Testing and Quality Engineering

We are professionals in providing testing services. We will make your software testing more efficient, reduce IT costs, and ensure quality at all stages of your product's development.

Back to testing listing

AI in Software Testing and Quality Assurance

The use of artificial intelligence in software testing is now a key topic for most enterprise QA teams we work with. Over the past two years, the pace of software delivery has changed significantly: with AI agents, developers are now producing 2–3 times more pull requests and changes to existing code, creating new demands on testing. The business expects more frequent releases, a higher volume of changes, and faster feedback. QA teams therefore need to prioritize more effectively what to test manually, what to automate, and where to focus on the greatest risks. AI agent-powered test automation is becoming even more important in this environment, but it needs to be reliable, maintainable, and built on high-quality data. We can help you tackle this challenge: from risk prioritization and designing reliable test automation to integrating AI agents into everyday testing workflows.
AI in Software Testing and Quality Assurance

What has changed in QA with the rise of AI?

AI is no longer just an experiment within development teams. Developers now routinely use it to write code, modify existing features, identify bugs, and work with documentation. As a result, QA teams are dealing with more changes in less time, more frequent modifications to existing parts of the system, and greater pressure to provide fast feedback.

However, a faster pace of development does not automatically speed up the entire delivery process. If testing continues to rely on the same processes as before, the pressure simply shifts to the QA team. QA then needs to assess risks faster, choose the right approach to verification, and identify where automation or AI can deliver the greatest value.

This is changing the role of testing. QA teams need to make better use of the information already available within the team: requirements, code changes, test scenarios, automation results, and production data. AI can support analysis, test design, automation maintenance, and result evaluation. But it only works effectively when it has sufficient context and is integrated into the everyday QA workflow.

What do we do?

We integrate AI into testing processes. The goal is to increase QA performance without expanding team capacity, stabilize test automation, and bring the release cycle under control. All of this in regulated environments and without disrupting ongoing delivery.

We typically work with companies where:

  • developers have significantly accelerated their work with AI, while traditional testing processes are becoming a bottleneck,
  • testing only starts after development is complete and cannot be finished in time for release,
  • management has decided to adopt AI and expects tangible results within a few months,
  • experimenting with uncertain outcomes is not an option,
  • significant investments have already been made in test automation, but its effectiveness, coverage, and reliability are becoming difficult to manage, making it time to analyze and reassess the existing approach.

How do we work?

The service is built around three pillars. They can be implemented gradually or launched in parallel, depending on your organization’s level of maturity.

1) Strategic QA AI Audit

We map your entire delivery process: how you develop, test, deploy, and release applications, how your teams collaborate, which tools you rely on, and what slows down your delivery the most. Based on this, we identify bottlenecks and create a roadmap of AI opportunities prioritized by their potential impact.

The outcome is a prioritized plan of specific use cases, including estimated benefits and costs for each opportunity, along with recommendations on where to start. In regulated environments, we also assess risks and compliance with internal policies.

In addition to the presentation, you receive a detailed decision-making document for management. Optionally, we can follow up with a pilot implementation of a selected use case, allowing you to see its real-world impact before deciding on a broader rollout.

2) QA AI Enablement & Upskilling

Our training is primarily delivered through hands-on workshops. Your team works with AI on its own real-world use cases and leaves with tangible outcomes. We tailor the program to the team’s needs and the participants’ existing level of knowledge.

Training typically covers prompt and context engineering for testing, AI-assisted test creation and maintenance, building AI agents to improve efficiency, and the safe use of AI.

For a broader audience, we also offer public courses, such as GenAI for Web UI Testing.

3) AI Implementation Services

We implement specific AI solutions within your processes, focusing on areas with the highest measurable impact:

  • AI-assisted test creation – generating test scenarios and scripts based on requirements, user stories, and existing code. This can be applied to GUI, API, and performance testing.
  • AI for defect and log analysis – automated classification of bug reports, log analysis, root cause identification, and duplicate detection.
  • AI code review and shift-left quality – reviewing pull requests, monitoring test coverage, and detecting defects earlier in the development lifecycle.
  • AI agents for failed automated test analysis – accelerating root cause analysis and enabling clear, transparent communication of findings.
  • And many other use cases tailored to the specific needs of your team.

