The test organization of the future is a model in which traditional hierarchies and fixed roles give way to small, self-organized units without managers. Testers in this model need what are called V-shape skills: deep expertise in one field combined with solid knowledge in neighboring areas. AI tools and cloud technology take over routine work, while exploratory testing and personal responsibility become more important.
Key Takeaways
- From I-shape to T-shape to V-shape: by 2030, testers will need more than basic knowledge of related fields. They will need real depth in several areas to stay flexible.
- Future test organizations work without hierarchy or fixed roles. Experts come together in small units and staff projects on their own, with no traditional management layer above them.
- ChatGPT can already write complete test plans, run tests and recommend whether to go live, but people still have to check the results, because they are not yet reliably correct.
- Exploratory testing is gaining weight because it covers the ground that machines and AI can’t handle reliably, as long as it is done in a structured, methodical way.
- Self-organization and personal responsibility become a requirement for every tester, because a model without hierarchy no longer has test managers who carry responsibility on everyone else’s behalf.
Why Test Organizations Will Have to Change in the Coming Years
The test organization of the future will be flatter, more connected and more technically demanding. Two forces are driving this change: AI tools such as ChatGPT and the broad shift to cloud technology. Both take routine work off people’s plates and raise a new question: what do we actually need testers for?
Nicolas Nwabueze takes a sober view. When test automation arrived, people worried that testers would become redundant. That never happened. AI is repeating the same pattern: the work doesn’t go away, but the tasks and the skills they call for are shifting.
So if you work in quality, the question to ask is less whether a machine will replace your role. The more useful question is which activities you can hand over to tools, and which ones only get room to grow once you do.
What ChatGPT Can Do in the Test Process Today
ChatGPT can take over parts of the classic test management chain, from the test plan through test execution to the go-live recommendation. In an example we walked through, the tool really could cover these steps.
Very few people use it in practice yet. A rough figure from the field: around 70 percent are looking into ChatGPT, but only about 5 to 10 percent actually use it. The reason is caution, not a lack of interest. The results aren’t reliable enough yet to trust them blindly.
That creates a new, ongoing job for test management: reviewing and validating what the AI produces. What ChatGPT delivers is a suggestion, not proof. Nobody should assume something is correct just because it was generated.
Unit testing is where AI clearly pays off. Developers often don’t enjoy this work, and this is exactly where ChatGPT does well. Take that load off developers and they have more time for features. It eases a familiar tension: when everything has to be tested, the throughput of new functionality drops.
From I-Shape to V-Shape: How the Skill Set Is Shifting
What testers need is moving from deep knowledge in one niche toward broad adaptability. Nicolas describes three stages in this development.
| Model | Timeframe | Profile |
|---|---|---|
| I-shape | around 2000 | Expert in one area, nothing else |
| T-shape | around 2014/2015 | Expert in one area, basic knowledge in related areas |
| V-shape | forecast for around 2030 | Expert in one area, deeper knowledge in adjacent areas |
The difference between T-shape and V-shape lies in how deep the secondary areas go. With a T-shape, a basic grasp of neighboring topics is enough. With a V-shape, that knowledge runs deeper, so you can move into another area quickly and do real work there.
The reason is adaptability. If a test manager with some programming knowledge suddenly finds AI taking tasks off their hands, a narrow profile won’t help much. Broader and deeper skills mean you keep delivering value somewhere else.
In practice, that means taking new tools and disciplines seriously. Examples include Azure DevOps, which few people have really mastered, and requirements engineering, which test management is likely to get more involved in.
Cells Instead of Hierarchy: A Test Organization Without Fixed Roles
The future model presented here does away with roles and hierarchy. Test managers, test coordinators and testers are replaced by small units called cells. Experts gather in these cells without a boss, and projects are staffed from them.
A Forrester study backs this direction, comparing today’s IT organization with tomorrow’s along four axes:
- Structure: hierarchical today, small and non-hierarchical in the future.
- Skill set: fairly generalist today, more specialized in the future.
- Speed: slowed down by hierarchy today, much faster in the future.
- Reach: internal to the company today, external too in the future, meaning offered as a service to others.
That last point means more networking. An established test process doesn’t have to stay in-house; it can also be offered to other organizations. In the same way, one service provider can supply whole areas to several clients.
The model is more than theory. During our research, we came across an IT service provider in Munich that already works this way. People in the audience and at an evening event also came forward because they recognized the approach from their own work. Cassini, where Nicolas works, reorganized in 2019 and at least flattened its hierarchy.
Cloud Technology Makes Organizations Faster
The move to the cloud is the second big driver of change, and it’s picking up speed. According to a KPMG report, 84 percent of companies in Germany have moved to the cloud, up from 37 percent ten years earlier.
The biggest advantage is speed. Restoring a system from the cloud is much faster than setting up a local machine from scratch over and over again. Faster recovery means a faster organization.
