The fit tax: what off-the-shelf software that almost works really costs

TL;DR

Almost every small firm runs software that only almost fits. The CRM that needs a spreadsheet bolted to the side. The job system nobody exports from without retyping. The subscriptions that overlap just enough to be annoying, but not enough to cancel.

The usual numbers used to describe that problem are mostly junk once you trace them. The industry’s favourite feature-waste stats come from software-maker analytics, a 2002 conference talk about four internal applications, decade-old enterprise desktop-licence research, or worked examples from vendors selling the cure.

The honest picture is narrower, but more useful. Off-the-shelf software plainly wins for standard jobs. The “drowning in unused subscriptions” story is mostly an enterprise story. But the real small-business cost still exists: it is the fit tax — the paid time your team spends carrying information across the gaps between systems that almost work.

There is no reliable external benchmark for that. Not for a ten-to-fifty-person UK firm. The only number worth having is the one you measure yourself.

The claim landscape: the numbers that don’t survive

Search for the cost of off-the-shelf software and the first page is written almost entirely by companies selling the alternative — custom-software agencies, or SaaS-management vendors selling the cure for subscription sprawl.

That is not a complaint. It is a measurement. And it matters, because when a whole genre is written by sellers, the numbers tend to drift in the seller’s direction.

Here is where the load-bearing claims actually come from.

“80% of software features are rarely or never used.” This is the fit stat, the one that supposedly proves you are paying for software you do not need. Its modern source is real and named: Pendo’s 2019 Feature Adoption Report, which analysed feature usage across 615 of Pendo’s own customer subscriptions and found that 80% of features were “rarely or never” used — though the honest breakdown is that 24% were never touched and the rest were simply used rarely.

The trouble is what the number measures. Pendo sells product-analytics software to the people who build applications; the report is a pitch to software makers about which of their own features get clicked, and it prices the finding as $29.5 billion of wasted cloud-vendor R&D. It says nothing whatsoever about a buyer wasting money. Relabelled from “the average product ships features few people use” into “you waste 80% of the software you pay for”, it becomes a different claim its own data cannot support.

“64% of features are rarely or never used.” The older, more-abused cousin, usually cited to the Standish Group. Trace it and, as agile consultant Mike Cohn documented, it comes from a keynote the Standish chairman gave at a conference in 2002, “based on a study of four internal applications. Yes, four applications. And, yes, all internal-use applications. No commercial products.” It was never published as a study; there is no methodology to read. Cohn’s plea is blunt: “if you’re citing this data and using it to imply that every product out there contains 64 percent ‘rarely or never used features,’ please stop.” A number about four bespoke in-house apps is now routinely quoted to prove off-the-shelf software is wasteful.

“$34 billion is wasted on unused software.” Still quoted in 2026 as if it were a SaaS figure. It comes from 1E’s Software Usage and Waste Report — dated 2016, measuring on-premise desktop software such as WinZip, Visio and Project that had been installed but “not run within the past 90 days”, across 149 enterprises averaging around 30,000 seats each. Three things break it for a small firm: it is desktop licensing, not SaaS; it is enterprise-scale, not ten-person-scale; and it is a decade old, published by a vendor whose old web address now redirects to TeamViewer. A decade-old desktop-licence study from giant estates is not evidence about your subscriptions.

“25% — or is it 30% — of SaaS spend is wasted.” There is a real Gartner figure here: a 2021 Gartner note (abstract) forecasts that 25% of SaaS software “will be underutilized or overdeployed” — an analyst prediction, about enterprises, with the detail behind Gartner’s paywall. What you meet in the wild is the escalated version: “30% of SaaS spend is toxic”, “$45 billion wasted industry-wide”, attributed to Gartner with a bare name and no document. Those bigger numbers do not appear in the Gartner note. They are the attribution graduating, quietly, into a fact of its own — which is exactly the move that should make you distrust the whole genre.

“UK small firms waste up to £10,000 a year on unused software.” The most quotable UK number in the field, and it is not a survey finding at all. It originates as a worked example from Fasthosts, a web-hosting company, imagining a fifteen-person marketing agency and calculating that “removing just two tools could result in savings of up to £10,000” — then syndicated until it reads like measurement. It is arithmetic about a firm that does not exist.

