Aryan Mahajan, an AI Architect for B2B and Capital-Intensive Firms, recently posted something that made me stop scrolling: "CFOs are still paying analysts $40/hour to do work AI does in 8 minutes." He added the part that really stings: leaders then "wonder" why insights arrive "3 weeks after the decisions were already made."

That framing is blunt, but it is also useful because it shifts the conversation away from tools and toward operating model. Aryan is not simply saying "use AI". He is arguing that many finance teams are forced into a slow, manual rhythm because the organization treats analysis like a monthly reporting task instead of a continuous capability.

Below is my expanded take on what Aryan is pointing to, why it matters, and what a practical finance intelligence system looks like when you move from spreadsheet grind to always-on decision support.

The real problem is not effort, it is latency

Aryan wrote that the bottleneck is not your finance team. I agree. Most finance teams I have worked with are already running at capacity, and the issue is not competence. It is the structural lag created by:

Data scattered across ERP, accounting tools, billing, payroll, and transaction logs
Manual extraction, cleanup, and reconciliation
Spreadsheet chains with fragile logic and limited auditability
A monthly cycle that turns "analysis" into a rearview mirror

When your process is built around month-end, every question becomes a project. By the time the answer lands in an inbox, the decision window has closed.

What Aryan means by a "continuous data layer"

One line in Aryan's post is the key: teams are "treating financial analysis like a reporting job instead of a continuous data layer." In plain terms, that means finance should operate more like an always-available service:

Data is connected once, then kept fresh automatically
Variances are detected as they emerge, not after close
Scenarios can be run on demand with consistent assumptions
Outputs are packaged for different audiences (operators, CFO, board)

Key idea: stop rebuilding the same analysis every month. Build the layer once, then let it run continuously.

This is not a nice-to-have. In volatile environments, the value of finance is proportional to speed and reliability, not the number of hours spent.

What a finance intelligence system actually does

Aryan listed the system behaviors clearly. Let us expand each one into what it looks like in practice.

1) Connects to ERP, accounting software, transaction logs

This is the unglamorous work that determines everything downstream. A real deployment typically includes:

ERP (NetSuite, SAP, Dynamics)
GL and accounting (QuickBooks, Xero)
CRM and pipeline (Salesforce, HubSpot)
Billing and subscriptions (Stripe, Chargebee)
Bank feeds and payment processors
Data warehouse or lake (if present)

The goal is not to copy everything everywhere. The goal is to establish governed, repeatable access to the metrics that drive decisions: revenue, gross margin, cash, working capital, headcount, utilization, and unit economics.

2) Runs real-time scenario modeling and variance detection

This is where finance stops being a reporting function and becomes an intelligence function.

Variance detection: When actuals diverge from plan, the system flags it, attributes drivers (price, volume, mix, churn, timing), and routes the alert.
Scenario modeling: Instead of one annual budget spreadsheet, you maintain a scenario engine that can answer, "What if we cut CAC by 10 percent?" or "What if DSO rises by 7 days?" within minutes.

Crucially, scenario modeling is only trusted when assumptions are consistent and traceable. Otherwise you just accelerate confusion.

3) Outputs board-ready reports, anomaly alerts, strategic recommendations

Aryan mentions "board-ready" outputs. That implies three qualities:

Consistency: same definitions, same rollups, same reconciliation logic
Explainability: not just a number, but drivers and narrative
Distribution: the right view to the right stakeholder at the right time

Anomaly alerts are the early-warning layer. Recommendations are the hard part. A good system does not pretend to be a human CFO, but it can:

Suggest likely drivers based on patterns (for example, margin drop tied to freight, refunds, or discounting)
Propose next questions (for example, drill into cohort, region, product line)
Provide decision-ready options (for example, best, base, worst scenarios with cash impact)

4) Zero manual Excel work, zero month-end delays

This line is provocative, and it is worth interpreting carefully.

I do not think the point is that Excel disappears. The point is that Excel stops being the production system. When spreadsheets are the source of truth, you get version chaos, broken formulas, and institutional knowledge trapped in one analyst's laptop.

In a modern setup, spreadsheets can still exist, but as optional front-ends for exploration, not as the engine that produces the company narrative.

The "8 minutes" claim and what it really represents

Aryan says work that took 40 hours monthly can happen automatically in 8 minutes. Whether your number is 8 minutes or 80 minutes, the breakthrough is the same: the marginal cost of producing an updated view approaches zero.

That changes behavior.

Operators stop hoarding decisions until month-end.
Finance stops batching analysis.
Leaders can ask more questions because answers are cheap and fast.

Speed alone is not the point. Speed plus reliability is.

A practical stack: engine, automation, and narrative outputs

Aryan shared a simple stack:

Nano Banana Pro (financial intelligence engine)
n8n (workflow automation)
Gamma (reporting outputs)

Even if you use different tools, the architecture is sound because it separates concerns:

Intelligence engine: metrics, models, variance logic, scenario logic
Automation layer: scheduling, triggers, approvals, notifications, data sync
Output layer: board decks, dashboards, narratives, and alerts

This separation matters because most failures happen when everything is jammed into one place. Either you end up with a dashboard no one trusts, or a spreadsheet army keeping the lights on.

This is not about cutting headcount

Aryan is explicit: "This isn't about cutting headcount." That is the right message to lead with, because otherwise the initiative dies in politics.

The best outcomes I have seen look like this:

Analysts spend less time reconciling and more time partnering with the business
FP&A moves from historical commentary to forward-looking guidance
CFOs get earlier signal on cash, margin, and risk
Boards get cleaner narratives with fewer last-minute fire drills

In other words, automation buys back attention. The company then decides how to reinvest it.

Implementation: what I would do in the first 30 days

If I were advising a CFO who resonated with Aryan's post, I would not start with a huge transformation program. I would start with a narrow loop that proves value.

Week 1: Pick the highest-leverage use case

Examples:

Daily cash and runway with variance to plan
Weekly revenue and pipeline conversion with driver analysis
Margin bridge (price, volume, mix, costs) updated continuously

Week 2: Lock definitions and data lineage

Write down metric definitions and owners. Decide what is authoritative. Make reconciliation explicit.

Week 3: Build alerts and narratives, not just charts

Charts are not decisions. Alerts and short narrative explanations drive action.

Week 4: Package board-ready output

Build the recurring artifact: a weekly exec brief and a monthly board pack that are generated from the same logic.

The most convincing demo is a report that refreshes itself, matches the GL, and explains the delta.

The finance function you get on the other side

Aryan's north star is "strategic intelligence that surfaces opportunities the second they appear." That is exactly the point. When finance becomes a continuous layer, you stop asking, "What happened?" and start asking, "What should we do next, and what will it do to cash and margin?"

Finance will always require judgment. But judgment improves when the data is timely, consistent, and easy to interrogate.

This blog post expands on a viral LinkedIn post by Aryan Mahajan, AI Architect for B2B & Capital-Intensive Firms | Fortune 500 Growth & Capital Efficiency. View the original LinkedIn post →