No accountant. No controller. No FP&A analyst
This is the shift you can be ready for (evidence based)
JPMorgan Chase’s COiN platform reduced legal review time from 360,000 hours per year to seconds. Their internal LLM suite now supports 60,000 employees, drafting reports, analyzing risks, and writing code.
Their fraud monitoring AI has prevented $1.5 billion in losses.
That’s not automation. That’s initiative.
So here’s the real question:
What happens when AI starts to work before your team even logs in?
You can’t manage that with more approvals or tighter oversight.
You need something stronger: trust.
Why the next CFO playbook starts with trust, not control
Greg Wookey, CFO of Wizehire, doesn’t believe in micromanagement. He builds around high-trust autonomy.
We know we can get the job done, he says.
The people focused on strategy are free to do it.
That mindset unlocks performance. Instead of wasting time proving their value, Wookey’s team builds it. They’re not frozen in fear. They’re empowered by clarity.
Research from AICPA & CIMA confirms it: Cultures built on coercive controls, tight rules, and top-down sign-offs destroy judgment and initiative.
Too many controls damage motivation.
But clear goals, aligned values, and autonomy motivate teams to experiment.
CFOs must recognize that AI is powerful and you must play with it.
So while caution is wise, paralysis is costly. AI is here. It’s reshaping workflows, strategy, and expectations. And your competitors are already building with it.
You can experiment with AI without uploading your sensitive data.
Thanks for sharing, Wouter, but where’s the proof?
Sometimes the clearest evidence of AI’s impact isn’t theoretical.
It’s operational and measurable.
A financial services firm managing accounts for 45,000 customers across 35 countries was growing fast. But their core credit process, spreading financial statements, was still being done manually.
Too many formats. Too much copy-paste. Too many errors.
Application-to-funding cycle time had ballooned to 8 days.
They needed to do things differently.
They needed to scale without sacrificing quality.
They needed consistency, speed, and automation.
So Genpact, a global services firm that is at the forefront of leveraging agentic AI to change finance operations, helped them automate 80% of their financial spreading process across 24 countries, using Cora LiveSpread, an AI-powered platform built on Genpact Cora.
Here’s how Agentic AI works
Extraction: Patented AI models pulled data from spreadsheets, PDFs, and even images, down to the footnotes.
Normalization: The system formatted data by region, language, and reporting standard, removing inconsistencies that had been bottlenecks for years.
Audit-Ready: Every step was logged. Every data point is traceable. A big data repository tracked everything for compliance and risk teams.
Deployment: They delivered a ready-to-use portal. Full rollout in under 2 months.
The results?
80% of statements automated
Global consistency across 24 countries
70% improvement in process efficiency
84% reduction in time spent spreading numbers
100,000+ statements processed across 35 countries
Application-to-funding time dropped from 8 days to 48 hours
Credit decisions got faster. Errors went down. Compliance tightened.
The team stopped chasing formats and started making better decisions.
This is what agentic AI looks like when done right.
AI isn’t about firing people, it’s about better use of talent
Energy Transfer is a $78B energy company with 700 accountants, 250 legal entities, and a month-end close that used to take up to 8 days.
Each month:
4,000 journal entries
3,500 close tasks
10,000 balance sheet certifications
Most accountants spent 80% of their time wrestling with data and only 20% using it.
Their goal? Flip that ratio.
They turned to Redwood’s Finance Automation, focusing on journal entries, task management, and balance sheet certification, all integrated with SAP.
In just 18 months, they automated 150 bank reconciliations (dropping from 60 minutes to 7), provisioned SAP user access in 10 seconds (vs. 20 minutes), and standardized asset settlements across a sprawling M&A-heavy org.
They retired 2 legacy systems (Blackline and Winshuttle), improved audit readiness, and built space for their people to grow into analytical roles.
We didn’t automate tasks. We automated processes, says Jeff Bankert, Assistant Controller.
This wasn’t task elimination. It was skill elevation.
The goal wasn’t to downsize. It was to free high-value talent from low-leverage work.
KPMG’s research shows this process is becoming the norm.
55% of finance executives don’t expect AI to shrink teams.
They expect AI to unburden teams.
Use AI like your second brain. Think with it.
Stop using AI like Google and don’t rely on default settings.
How to set up your ChatGPT to think like a CFO in 10 minutes
Custom instructions are your secret weapon.
Bridgewater Associates launched a $2B AI-native fund
Finance has always leaned on hindsight.
But agentic AI flips that on its head.
Bridgewater Associates launched a $2B AI-native fund.
It’s not an experiment. It’s live capital.
The fund was incubated inside the flagship Pure Alpha strategy and now runs independently, driven by systems developed at AIA Labs (Artificial Investment Associate Labs).
Led by Co-CIO Greg Jensen, AIA Labs combines proprietary models with external LLMs from OpenAI, Anthropic, and Perplexity.
This is not surface-level automation. It’s a system that:
Models causal relationships
Reads global macroeconomic signals
Simulates policy outcomes across countries
Makes investment decisions without human PMs
This is maybe the most significant and pure manifestation of the moment we’re in, says Bridgewater CEO Nir Bar Dea.
The technology already answers questions like
What happens if Trump wins and raises tariffs?
What’s the impact of Fed tightening on bond spreads?
Greg Jensen, who backed OpenAI and Anthropic early, said this leap would change not only funstrategy , but also Bridgewater’s hiring philosophy: more scientists, fewer traditional PMs.
But there are checks in place. Humans still oversee:
Risk management
Data acquisition
Trade execution
Machines don’t know what greed is, what fear is, says Jensen.
That’s where we still lead.
Their insight for CFOs is simple:
You can’t just layer AI on top of legacy systems.
You have to own the data, own the infrastructure, and rethink how decisions are made.
This is where the role of the controller evolves, from policy enforcement to systems design.
A list of Real World AI use cases in finance by Google
Access the full list of AI use cases here.
These aren’t stories about finance alone.
They’re about how finance is becoming a partner to the rest of the business, deploying AI to serve stakeholders, not just the back office.
The Bottom Line
So what happens to the CFO, the controller, and the accountant?
The manual, reconciliatory layer of finance is disappearing.
But the human layer
The insight
The judgment
The strategy is more important than ever
CFOs won’t be the ones chasing receipts.
They’ll be the ones shaping companies that think with AI.
Accountants become analysts.
Controllers become systems designers.
Ops teams become operators of autonomous workflows.
This isn’t augmentation. It’s redefinition.
Agentic AI is already live.
It’s building board decks. Spreading financials. Reconciling balance sheets. Flagging anomalies.
And it doesn’t replace people.
AI replaces whatever slows it down.
If your finance org is built on approvals and fear, it will break
If it’s built on trust, systems, and clarity, it will scale.
The CFOs who understand these principles won’t just use AI.
They’ll build orgs that are ready for it.
And that’s the game now.
And that’s all for today.
See you on Sunday!
P.S. The CFO using AI won’t take your job
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I’m Wouter Born. A CFOTech investor, advisor, and founder of finstory.ai
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