There is a quiet truth in enterprise QA right now.
Many teams feel let down.
For the last several years, vendors have promised an AI revolution in testing. Autonomous agents. Self healing frameworks. Copilots that would “change everything.” Yet when you talk to QA leaders privately, the story is different. Productivity has barely moved. Script maintenance is still endless. Coverage gaps remain. And the hype cycle is drifting toward frustration.
This is not a criticism of buyers. It is an acknowledgement of reality.
We saw it coming.
In our own materials we have written about AI washing in QA and about AI that actually slows experienced engineers down. Those were not attacks on competitors. They were reflections of what we were hearing directly from the field.
Let’s unpack why so many AI efforts in QA have disappointed.
AI Washing in QA
Separating Real Capability from Marketing Spin
Every major vendor now claims to be AI powered.
But in practice, much of what is labeled AI falls into one of three buckets:
Element re identification rebranded as intelligence.
Prompt driven helpers layered onto legacy recorders.
Suggestion engines that still require humans to do all the work.
That is not autonomy. It is augmentation of old workflows.
When the core model is still human authored scripts, human maintained logic, and human curated test cases, adding a chatbot on top does not change the productivity equation. It simply adds another interface.
The industry rushed to attach AI to existing products rather than rethink the testing paradigm itself. The result is predictable. The labor model did not fundamentally change.
And if the labor model does not change, the economics do not change.
Copilots vs Productivity
Why Output Hasn’t Actually Increased
There is a growing body of research showing that AI assistance can reduce productivity for experienced engineers in certain contexts .
QA has felt this firsthand.
Copilots that generate partial test steps still require validation.
Agents that propose test cases still require review.
Natural language recorders still require someone to define every flow.
If a senior QA engineer must stop, inspect, correct, and refine AI output, the time savings often evaporate. In some cases, output slows down because oversight overhead increases.
Productivity is math, not magic.
Peter Diamandis often says that if you are not aiming for at least 10X improvement, do not bother. Incremental improvements feel good in demos. But they do not transform organizations.
In QA, most AI enhancements have delivered 10 percent improvements at best. Sometimes less. That gap between promise and measurable output is the root of the disappointment.
“Autonomous” Testing That Is Not Actually Autonomous
The word autonomous has been used liberally.
In many cases it means:
The system can re locate a button if its identifier changes.
The system can suggest additional test cases.
The system can auto fill some code.
That is not autonomy. That is assistance.
True autonomy in QA would mean the system can generate, execute, adapt, and expand coverage with minimal human direction. Most tools do not do that. They still depend on human defined logic as the foundation.
Without a fundamentally different architecture, including models that can explore applications beyond predefined flows, the system will never escape the limits of human imagination.
And human imagination is exactly what constrains coverage today.
Demo Magic vs Production Reality
Where the Value Breaks Down
Almost every AI QA tool looks impressive in a demo.
Type a sentence. Watch something happen. See a test generated. Applause.
But production environments are not demos.
They include:
Complex multi step workflows.
Conditional validations.
Data dependencies.
Edge cases that only appear at scale.
Continuous change across sprints.
That is where AI overlays often collapse. They generate a handful of scripts successfully. Then maintenance begins. Exceptions pile up. Human intervention creeps back in.
The delta between demo performance and enterprise scale reality is where trust erodes.
Agent Washing
When “Agentic” Language Replaces Actual Capability
There is a new wave of marketing language washing over QA.
The word AI is no longer enough. Now everything is agentic. Autonomous agents. Swarms of agents. Orchestrated agents. Cognitive agents.
It sounds advanced. It sounds inevitable. It often means nothing.
This is what we might call agent washing. It is the practice of layering impressive sounding agent terminology on top of workflows that still require humans to design, trigger, supervise, and maintain everything.
Here are a few phrases that look powerful on a slide and collapse under scrutiny:
“Self orchestrating multi agent quality mesh”
Translation: several background services calling each other while humans still write and maintain the tests.
“Cognitive autonomous validation agents”
Translation: a rules engine wrapped in large language model prompts that still depends entirely on human defined scenarios.