The cloud also makes possible the ways of working we now take for granted. Remote work and new work depend directly on it. Working together on a document or on source code no longer depends on where you are.
Then there’s the environmental footprint. Companies in the cloud tend to use less energy and emit less CO2. That’s one more reason the switch appeals to many of them.
Why Manual and Exploratory Testing Is Becoming More Important
Manual testing keeps its place and is gaining weight. Exploratory testing stays a human domain, because it covers things machines are worse at picking up.
The logic of the test pyramid still holds. A lot can be automated, and AI can take over entire test levels. But in the end, some tests should still be carried out manually and exploratively, backed up by your own spot checks of the AI’s results.
This is where a common misunderstanding comes in. In practice, exploratory testing often serves as a stopgap when people don’t want to test properly. The future calls for the opposite: deliberately designing good exploratory tests and running them in a structured, methodical way to find defects.
“Maybe this really is an area where we as humans will get even stronger. There are things the machine doesn’t test that well.”
(Nicolas Nwabueze)
How to Prepare for This Future Today
Individuals can adapt faster than whole companies, and that’s your opportunity. A company has to rebuild large processes. For you as an individual in testing, switching to a new model is much easier.
The first lever is your skill set. The path from I-shape through T-shape to V-shape means learning new things, mastering new tools and digging deeper into neighboring disciplines.
The second lever is attitude. A model without roles and hierarchy demands a lot of self-organization and personal responsibility. That responsibility no longer sits with one test manager but with everyone, each in their own area. Not everyone is cut out for that, and this is exactly where personal development pays off.
The third lever is how you deal with AI. Many people were afraid of tools like ChatGPT at first. Once they get a real look at it, that changes noticeably, and they start to give it a try. A practical way in is the place where developers don’t like to test, such as unit tests. Many people know the benefit, but very few actually put it to use. That’s the next step.
Frequently Asked Questions
Will Artificial Intelligence Make Testers Obsolete?
No. Even when test automation first emerged, there were concerns that testers would no longer be needed, but that didn’t happen. With AI, the pattern repeats itself: the work doesn’t disappear, but tasks and the required skills shift. Rather than asking whether testers can be replaced, it’s more useful to ask which tasks can be delegated to tools and which tasks will gain more prominence as a result.
How many professionals in the testing field actually use ChatGPT for their work?
Significantly fewer than those who are exploring it. A rough estimate from real-world experience: about 70 percent are looking into ChatGPT, but only about 5 to 10 percent are actually using it. The reason is caution, not disinterest. The results aren’t yet as reliable as they could be to be trusted without verification.
Can a language model provide a complete test plan?
Yes, but only as a suggestion. In a simulated example, ChatGPT was able to map out the entire process, from the test plan through test execution to the recommendation for going live. This creates a new ongoing task for test management: reviewing and validating the results. What the model generates is therefore not yet proof of correctness.
At what point in the development process does AI support pay off the fastest?
With unit tests. Developers are often reluctant to do this work, and this is precisely where ChatGPT delivers good results. If developers are relieved of this burden, they’ll have more time for features. This addresses a well-known trade-off: when everything has to be tested, the throughput of new functionality decreases. This point is particularly well-suited as a practical introduction to AI.
Why is in-depth knowledge in a single area no longer sufficient for testers?
Because adaptability determines long-term employability. Around the year 2000, the I-shape profile, with expertise in a single field, was sufficient; by 2014/2015, the T-shape model introduced the need for basic knowledge in related fields. Looking toward 2030, the V-shape model expects deeper knowledge in adjacent areas so that a change in field of work remains possible. Examples mentioned include Azure DevOps and requirements engineering.
Are there organizations that actually operate without test managers or a hierarchical structure?
Yes. During the research, an IT service provider in Munich was found that already operates this way, and practitioners in the audience also recognized this approach. Cassini, where Nicolas Nwabueze works, reorganized in 2019 and at least flattened its hierarchy. In this model, small units, known as “cells”, replace fixed roles such as test manager, test coordinator, and tester.
What advantages does the cloud offer beyond cost considerations?
Speed. Restoring a system from the cloud is significantly faster than setting up a new local computer, and faster recovery means faster operations. Added to this are remote work and the ability to collaborate on documents or source code regardless of location, as well as lower energy consumption. According to KPMG, 84 percent of companies in Germany were using the cloud, compared to 37 percent ten years earlier.
Is exploratory testing just a stopgap solution when there isn’t enough time for structured testing?
In practice, it’s often used that way when people don’t want to test properly. That’s a misconception. Exploratory testing specifically covers the areas that machines and AI are less capable of addressing, which is why it’s gaining importance. The prerequisite is that the tests are deliberately designed and structured, carried out methodically, and supplemented by random checks of the AI results.
