The Swivel Test: How To Find Friction That Causes Chaos

When I step into a new client environment, I'm not looking for one big catastrophic issue - though that's often when we get pulled into new clients.

It's friction; what's slowing your business down and causing unseen chaos?

One of the fastest ways to identify friction in a Lead-to-Cash process is what I call the swivel chair test.

If someone is copying information from one screen into another system manually, you have a problem. And you might be shocked at how often this actually happens at companies both large and small.

What "Swivel Chairing" Really Means

Failure with the Swivel Chair test shows up at handoff points.

• A website form creates a lead, but someone manually re-enters it into CRM.

• A quote is approved, but someone manually recreates it in ERP for invoicing.

• An amendment is booked, but someone in finance adjusts the numbers because the system does not handle a specific scenario correctly.

Each of these feels small, and individually don't seem like a transformation-level issue.

But collectively, they introduce risk, inconsistency, and reporting gaps in your entire lead-to-cash system.

Manual data entry is one of the most common sources of data quality problems - we extrapolated Gartner's $12.9 million dirty data claim with additional research to show it adds up to over $600B annually for US businesses alone.

Swivel chairing is major way this cost accumulates - and your organization isn't immune, I promise you that.

The Tribal Knowledge Problem

Revenue accuracy often depends on specific individuals knowing how to “fix” issues between systems.

• Someone in accounting knows that certain amendments produce incorrect pricing and corrects it manually before it goes to the customer.

• Order Fulfillment knows a product code sometimes shows incorrectly, so double-checks it before pushing it through the system

• Sales reps know how to translate naming inconsistencies between CRM and ERP.

• Marketing exports leads and recreates them downstream because systems aren't connected.

The process works because people intervene, and that's why there's significant risk - it isn't scalable, and it you try (for instance, with AI), you're doomed without understanding what led to it.

And if one key person leaves, suddenly that friction they handled crops up and starts costing you money - both in slower cash flow and lost sales / increased churn. All because these processes were never formalized in documentation ... or fixed at the source as they should've been.

That is when reporting begins to break down, too: because as friction creeps in, downstream impacts become more severe.

Symptoms Of Lead-To-Cash Friction I See Frequently

When companies describe their challenges, they rarely say, “We have swivel chairing.”

They say:

• We can't reconcile funnel reporting with what we know is true

• Finance doesn't trust Sales forecasts - downstream revenue isn't matching up

• Campaign impact? Unclear on what's driving revenue for the business

• There's no unified view of the customer - it's always fragmented based on which team you're talking with

These are downstream symptoms; the root issue is usually fragmented revenue architecture and inconsistent data discipline across systems.

Harvard Business Review has written extensively about the importance of treating data as a shared enterprise asset rather than a departmental byproduct. When data lives in silos, cross-functional alignment breaks down.

Lead-to-Cash is a cross-functional system by definition. Leads come from marketing, quotes and revenue come from those selling, and revenue recognition from finance, purchasing, and accounting.

If your systems - which I call revenue architecture - aren't aligned, your reporting can't be!

What Healthy Lead-to-Cash Looks Like: Three Key Items

In a healthy environment, three things are true.

First, automation exists at every major handoff. Information gathered at the beginning of the process flows through to opportunity, quote, invoice, and renewal without being manually re-entered.

Second, there is a consistent identity for accounts and contacts across systems. Unique identifiers connect CRM, ERP, and billing platforms. Naming inconsistencies do not require human interpretation.

A slight aside, manual re-entry happens when your underlying data architecture isn't setup well - and this covers everything from how job titles are handled to data flows across systems. This foundation is a typical organizational starting point that feeds not just revenue processes, but everything else.

It's often an architectural issue - stemming from siloed or improper implementation that wasn't the holistic investment you needed it to be both originally and now.

Third, there are no hidden corrections. If the system produces a result, that result is the source of truth and it's trusted because it passes everyone's gut check and survives further scrutiny.

This doesn't happen overnight, but one good project with follow-ups around guardrails, optimization, and continued evolution does wonders.

When these conditions exist, you can trace a deal from first website interaction to renewal without second-guessing your reports or reconciling spreadsheets.

That clarity becomes especially important as organizations invest in AI.

Why This Matters More Now

Many revenue teams are focused on AI-driven forecasting, intelligent approvals, and guided selling.

Those initiatives depend on clean, connected lifecycle data. Many don't have it, which is why AI adoption isn't equating to higher revenue.

If your underlying systems require manual corrections and inconsistent naming, AI does not solve for this - it only amplifies it. Maybe this is why BCG research showed companies investing 120% more in infrastructure (including architecture and IT resources) were the ones most "future-proof" to disruptions ... just like AI is doing.

Before layering AI into your revenue engine, you absolutely must pass the swivel chair test.

A Practical Evaluation

Pick a recently-closed deal.

Trace it from:

• Initial marketing touch

• Opportunity creation

• Quote generation

• Accepted quote

• Invoice

• Fulfillment or provisioning

• Renewal

At each step, ask:

• Was data re-entered manually?

• Did someone correct inconsistencies?

• Were there mismatches in naming or account identity?

• Did reporting rely on spreadsheet reconciliation?

Every yes is friction!

Lead-to-Cash does not fail because companies lack technology - far from it, there's often too much of it. And as processes evolve, the tech doesn't keep up.

The goal isn't perfection; it's eliminating unnecessary human intervention between systems so your revenue engine can scale cleanly.

That is where operational efficiency improves, reporting becomes trustworthy, and where AI initiatives in your lead-to-cash operations start making sense (instead of noise).