“Dynamic intent aware regression swarm”
Translation: parallel execution of pre written scripts with a dashboard that looks futuristic.
The language implies autonomy. The productivity data often says otherwise.
If the so called agent still needs humans to define the flows, approve the output, repair the logic, and maintain the scripts, it is not an agent. It is an assistant with a fancier job title.
Agentic architecture should mean one thing: measurable elimination of manual effort.
If there is no hard metric showing reduced labor, expanded coverage, or dramatically accelerated throughput, then the agent is just a marketing character in a story.
In QA, the only agents that matter are the ones that change the math.
A Broken Business Model
When Productivity Threatens Revenue
There is another uncomfortable truth in the QA tools market.
Most vendors do not actually benefit when your productivity improves.
If your core product is a recorder and your primary customers are large global system integrators, your revenue model is simple. Sell more seats. Expand test teams. Increase usage. Charge per user. Charge per execution minute. Grow headcount and grow license count.
Now ask a hard question.
What happens if you deliver true 10X or 50X productivity gains?
Fewer test engineers are required.
Fewer seats are needed.
Less manual scripting means less user based licensing.
In other words, if your AI truly eliminates labor, it directly reduces your revenue.
That creates a structural conflict.
When your business depends on the number of humans touching the tool, you are financially disincentivized from removing those humans from the workflow. So what happens instead?
You enhance the recorder.
You add copilots.
You add agent dashboards.
You add orchestration layers.
You introduce premium AI add ons.
All of which increase seat cost, increase platform complexity, and expand perceived value, without materially reducing labor.
The labor model stays intact.
The seat count grows.
Revenue scales.
But productivity barely moves.
It is not malicious. It is economic gravity.
If your pricing model is tied to human effort, you cannot afford to eliminate human effort. So innovation focuses on features that justify higher per seat pricing rather than features that collapse the seat requirement entirely.
This is why many AI features feel additive rather than transformative. They enhance the workflow instead of redefining it.
True autonomy challenges the revenue model of legacy QA vendors. It shrinks the dependency on armies of testers. It compresses license demand. It changes the economics of the entire ecosystem.
And that is precisely why it has been slow to emerge from vendors whose survival depends on maintaining the old labor structure.
In the end, AI in QA will force a choice.
Optimize for seat expansion.
Or optimize for productivity expansion.
Those two paths rarely point in the same direction.
A Different Experience Emerging
Here is the encouraging part.
Not all AI in QA has been disappointing.
In environments where AI is architected to reduce or eliminate labor in certain tasks rather than assist it, teams are reporting something different. We have consistently seen measurable improvements between 10X and 100X in productivity, coverage, and labor efficiency when AI is applied to real autonomous script generation and large scale test expansion.
That is not marketing language. It is measured throughput.
More coverage per release.
More defects surfaced before production.
Less human scripting time.
Greater visibility across applications.
When AI removes the bottleneck rather than sitting beside it, the math changes dramatically.
And that is what many teams were hoping for in the first place.
A Fresh Breath in the Storm
This post is not about attacking vendors. The industry is learning in public. The first wave of AI in QA was inevitably experimental. Some approaches were evolutionary. Almost none were revolutionary. And as we discussed…they cannot be revolutionary with the current tool vendors. It would kill their revenue.
Disappointment is part of any hype cycle.
But the lesson is clear.
AI that assists manual processes will produce small incremental gains.
AI that replaces manual bottlenecks can produce exponential ones.
As an industry, we should demand measurable outcomes. Not better interfaces. Not clever prompts. Not agent branding.
Real coverage expansion.
Real labor reduction.
Real productivity multipliers.
If your AI investment is not driving at least an order of magnitude improvement, it is fair to question whether it is transformational at all.
We knew the first wave would overpromise. We also knew the architecture would matter more than the marketing.
For teams who feel frustrated right now, you are not wrong. You were promised a step change. You received polish on legacy tooling.
The good news is this. True AI led QA is possible. It simply requires rethinking the foundation.
And that shift is already underway.
