
Days per year your team spends finishing the books instead of forecasting what's next
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Annual cost of operating below top-performer close speed
Based on APQC benchmarking of finance close cycles: top performers close in 10 days or less, the median organization takes 18, and manual-reconciliation-heavy teams average 35. Cost estimate assumes a typical mid-market finance team of 8 (per GrowCFO and CompanySights benchmarking) at $90/hr average fully-loaded cost, 8 hours per lost day.
This isn't just a few extra days on the calendar. Every day beyond top-performer pace is a day your team spends verifying numbers instead of advising on what they mean, and that gap is exactly what separates a finance function that reports the past from one that shapes what's next.
Here's the shift that actually closes the gap:
From
To
Which side of this shift are you on?
Share your work email and we'll show you what closing this gap looks like for your finance team.
Video placeholder: CEO talk track on Finance Intelligence, approximately 45 seconds
If a past AI effort didn't stick, that's not a reason to wait. It's usually a sign no one diagnosed the right starting point first.
Our edge isn't more automation. It's activating the human advantages AI can't replace. Practically, that means we diagnose before we build, never the other way around.
Put AI to work for your people.
The Rumble is a fixed-price engagement that generates an AI activation roadmap, then tells you whether you need Guidance, Execution, or both.
Single Core Function
$10,000
Pinpoint one Intelligence domain and roadmap where to activate AI first.
Recommended
Business-Wide Diagnosis
$25,000
A full cross-functional view, prioritized by impact.
Coming out of the Rumble, you'll know exactly which comes next:
Case Study
[Client Name]
[One or two sentence summary of the client's challenge and how Finance Intelligence solved it.]
An AI system that turns fragmented financial data into a single, structured source of truth: verified, current, and available to every finance function, from FP&A to the close. Financial knowledge becomes infrastructure. Modeled. Forecasted. Trusted.
Real-time financial modeling
Every forecast draws from live, verified data across every system, updated automatically as your business changes, not just when someone remembers to refresh the export.
The ProblemEndless manual reconciliation. Countless forecasts already stale before the meeting starts.
Automated close acceleration
Reconciliation, variance analysis, and reporting run continuously in the background, so your close compresses from days to hours.
The ProblemEndless spreadsheet chasing. Countless hours spent proving the numbers are right instead of acting on them.
Institutional forecasting memory
Every assumption, adjustment, and modeling decision your best analyst ever made stays captured and searchable, so forecasting logic survives turnover.
The ProblemEndless forecasting knowledge lost to turnover. Countless models rebuilt from scratch because no one remembers the old assumptions.
The fragmented-data problem on this page isn't unique to Finance. It shows up everywhere else in your business, and costs growth in each one. Explore where AI activation moves you forward next.
01
Company Intelligence
Activate the knowledge layer your AI is missing.
02
Finance
Activate the forecasting layer your finance team is missing.
03
Sales
Activate the revenue layer your sales team is missing.
04
Marketing
Activate the attribution layer your marketing team is missing.
05
HR
Activate the workforce layer your HR team is missing.
06
Operations
Activate the process layer your operations team is missing.
07
Engineering
Activate the context layer your engineering team is missing.
08
IT
Activate the infrastructure layer your IT team is missing.
09
Security
Activate the posture layer your security team is missing.
Ready to put AI to work for your people?
Let's get started!