The claim, as it circulates	Where it was born	The labels that fell off
“80% of features never used”	Pendo, 2019 (615 of its own customers)	A software-maker’s metric; 24% actually never used; says nothing about buyers
“64% of features rarely/never used”	Standish chairman, 2002 keynote	Four internal-use apps; never published; the critic’s own plea is “please stop”
“$34bn wasted on unused software”	1E, 2016	Desktop licences, 30,000-seat firms; vendor now redirected/absorbed; a decade old
“Gartner: 30% toxic / $45bn wasted”	escalation of a 2021 Gartner “25%” note	Gartner says 25%, about enterprises; the 30% / $45bn is not in the document
“UK SMEs waste £10k/yr”	Fasthosts worked example	A hypothetical 15-person agency; not a survey; no sample

The pattern is always the same. A real number, measured on the wrong population — usually a large enterprise — for the wrong purpose — usually selling software — loses its date, its size, and its hedge, and walks around as a fact about your firm.

What the evidence actually says

Strip the genre back to what survives, from sources that are not selling you anything, and the picture is smaller and more honest.

Off-the-shelf already won the standard jobs. The government’s Longitudinal Small Business Survey 2024 — 8,396 SME employers — finds that 69% used technologies or web-based software to sell to customers or manage the business. Among those digitally active firms, accountancy software was used by 80%. That is not a market with a fit problem in its core admin; it is a market that has, sensibly, bought the standard tool for the standard job.

The same survey shows where adoption thins out: customer-relationship management runs at 26% of micro firms, 42% of small and 54% of medium; HR management software runs at 14% of micro firms, 44% of small and 62% of medium. The further a tool sits from a universal task, the less universal adoption becomes.

Software (LSBS 2024, SME employers)	Adoption
Accountancy software among digitally active SME employers	80%
CRM — micro / small / medium	26% / 42% / 54%
HR management — micro / small / medium	14% / 44% / 62%

The official worry is under-investment, not waste. This is the finding that cuts hardest against the “SMEs drown in software” story. When the Bank of England and DBT surveyed 2,885 UK SMEs about their investment, 76% felt they had invested about the right amount, 22% felt they had invested too little, and just 2% thought they had invested too much. A market where one firm in fifty thinks it over-bought is not a market with a shelfware epidemic.

UK SMEs on their own investment (BoE/DBT 2023, n=2,885)	Share
Invested about the right amount	76%
Invested too little	22%
Invested too much	2%

The real problem has a name, and it is fit. The OECD’s 2026 review of UK SME technology adoption puts it in flat official language: many SMEs face “difficulty in the identification of solutions that match their operational needs”. It also names the cost the sticker price hides — “not only the upfront investment costs but also implementation, maintenance and subscription costs”. That is the fit problem, described by an institution with nothing to sell.

And more software is not a reliable fix. The Enterprise Research Centre found, even among advanced digital tools, that “more isn’t always better” — some combinations lower productivity through “overlapping functions, integration challenges, or the complexity of managing multiple systems”.

And the sprawl is real — for someone else. The alarming waste numbers are not fabricated; they are just measured on giants. When Zylo reports about half of provisioned licences sitting unused, or BetterCloud counts an average company running over a hundred applications, they are describing corporate estates of thousands or tens of thousands of seats, not a ten-to-fifty-person UK firm. Tellingly, even BetterCloud — a company that sells the cure for sprawl — reports the number of apps per company falling for the first time in a decade, from 130 in 2022 to 112 in 2023, as firms consolidate. The runaway-sprawl narrative is softening even where it was strongest, and it was never really about you.

Company size	Software spend per employee/year (Cledara, 2024)
Up to 20 staff	~$8,000
50–100 staff	$2,583
100–200 staff	$1,741

The real cost is the fit tax

Notice what the honest evidence does not give you: a pound figure for what off-the-shelf software costs a ten-to-fifty-person UK firm.

I looked hard. I could not find one — not at the ONS, not at DBT, not in any vendor report segmented small enough to matter. Every waste percentage is enterprise-scale and sold by someone; every spend figure, as the chart above shows, swings wildly with size, country and sector. Even Cledara’s own numbers put per-employee spend anywhere from $1,741 to $8,000.

That gap is not just a research failure. It is the point.

The cost of almost-fitting software is genuinely hard for national statistics to see, because it does not live neatly in the subscription line. It lives in three places the accounts never separate.

• The workaround. The spreadsheet bolted onto the CRM. The figures retyped from one system into another because they do not connect. The manual export, every Friday, that turns two tools into one report. This is labour, and it is usually the largest hidden cost — and the one, tellingly, that not a single survey in this field even tries to price.

