Three QA Trends Defining 2026: A Perspective from Making Sense’s Head of Quality Assurance

As 2026 begins, software teams are working under very different conditions than they were just a few years ago. From what I see working closely with QA, product, and engineering teams at Making Sense, AI is now part of almost every conversation. Platforms are more connected and more complex, and the pressure to deliver faster while keeping costs under control keeps increasing.

In this environment, quality cannot be treated simply as a final checkpoint or a safety net at the end of the delivery cycle. What I see in real teams today is a clear shift. QA has become part of the infrastructure that allows businesses to move quickly without breaking under their own complexity.

From my perspective as Head of Quality Assurance at Making Sense, these are the three trends that are already defining how quality works in 2026. Not as predictions, but as the baseline modern teams are operating from.

1. QA is essential to how the business operates 

In my experience, the biggest shift over the last few years has not been purely technical. It has been organizational. QA moved from being a function primarily focused on late-stage validation to becoming essential to how teams keep systems stable as everything moves faster.

Today, QA plays a role similar to an operating system. It helps the business move safely and at the speed it needs by connecting delivery speed, risk, customer experience, and business outcomes into a single view.

As platforms grow more complex and teams are asked to do more with fewer resources, quality needs to be built into the process, not added at the end. When QA is absent or disconnected, things do not just slow down. They become fragile. Small issues compound, confidence drops, and teams lose the ability to make fast, informed decisions.

At the start of 2026, the expectation for QA in mid market and PE backed companies is clear. QA is not an execution function. It is a structural capability that helps organizations scale without collapsing under their own weight.

2. AI augmented QA is now standard, but judgment still matters

AI has moved into day to day QA workflows. It is already embedded in day to day workflows, and when used in the right places, it delivers real leverage.

From what I see across teams, AI is especially effective in tasks that used to consume a lot of human time without requiring deep judgment. Reading logs to identify potential root causes, generating test cases from requirements or past bugs, supporting exploratory testing, and flagging risk areas in pull requests are now much faster with AI support.

In practice, QA teams are already using AI to take a Jira story and automatically generate test cases, acceptance criteria, edge scenarios, and regression candidates. During exploratory testing sessions, AI can help surface signals such as changes in behavior compared to previous builds. In many cases, it can even support developers earlier by analyzing code changes and highlighting risky areas before QA is formally involved.

At the same time, 2026 is making the limitations of AI very clear. I often see teams get excited about AI, generate a large volume of automated tests, and still feel uncertain when it is time to release.

AI continues to struggle with flows that require deep domain knowledge, environments where data is incomplete or misleading, and complex scenarios where timing and sequencing matter. These gaps become more visible as systems grow more interconnected.

The biggest risk is not using AI too little, but using it poorly. More tests do not automatically mean more confidence. Without human QA professionals validating coverage, curating outputs, and guiding how AI is applied, teams can end up with noise, false confidence, and missed risks.

In 2026, AI augments QA. It does not replace judgment. The teams that understand this are the ones seeing real gains in both speed and confidence.

3. From Shift Left to Shift Everywhere

The idea of shift left has been discussed for years, but what I see now goes further. In 2026, QA is not just moving earlier in the lifecycle. It is embedded throughout it.

One of the most important changes is QA’s involvement in Discovery. When QA participates early, it brings a different lens. It helps spot ambiguity in requirements before development starts, surfaces risky assumptions in user flows, and works closely with Product to define acceptance criteria that reflect real usage. It also brings system level and user centered thinking from the outset.

This early involvement reduces rework and prevents late stage surprises that erode trust between teams.

When QA joins late, I usually see the same pattern repeat. More rework, more last minute decisions, and less confidence in every release, even when teams are clearly working hard. Modern teams move too fast, and systems are too complex for quality to be treated as a final phase.

What works better in 2026 is a combination of contract testing to keep teams aligned, observability practices that surface issues quickly, and shared ownership models where quality is built into the process instead of being inspected afterward.

Teams that embed QA everywhere, not just early, are the ones maintaining delivery speed without sacrificing stability.

Additional QA Trends Shaping 2026

Beyond the three trends discussed above, several additional signals are shaping how quality organizations are evolving as 2026 begins.

One of them is the maturation of test automation economics. Teams are moving away from treating automation as a volume exercise and toward evaluating its operational cost, maintenance effort, and impact on release confidence. In many environments, reducing noise and improving signal quality has become more valuable than expanding coverage.

Another signal is the rise of quality ownership across roles, not just within QA teams. Developers, product managers, and platform engineers are increasingly expected to own quality signals within their domains, supported by shared standards and tooling. This reduces handoffs and shortens feedback loops, especially in distributed teams.

A third trend gaining traction is the focus on environment and data stability as a quality concern. As systems rely more heavily on integrations, real time data, and asynchronous flows, unstable environments and inconsistent datasets have become a major source of false negatives and wasted effort. Addressing these issues is increasingly seen as part of quality strategy, not just infrastructure hygiene.

Together, these signals reinforce the need for QA practices that can scale alongside both technical complexity and organizational speed.

What Quality Should Mean in 2026

As 2026 gets underway, one thing is clear. The way teams think about quality directly shapes how fast they can move, how confidently they can release, and how well their systems hold up as complexity grows.

From my perspective, QA today sits at the intersection of technology, product thinking, risk management, and customer experience. Companies that treat quality as infrastructure rather than inspection are better positioned to move fast, adapt, and scale with confidence.

The trends shaping QA this year are not about tools or tactics alone. They reflect a deeper shift in how modern organizations think about building software that can handle complexity while still delivering results.

In that sense, QA plays a broader role than execution alone. It is about enabling sustainable progress.