“How can we accelerate our testing processes without sacrificing quality?”
“What low-code tools and AI capabilities best fit our existing environment?”
“How do we ensure that our team goes through this transition smoothly, and gets real value from automation?”

Among clients of M2Q.be, we hear these questions more and more often. Not as a theoretical consideration, but from an urgent need. Organizations feel that manual testing and isolated automation no longer suffice. However, the combination of low-code test automation and AI offers a tipping point: an opportunity to make testing smarter, faster and more scalable.

Everyone is experimenting with automation and AI. But experimenting is not the same as creating structural value.

From uncertainty to a clear strategy

IT and QA managers know that traditional testing methods have reached their limits. Test cycles take too long, script maintenance is too expensive, and the pressure to release faster is increasing. Low-code automation (with tools such as Tosca from Tricentis, Katalon, Testcomplete from Smartbear, Leapwork and UFT from Open Text) already offers a solution, but AI adds an extra layer: intelligence, self-learning and autonomy.

Yet crucial questions remain unanswered:

• What low-code tools and AI features are relevant to our testing environment today?
• How do we seamlessly integrate them into our existing workflows?
• What does this mean for our testers, automation engineers and QA processes?
• How do we avoid isolated initiatives with no lasting effect?

The answer lies not in one tool or one technology, but in a different way of working – where low-code automation and AI reinforce each other.

Why 2026 will be the tipping point

Those who still rely on 100% manual testing or isolated script automation in 2026 risk falling behind. The shift is clear:

• Low-code platforms make test automation accessible, scalable and low-maintenance.
• AI adds intelligence: self-learning models, autonomous decision-making and predictive analytics.

What makes this year different?

We are moving from loose automation scripts to integrated ecosystems in which:

• Tosca (Tricentis) not only generates test cases, but uses AI to predict risk and optimize test coverage.
• Katalon not only creates low-code scripts, but deploys AI for self-healing tests and automatic bug detection.
• SmartBear not only runs API tests, but uses AI to predict performance bottlenecks.
• Open Text not only offers enterprise scalability, but integrates AI for adaptive testing strategies.

The result?

A fundamental shift in how quality is assured:

• Test design becomes risk-driven, AI-supported and reusable.
• Automation becomes fast, flexible, low-maintenance – and self-correcting.
• Test knowledge is centrally managed, always up-to-date and AI-enriched.
• Testers are evolving from implementers to strategic directors, with AI as an intelligent assistant.

Specifically, what does this mean for your testing process?

The impact is measurable and touches multiple layers of your QA organization:

Accelerated and smarter test design

• Low-code tools analyze requirements, user stories and code changes to generate test cases within minutes.
• AI goes a step further: it predicts critical test paths, identifies missing scenarios and optimizes test coverage based on historical defects and risks.
• Example: Tosca’s AI-assisted model-based testing reduces design time by up to 60% and increases accuracy.

Sustainable and self-learning automation

• Low-code platforms generate, maintain and restore tests automatically when changes occur.
• AI adds self-healing: tests adjust themselves when UI elements change, detecting flaky tests before they fail.
• Example: Katalon’s AI-powered self-healing reduces maintenance costs by up to 70% and lowers false positives.
• Performance-testing becomes predictive: SmartBear’s AI predicts bottlenecks before they occur, based on historical data.

Centralized, AI-enriched knowledge management

• Instead of scattered documentation, a living, AI-powered knowledge base emerges:
  • Defects, risks and best practices are automatically analyzed and updated.
  • New team members are productive within days, thanks to AI-driven onboarding and contextual help.
• Example: Open Text AI knowledge graphs link test results to business impact, enabling teams to make smarter decisions.

From performing to directing

• The role of testers is shifting from manual execution to strategic direction:
  • Less repetitive work, more focus on quality strategy and risk analysis.
  • Human-in-the-loop validation: testers define the frameworks, while AI and low-code tools do the operational work.
  • QA is becoming a strategic discipline, where human expertise and AI autonomy reinforce each other.
• Example: A tester reviews AI-generated test cases, adds contextual knowledge, and has the tool make updates automatically.

Technology alone is not enough

While the possibilities are impressive, in practice we often see the same pattern: organizations invest in powerful tools and AI, but get only a fraction of the potential out of it. Reason?

• Lack of knowledge about what low-code + AI can and cannot do.
• Insufficiently adapted processes for integrated tooling and AI agents.
• Teams not ready for transition.
• Mismatch between tools, data and QA maturity.

M2Q.be: your partner in low-code + AI test transformation

At M2Q.be, it’s not just about the technology. We help organizations really take advantage of low-code automation and AI, on multiple levels:

• Identify concrete use cases tailored to your context, maturity and business goals.
• Knowledge of low-code tools (Tosca, Katalon, SmartBear, Open Text, Leapwork) + AI capabilities – and how they work together.
• Prepare teams for efficient use of low-code + AI, with training and change management.
• Redesign QA processes so that automation and AI not only accelerate, but also add structural value.

In short, we make sure you are ready for the transition, not just implementing a tool or AI model.

Looking ahead to 2026: what will change?

The combination of low-code automation and AI will not replace manual testing, but it will fundamentally change it. Teams that invest in knowledge, mindset and proper integration today will build a lead that will be hard to catch up with.

By 2026, QA teams will:

✔ Faster release without quality loss thanks to AI-optimized test suites.
✔ Have greater insight into risks and impact of changes through predictive analytics.
✔ Play a more strategic role within product development as directors of quality.

The question is not whether low-code and AI will transform your testing process. The question is: Are you ready?

📩 Get in touch and find out how M2Q.be future-proofs your testing process with low-code automation and AI. 📞 0472.593.797 ✉️ jurgen.meheus@m2q.be