• The overlap. The three tools that each do part of the job because none does all of it. Cledara found that firms underestimate their own tool count by around 40% — “for every 10 tools a company thinks they’re using, there are actually 14 in play”. You cannot manage, or cancel, what you have lost count of.

• The unused seat. The licences bought for a team that shrank, the annual plan nobody reviews. Real, but — for a small firm — usually the smallest of the three, and the easiest to fix without building anything.

A small firm runs around 36 applications, on Okta’s 2024 identity-network data for firms of 50 or fewer staff worldwide — itself a number to hold lightly, since it comes from firms organised enough to buy single sign-on. But 36 tools is 36 possible seams, and every seam is a place where information may need to be carried across by hand.

That is the fit tax: the paid human effort required to make almost-right software behave as if it fitted properly.

Reckon your own fit tax

Because no honest external number can price this for you, here is the method that can. It takes an afternoon, and it is the same shape as any decent cost estimate: count, time, and price.

1. List the seams. Every place the same information is typed into two systems, exported from one to feed another, or held together by a spreadsheet. Be specific: “bank lines into the job sheet”, “job sheet into the invoice”, “enquiry email into the CRM”.

2. Time one crossing of each, with a clock. Not a guess — every failed number in this article began as somebody’s guess. Then count how often it happens in a normal week.

3. Price the labour. Minutes ÷ 60 × weekly occurrences × 46 working weeks × the loaded hourly wage of whoever does it. Loaded wage means their gross hourly pay plus employment costs. For a rough UK SME estimate, gross pay plus 15% employer National Insurance is a better starting point than pretending staff time is free.

4. Add the overlap. List every subscription. Mark the ones whose job substantially overlaps another’s, and the ones you had lost count of. That annual total is the ceiling on what consolidation could return.

For example:

Seam	Time per crossing	Frequency	Loaded hourly cost	Annual fit tax
Re-key enquiry email into CRM	4 mins	40/week	£30	£3,680
Export job sheet into invoice draft	10 mins	15/week	£30	£3,450
Build Friday reporting spreadsheet	90 mins	1/week	£30	£2,070
Manually reconcile two status lists	20 mins	3/week	£30	£1,380

Four boring seams: £10,580 a year.

The exact number is not the point. The discipline is. A measured £6,000 problem should not be sold a £40,000 build. A measured £30,000 problem should not be waved away because the subscription itself only costs £99 a month.

The question is not “could this be automated?” Most things can. The question is:

Is this crossing costing meaningfully more than fixing it would?

That is the honest bar.

What it means for you

For most firms, most of the time, off-the-shelf software is the right answer. This piece is not an argument against it.

The standard tool for accounts, payroll, email, card payments, bookkeeping or tax filing is cheaper, better-maintained and more secure than anything worth building from scratch. That is exactly why off-the-shelf dominates the standard jobs. If your loops are small, your processes ordinary and your tools genuinely talk to each other, the fit tax is a rounding error.

Leave it alone.

The tax bites in a narrower case: when your process is genuinely non-standard — the thing you actually do differently from your competitors — and the off-the-shelf tools were built for the average firm, not yours.

The signals are familiar:

• you export to a spreadsheet to make two systems agree;
• you keep a tool for one feature and pay for the other ninety;
• a person spends part of every day being the pipe between two systems;
• management information exists, but only after someone has assembled it by hand;
• the business has changed, but the software stack still reflects how things worked three years ago.

That is the fit tax, and it compounds quietly on the payroll where no statistic can see it.

None of this requires a grand replacement. In fact, the grand replacement is usually the dangerous option. Custom software has earned its bad reputation precisely when it was sold as a big-bang rescue.

The honest move is smaller: take the single most expensive crossing on your list — the one costing four hours a week — and close just that gap, with something built to fit the way you actually work. Measure it. Then decide whether the next one is worth touching.

Built one fitted module at a time, against a number you timed yourself, custom software stops being a leap of faith. It becomes a normal, reversible business decision.

That is the work I do: building the custom processes and workflows — increasingly with AI at their core — that close the specific gaps off-the-shelf software leaves, one measured module at a time.

If you would rather someone else held the stopwatch first: book a free process audit. We will map your worst software seams, time the manual work, and tell you whether custom software is worth touching.

And if the honest answer is that off-the-shelf already fits, I will tell you that too.

