Stop chasing smarter AI. Build the Agentic Finance Office
AI models are hitting a wall. They're smart on paper, but in the trenches of finance they fail.
CFOs won’t lose sleep worrying that AI isn’t autonomous enough. They’ll lose sleep worrying that it won’t be accurate, repeatable, and auditable.
New generation of LLMs offers smaller gains.
We’re hitting diminishing returns. Simply throwing more data and compute at a single giant brain isn’t producing breakthroughs.
AI models are hitting a wall.
Even worse, these all-in-one models are super untrustworthy for finance.
Raw intelligence doesn't cut it… when data's messy, regulations bite, and humans demand trust.
In today’s edition, I’ll help you understand how general AI is not built for finance and give you a glimpse of the future of the finance playbook and the role of AI agents.
The future of finance isn't about one giant, smarter AI.
The future of finance is about applying AI in controlled, structured ways.
This is how you transform finance from a cost center to a strategic powerhouse.
Let’s dive in.
Previously, I built a full interactive dashboard for the next board meeting as a working, production-grade website in 10 minutes with financial storytelling using ChatGPT Agent.
How I used ChatGPT Agent to build an interactive Board Dashboard in 10 minutes
Financial storytelling is changing.
General AI is not built for finance
Big, general AI models (like GPT-5 are impressive. But they fail in finance. Why?
They hallucinate. Even a tiny error (0.01%) is a disaster in finance. General models often make things up or get facts wrong. They misclassify income, confuse transfers with purchases. Precision isn't optional here.
They're slow and expensive. Finance needs real-time answers. General models are heavy, slow, and costly. Not practical for high-volume tasks like fraud detection.
Compliance is a nightmare. Cloud-hosted general AI means more security risks and legal headaches. Data leaks, unauthorized access—these lead to huge fines and lawsuits.
The problem isn't AI's intelligence. It's the design.
You need specialized tools, not a Swiss Army knife for brain surgery.
The Playbook for future ready finance
Instead of one mega-AI that tries to do everything, finance must move toward agentic frameworks. AI teams where each agent has a role, and their work is coordinated and checked.
We’ve seen this shift already in software engineering.
Harry Wang of Sonar put it clearly:
A generalist ‘coding agent’ is not enough. Just like a human team has specialists—back-end, security, and testing engineers—agentic systems will require multiple specialized agents.
Eran Yahav, CTO of Tabnine, adds:
Think of it like a high-performing engineering team. One agent writes code, another tests it, a third validates it, and a fourth checks for security and compliance.
Now replace code with financial reporting.
One agent checks the raw data.
Another agent drafts the variance analysis.
A third writes the management narrative.
A fourth ensures compliance with accounting standards.
The human finance leader.
Just like an engineering lead, reviews and signs off.
The result?
Fewer mistakes, faster cycles, and scalable trust.
Software teams didn’t get here overnight.
They went from lone coders with some AI auto complete to vibe coding with AI writing chunks of code to multi-agent CI/CD pipelines where specialized bots run tests, security checks, and deployments automatically.
Finance is still in autocomplete or at best vibe-coding mode with AI:
Task helpers like ChatGPT draft memos.
Simple variance commentary or Excel formulas.
But brittle, manual, and hallucination-prone.
The next step is finance agent teams, just like engineering pipelines:
Controller Agent: Handles close & reconciliations.
Data Quality Agent: Flags gaps, communicates with remote teams, enforces consistency.
FP&A Agent: Builds forecasts, runs backtests.
Variance Agent: Runs variance analysis, communicates with managers to identify root causes.
Compliance Agent: Checks policy & PII.
Storytelling Agent: Prepares board-ready narratives.
Orchestrator Agent: Sequences workflow & ensures approvals.
This isn’t futuristic. Tech vendors are already enabling it.
Salesforce’s Agentforce, UiPath, and startups like Moveworks are letting enterprises build multi-agent workflows.
Finance leaders can leverage the same playbook.
People say AI can’t solve real finance problems.
0.01% hallucination is enough to destroy trust. That’s valid.
But the agentic approach is designed for this.
One agent checks the work of another.
Supervisor agents reconcile data.
Every action is logged.
Humans remain in the loop for final decisions.
Consider Navan, which runs a multi-agent AI assistant for travel and expense. Over 2 years, its agents processed the workload of hundreds of human agents with zero critical errors, because every action was checked and validated.
This is the mindset finance must adopt:
Determinism over flash.
Guardrails over autonomy.
Boring but reliable over bold but risky.
Think about where we are now.
We’re not at the autonomous auto stage.
We’re at cruise control. You still need hands on the wheel.
The Bottom Line
The future of finance AI won’t be one super-intelligent model doing everything.
It will be a team of specialized AI agents, orchestrated with the same rigor as a finance team.
This is how finance finally gets trustworthy, scalable AI:
Not by asking for a smarter model.
But by building a structured AI team with guardrails.
The winners won’t be those who wait for perfect models.
They’ll be the finance leaders who start experimenting with agentic AI today.
Safely, step by step.
In finance, a dependable AI team beats a solitary genius model every time.
And that’s all for today.
See you on Thursday!
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I’m Wouter Born. A CFOTech investor, advisor, and founder of finstory.ai
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