What Prevents the Software Industry from Switching to Outcome-Based Pricing?

Imagine an oil rig in the middle of the ocean at night. An explosion occurs, and within minutes, flames engulf the platform. Below is pitch-black, freezing water that no sane person would voluntarily jump into. But staying is no longer an option.

Hourly billing (Time & Material) in software engineering is that exact platform. For decades, it kept the IT industry afloat: predictable, comfortable, and easy to measure. Today, it is engulfed in invisible flames but not physical, but economic: an hour of engineering labor is no longer an honest measure of value.

In my previous article, we analyzed the raw arithmetic: generating 24% more features for the same hourly budget isn't a gift to the client; it’s deferred technical risk. Many vendors are frantically trying to build "salvage rafts" using fancy new "AI Delivery Frameworks." But almost none of them address the fundamental question: How do you accurately price a real, verified engineering outcome?

Let's break down why previous industry "lifeboats" fail in the AI era.

Story Points were originally created as a relative, team-specific metric for internal sprint planning. Using them as a foundation for commercial pricing is a well-documented industry mistake. A unit that was never intended to be comparable across projects cannot explain why one "5-point task" costs $500 while another costs $5,000.

Fixing the price upfront based on high-level specs leads to two outcomes: either the vendor inflates the budget with massive risk buffers (and the client overpays), or the very first change request destroys the model from within. Without an objective mechanism for recalculation, change requests inevitably lead to friction, conflicts, or mid-project contract renegotiations.

T&M was the industry's most reliable lifeboat for decades. It survived CASE tools, low-code platforms, and offshore outsourcing because none of them disrupted the fundamental equation: 1 hour ≈ 1 predictable unit of work created.

AI broke this link entirely. Code generation speed has completely detached from code verification speed. Paying for hours when code output triples forces vendors into a toxic dilemma: either lose profit margins or silently cut code-verification standards. T&M isn't sinking because it was poorly designed; it's sinking because the cargo changed while the hull stayed the same.

If you ask any CTO or enterprise client whether it makes sense to "pay for results instead of hours", almost everyone will agree.

So what happens in practice?

According to Gartner, only 19% of buyers and 13% of software providers use outcome-based pricing today. Gartner predicts this share will barely reach a quarter of the market by 2031. As a Gartner analyst noted: "More buzz than reality" (Source: CIO Dive)

The gap is massive: everyone sees the rescue helicopter on the horizon, but almost nobody can board it.

Why? Because "paying for results" is not a marketing slogan. It's a complex engineering problem requiring clear answers to 3 fundamental questions:

1. What constitutes an objective "unit of result"? It must be repeatable, standardized, and identical across projects, rather than estimated based on a manager's intuition.

2. How do you calibrate the per-unit price? It must be calibrated against verifiable historical delivery data, not seller guesswork or buyer anxiety.

3. Who verifies that the result was achieved before money changes hands? Independent, structured quality verification must be embedded into the unit itself.

Without honest answers to all three questions, "outcome-based pricing" silently collapses back into hourly billing with a prettier slide deck or turns into a traditional fixed-bid model.

A sustainable estimation methodology cannot compromise between "easy to explain to a client" and "reflecting technical reality." To survive, it must:

• Measure work in minimal (atomic), objectively comparable units across any project.

• Calibrate pricing against empirical delivery data.

• Embed code verification directly into the pricing unit itself to ensure quality isn't sacrificed under budget pressures.

None of the legacy pricing models cover all three requirements simultaneously. That is why the gap between the promise of "paying for results" and its reality remains so wide.

In my next article, I will break down how to design an estimation framework from scratch, built specifically around these three principles and backed by algorithms, mathematics, delivery data, and engineering aaproaches.