Sources and method

Every claim above is hyperlinked where it is made; the notes below restore the labels — sponsor, sample, date — and add an archived copy of each source, checked 6 July 2026 and re-verified 14 July 2026. Numbers named in the debunk as manufactured or misapplied are named but, deliberately, not linked: no authority is passed to bad figures.

• Pendo, 2019 Feature Adoption Report, 5 Feb 2019. Product-analytics vendor; feature-usage across 615 Pendo customer subscriptions; “80% of features… rarely or never used” (of which 24% never). A software-maker’s metric, priced as cloud-vendor R&D. Primary PDF · archive.

• Mike Cohn, “Are 64% of Features Really Rarely or Never Used?”, Mountain Goat Software (updated 13 Nov 2016). Traces the “64%” to Jim Johnson of the Standish Group, a 2002 XP conference keynote based on four internal-use applications, never published. Live · archive.

• 1E, Software Usage and Waste Report, 2016. On-premise desktop software “not run within the past 90 days”; 149 enterprises, 4.6 million machines, ~30,000 seats average, US + UK; “$34 billion” combined waste; 38% total (30% unused + 8% rarely). Vendor sold licence-reclaim tooling; its old domain now redirects to TeamViewer. Mirror PDF · archive.

• Gartner, “Why Are You Wasting Your SaaS Expenditure?” (infographic, doc 4006574), 7 Oct 2021. Forecast that “25% of… software will be underutilized or overdeployed”; enterprise-framed; full document paywalled. The circulated “30% toxic / $45bn” figures do not appear in it. Abstract, archived.

• Fasthosts / SME-Today, “UK SMEs wasting up to £10k a year on unused SaaS tools”, Apr 2026. The £10k is an illustrative worked example (a hypothetical 15-person agency), not a survey; named here as a specimen of the genre, deliberately not linked.

• DBT, Longitudinal Small Business Survey 2024 (SME employers), 25 Sep 2025. n = 8,396 SME employers (1–249 staff). 69% use technologies or web-based software to sell to customers or manage the business; among those, accountancy software 80% (89% in 2023); CRM 26% micro / 42% small / 54% medium. Live · archive.

• OECD, SME Technology Adoption in the United Kingdom, Apr 2026 (CC BY 4.0). “Difficulty in the identification of solutions that match their operational needs”; “implementation, maintenance and subscription costs”; ERP adoption 6.7% (2018) → 10.9% (2022). Live PDF · archive.

• Enterprise Research Centre, Research Paper 119 (exec summary), Oct 2025. LSBS 2022–23, nationally representative; among six advanced technologies (AI, cloud, BI, CAD, IoT, VR/AR), “more isn’t always better” — some combinations associate with lower productivity. Cited here as an advanced-tech finding, not a claim about basic SaaS. Gov-funded academic centre. Live PDF · archive.

• Bank of England / DBT, Finance and Investment Decisions Survey 2023 (Quarterly Bulletin 2024). n = 2,885 UK SMEs; 22% “invested too little”, 76% “appropriate”, 2% “too much”; “little variance… across firm size”. Live · archive.

• Cledara, 2025 Software Spend Report (published 30 Sep 2024) and “Average SaaS Spend Per Employee 2026” (25 Mar 2026). SaaS-management vendor. Per-employee spend ~$8,000 (up to 20 staff) → $2,583 (50–100) → $1,741 (100–200); UK ~$4,180 per employee at a 50-person firm; firms underestimate tool count by ~40%. Sample described in public materials as tech companies under 200 staff across the US, UK and EU; carried here as a vendor-sponsored directional benchmark, not a UK SME population estimate. Report (archive) · per-employee (archive).

• Okta, SMBs at Work 2024, Aug 2024. Identity-platform telemetry across 18,000+ companies; firms of 50 or fewer employees deploy “around 36 apps”. Vendor sells single sign-on; sample skews to digitally-mature firms. Live · archive.

• BetterCloud, State of SaaSOps 2024, Jul 2024. SaaS-management vendor; practitioner survey (company size undisclosed, skews larger). Apps per company 130 (2022) → 112 (2023), “first decline in over a decade”; 106 in 2024 on BetterCloud’s later restatement; 53% consolidated redundant apps. Press release · archive.

• Zylo, SaaS Management Index 2024 & 2026. SaaS-management vendor; enterprise estates (30–40 million licences, firms up to 10,000+ staff). Reports about half of provisioned licences unused (2024) and 36% “against recommended utilization” (2026) — different measures, not a like-for-like trend; cited only as enterprise context. 2026 edition · archive.