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Whitepaper

The Friction Tax

What disconnected credit, compliance and onboarding processes really cost your business, and what to do about it.

The deal you lost wasn't to a better price. It was to a faster answer.

Your rep has done the work. The prospect is interested, the pricing is broadly agreed. Then comes the question that sounds administrative but isn't: can we actually trade with you, at what limit, and on what terms? While Credit and Compliance reconstruct that picture by hand across systems that don't talk to each other, the prospect is free to keep shopping.

That wait has a name: the friction tax. It's the hidden cost of making a customer wait while your organisation reassembles information it already holds, and it's paid out of your pipeline every quarter, whether or not anyone is tracking it.

This short paper breaks down what slow onboarding actually costs Sales, how to measure it in your own pipeline, and what changes when Sales, Credit and Compliance work from one connected record instead of five disconnected ones.

What's inside
  • Why buyers rank speed above price when choosing a supplier
  • The exact point in a deal where slow onboarding starts losing it
  • What changes for Sales when risk teams work from one connected record
2×more B2B buyers cite slow response than price as their top supplier complaint
50 daysthe cliff beyond which deal win rates roughly halve, from ~47% to ~20%
90%+faster time-to-quote at an organisation in McKinsey’s research, with central control intact
The Friction Tax: whitepaper cover
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Key findings

The Friction Tax in brief

The friction tax is the compounding cost of making a customer wait while your organisation reassembles information it already holds. These are the paper’s five main findings and the research behind them. The full 16-page whitepaper adds the diagnostic checklist, the mechanism in practice and the design principles.

01

Buyers rank speed above price

When McKinsey surveyed 1,000 B2B decision makers about what frustrated them most in dealing with suppliers, the number one pain point was lack of speed, mentioned twice as often as price.

Source: McKinsey & Company, Finding the right digital balance in B2B customer experience

02

Delay roughly halves the win rate

Across 4.2 million B2B opportunities ($54 billion in revenue, 530 companies), deals that closed within 50 days were won at about 47%. Beyond that point the win rate fell to around 20%. Delay does not shave points off conversion; it roughly halves it.

Most organisations cannot say how many days their credit and onboarding process adds to a deal. Where that number is unknown, the friction tax is paid out of the pipeline every quarter, without anyone attributing it.

Source: Ebsta & Pavilion, B2B Sales Benchmarks

03

Friction also creates late payment and bad debt

In Western Europe, 47% of B2B invoices are currently overdue and around 6% of invoices are ultimately written off (about 1.6% of invoiced turnover). The leading cause of late payment is customer cash-flow pressure, but administrative inefficiency in the credit and onboarding process is a documented contributor, delaying settlement even where the customer can pay.

So friction does not only cost deals at the front end. It reappears at the back end as the late payment and write-offs the credit policy exists to prevent: commercial and risk performance fail together, for the same reason.

Source: Atradius, Payment Practices Barometer, Western Europe edition 2025–2026

04

Redesigning the process beats automating tasks

Benchmarks for an AI-redesigned order-to-cash process show automated credit decisions rising 138%, delinquent days falling 85%, order-to-cash process cost falling 52–59% and dispute resolution speed rising 43%. These are modelled figures, not observed averages.

The central conclusion matters most: the gains come from redesigning the end-to-end process, not from automating individual tasks inside a fragmented one.

Source: The Hackett Group, AI World Class Finance Benchmarks, July 2026

05

Speed and control are not a trade-off

Being well controlled and being easy to buy from only look like opposites because of fragmented systems. McKinsey’s research includes an organisation that cut its time-to-quote by more than 90% while keeping a high degree of central control. Both speed and control depend on whether the process is connected.

The three levers of friction

Walkthrough timeHow long until there is an answer. A slow decision forces the sales rep to stall the customer or improvise a promise nobody approved.
Size of the limitA limit can be entirely defensible on risk and still be commercially useless, because it sits below what the customer needs to buy.
Applicable termsShort payment windows, guarantees and extra conditions each protect risk, and each makes you harder to buy from. Close to 40% of European companies offer credit terms mainly to win new business.

Three measurements to size your own friction tax

Elapsed time to decisionThe median time from credit application submitted to decision communicated to the sales rep, measured end to end, not per system.
Straight-through rateThe share of applications that complete without manual intervention.
Exception volumeHow often policy is worked around informally, and in which regions or teams.

If these three numbers cannot be produced from your existing systems within a working week, that is the strongest evidence that the friction is present, material and currently unmanaged.

Sources

  • The Hackett Group. AI World Class Finance Benchmarks. 15 July 2026, distributed via Business Wire. Modelled benchmark performance for AI-redesigned order-to-cash processes.
  • McKinsey & Company. Finding the right digital balance in B2B customer experience. Based on a survey of 1,000 B2B decision makers.
  • Ebsta & Pavilion. B2B Sales Benchmarks. Analysis of 4.2 million opportunities and $54bn in revenue across 530 companies.
  • Atradius. Payment Practices Barometer, Western Europe edition, 2025–2026, covering 11 markets including the Netherlands, Germany, France, Italy, Spain and the UK.

All figures come from named, publicly available industry research and are benchmark indicators, not guarantees. The Hackett Group figures reflect modelling of AI-redesigned processes rather than observed averages. Actual results vary by industry, market, data quality and process maturity.

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