How does the whole process work?

Each implementation consists of three phases:

  1. Baseline measurement
  2. Pilot
  3. Final evaluation of the benefits, including recommendations for next steps

First, we measure the baseline using real-world metrics, such as time required to create tests, test automation instability, defect resolution time, and test coverage. The pilot (PoC) then runs within a clearly defined scope and against pre-agreed success criteria. Throughout the implementation, you have access to ongoing results and progress. Finally, we summarize the outcomes in a case study that can be used both to demonstrate the value of the investment internally and for external communication.

Why work with us?

We are an implementation partner focused on strengthening the quality of your products and services over the long term.

What does this mean in practice?

  • Independence from specific AI tools. We recommend a technology stack that fits your environment and regulatory requirements. We most often work with Claude Code and GitHub Copilot, but we design our solutions so that you can easily switch between tools as technologies and trends evolve.
  • Specialized expertise in software testing. We know which AI tools work in real-world environments and which tend to fall short after the first few attempts.
  • Experience in regulated industries: banking, insurance, automotive, and pharmaceuticals.
  • Focus on measurable results. Each phase has clearly defined metrics and acceptance criteria.
  • We transfer know-how to your team. Our goal is to enable your people to use AI effectively and integrate it into their everyday work.

If you would like to learn more about the real-world use of AI, take a look at our case study developed together with the delivery company Zásilkovna, owned by the Packeta Group. The company has been exploring AI solutions in areas where they make practical sense and can deliver measurable value. One such project was AI Perf Tester, an AI assistant designed to support performance testing of public APIs.

Frequently Asked Questions

What does “AI in software testing” mean?

A term describing the use of AI tools and techniques throughout the entire software testing process. It covers test scenario generation, defect and log analysis, pull request reviews, and test automation maintenance. The goal is to accelerate testing, increase test coverage, and free up testers’ capacity for higher-value activities.

How quickly will we see results?

After the audit, we launch a pilot for the selected use case, which delivers the first measurable results. Full implementation and a case study with demonstrable results are typically completed within one to two months of starting the engagement.

Is AI in software testing safe for sensitive data?

Yes, provided it is implemented within an appropriate governance framework. For regulated industries such as banking, insurance, and automotive, we design architectures that comply with data-handling requirements, address the risks of AI hallucinations, and ensure a clear audit trail. Our services also include support with developing internal guidelines and policies for the use of AI.

Will AI replace testers?

Probably not, but AI does mean a significant change in the role of testers. AI cannot replace responsibility for quality. Instead, it mainly takes over routine tasks such as test design, writing repetitive scripts, and analyzing results. This enables QA teams to handle more changes without a proportional increase in capacity. As a result, testers can focus on higher-value activities, such as making decisions about risks and priorities, defining testing strategy, and reviewing AI-generated outputs.

Which AI tools do you recommend?

We do not recommend any specific vendor-locked solution. We recommend a technology stack based on your environment, regulatory requirements, and the maturity of your team. We combine general-purpose AI assistants with tools specifically designed for QA. As part of the audit, we provide both short-term and long-term recommendations for your AI toolset. We most often work with Claude Code, GitHub Copilot, Microsoft 365 Copilot, and ChatGPT, but our principles can be applied to any other tool required in your specific context.

How much does implementing AI in software testing cost?

The strategic audit and training have a fixed scope and a fixed price, so you know the cost upfront. Implementation costs are calculated based on the scope and number of use cases. Each phase is designed to deliver clear, measurable value in relation to the investment.

Next Step

We start with a no-obligation 30-minute online meeting. We’ll discuss the current state of your QA processes, the challenges you are facing, and where you see the greatest potential. Based on our findings, we’ll provide specific recommendations for the next steps, with no obligation to continue.

Please use the form below to get in touch.

Do you need other testing services?

Let us know, and we will find a suitable solution for you.

By submitting the form you agree to privacy policy.

Formulář

Don't miss the latest updates

Leave us your email address and receive a regular dose of information about upcoming courses, events, and testing know-how.

By submitting this form, you agree to the processing of your personal data in accordance with GDPR and to receiving marketing emails.