Modernization ROI Statistics & Benchmarks

This is the data hub for the series. Every figure cited across Parts 1 through 8, the failure rate of big-bang programs, the maintenance ratio, the cost of technical debt, is collected here, named, dated, and sourced, so you can pull it into your own business case and stand behind it under scrutiny. It is also the part that has to be the most disciplined, because a statistics article is only as valuable as it is honest. Every number below is reproduced from its original source as that source stated it. Where two credible sources disagree, the range is shown rather than a side picked. None is invented, and none is restated to sound stronger than its source supports.

A note on how to use these before the numbers themselves. Industry statistics establish scale and credibility. They show the problem is real and well-documented across the industry. They do not prove the problem exists in your system, and a case built only on industry figures is one a skeptic can dismiss as “that’s other companies, not us.” The figures anchor. Your own operating data convicts. Use both, in that order.

The benchmark figures

Figure	Claim	Source	Date
Up to 70%	of digital transformations fail to deliver on their objectives	Boston Consulting Group (BCG)	September 13, 2023
~55 to 57%	of enterprise IT spend goes to running existing systems	Deloitte	Global CIO surveys, 2016 to 2020
~42%	of developer time is lost to technical debt and bad code	Stripe, “The Developer Coefficient”	2018
20 to 40%	of a technology estate’s value is technical debt, and managing it can free up to 50% more engineering time	McKinsey & Company	October 6, 2020
~220B to 800B+	lines of COBOL estimated in production worldwide	Reuters, via IEEE Spectrum. Upper bound, Micro Focus	2017 · 800B+ by 2022
~58	average age of a COBOL developer, and about 10% of the COBOL workforce retires each year	IBM, via Fujitsu, 2020	Reported 2020 to 2023
$8.39B → $13.34B	mainframe modernization market, 2025 to 2030, a 9.7% CAGR	MarketsandMarkets	August 21, 2025

Each figure is maintained on our statistics hub, refreshed quarterly and corrected in place with a dated note when a source updates. What follows is how to read the ones that carry a business case.

The failure rate: read it as a base rate

Boston Consulting Group’s 2023 finding that up to 70% of digital transformations fail to deliver on their objectives is the most-cited number in modernization, and the most-misused. It is not the failure rate of all modernization regardless of method, and quoting it as such overstates what the source says. Read it for what it is: the base rate for large, all-at-once change programs, the kind defined entirely upfront and proven only at a distant cutover.

Read that way, it is the strongest single argument in the series for an incremental approach. Incremental, slice-by-slice delivery exists precisely to change which side of this number a program lands on, by replacing one large untested bet with a sequence of small, validated ones. The statistic does not say modernization fails. It says betting everything on a single cutover fails most of the time. That distinction is the whole case.

The maintenance ratio: the cost of the status quo

Deloitte’s CIO surveys, which put roughly 55 to 57% of enterprise IT spend on running existing systems, quantify the thing the cost-of-inaction argument is built on. Most of the IT budget keeps yesterday running rather than building tomorrow, and for an aging system the ratio worsens over time.

This is the number behind two channels in the ROI model from Part 2. The lower-maintenance channel is the work of reversing this ratio so more of the budget points at the roadmap. The cost-of-inaction figure from Part 4 is, in part, this ratio climbing year over year. When you cite it, cite it as the cost of the status quo, the spend the business is already making to stand still.

The capacity figures: what’s actually at stake

Two figures size the engineering capacity in play. Stripe’s 2018 Developer Coefficient report found developers lose roughly 42% of their time to technical debt and bad code, nearly half of engineering capacity spent fighting the codebase rather than building on it. McKinsey’s 2020 analysis put technical debt at 20 to 40% of a technology estate’s value and found that managing it can free up to 50% more engineering time.

Together these quantify the recovered-capacity argument. The cost of the status quo is not only the maintenance bill. It is the share of your most expensive resource, engineering time, consumed by the system’s condition rather than the business’s goals. These figures are explored in depth in the technical-debt series. Here they serve to size the prize in a modernization case.

The talent and market figures: the clock and the trend

Two more figures supply context the case often needs. On talent, IBM (reported via Fujitsu, 2020) has reported the average COBOL developer’s age at around 58, with roughly 10% of that workforce retiring each year, the concrete form of the thinning-knowledge cost from Part 4. The people who hold the undocumented rules are leaving on a schedule, which is why knowledge capture is a race against the calendar. On the market, MarketsandMarkets valued the mainframe modernization market at $8.39 billion in 2025, projected to reach $13.34 billion by 2030, a 9.7% CAGR, evidence that modernization has moved from optional to budgeted across the enterprise, useful when a case needs to show this is an industry-wide shift rather than a local preference.

How to deploy each figure in a business case

A statistics page is only useful if you know which number does which job. Each figure supports one argument well and weakens if you push it to do another. Here is where each one belongs and the caveat to state alongside it, so a skeptic in the room cannot turn your own number against you.

The figure	The argument it makes	Where it belongs	The caveat to state with it
BCG failure rate	Big-bang change is the risk, not modernizing itself	The method choice: why incremental	The base rate for all-at-once programs, not a verdict on every modernization
Deloitte maintenance ratio	The status quo already consumes most of the budget	Cost of inaction and the lower-maintenance return	An industry average, not your ratio. Pull your own maintenance split
Stripe developer-time figure	Capacity goes to fighting the code, not building on it	The recovered-capacity return	Self-reported survey data. It sizes the problem, it does not measure your team
McKinsey technical-debt figures	The upside of managing debt is large	Sizing the prize	A range across a broad sample. Directional, not a promise for your estate
IBM COBOL workforce figures	The knowledge is leaving on a fixed schedule	Cost of inaction, timing, and risk	Specific to COBOL. It applies by analogy to any aging stack
MarketsandMarkets market size	This is a budgeted, industry-wide shift	Showing the move is not a local preference	Market sizing. It says nothing about your own return

The pattern is the same down the whole table. Every figure anchors scale and none of them convicts your system. That is the job of your own operating data, which is the subject of Part 2 on calculating ROI.

Three cautions about every number above

Statistics are the most abused element of a business case, and a data hub has an obligation to say how they go wrong. Three cautions. First, none of these figures is about your system. They establish scale, not local fact, and a case that leans on them alone is one a skeptic rightly discounts. Second, ranges are honest and false precision is not. Where sources disagree, as on COBOL line counts, the range is the truth and collapsing it to a single number is a distortion. Third, a statistic ages. Every figure here carries a date for exactly that reason, and a number cited without its date is a number you cannot defend. The discipline that makes these figures useful is the same one that runs through the whole series: name the source, show the date, state the range, and never let a borrowed number do the work your own data should.

Where this leads

That closes the series. You now have the full economic toolkit: how to reason about cost without a sticker price, how to calculate ROI across its channels, how to build the case and lead it with the cost of inaction, how to budget and structure the engagement, how to win executive buy-in and sustain it through phased funding. The full series lives at the Cost, ROI & the Business Case hub.

The figures here size the problem. The only number that prices your system comes from examining it. When the case is ready and the question shifts from “what does this cost in general?” to “what does this cost for us?”, that is what a discovery is for. Book a 30-minute discovery call, no deck, just a conversation about your system and what acting on it would actually take.