Tricentis predicts AI will transform UK software testing

Tricentis has outlined predictions for how UK organisations will change software delivery practices in 2026, with a focus on quality assurance as more teams adopt AI-generated code and automation.

The company said UK organisations face a shift in how they run digital transformation work as the Government expands the BridgeAI programme into industrial strategy sectors. It said teams need changes in process design, data governance and software quality practices.

Tricentis is a software testing company. It sells tools used for testing and quality engineering. The company positions quality assurance as a strategic issue rather than a task placed midway through the development process.

"The Chancellor's commitment to invest in AI and expand adoption support through programmes like BridgeAI is a vital signal that the UK intends to lead the next wave of digital innovation," said Andrew Power, Head of UK & Ireland, Tricentis.

"But investment alone is not enough. Fitting AI into old workflows risks joining the 95 per cent of AI projects that fail to deliver ROI. Today's digital complexity demands a fundamental shift: embedding quality from the start, redesigning processes and building confidence that your software is reliable, resilient, and truly solves business problems. This isn't just best practice; it's the only way to unlock real value in the AI era," said Power.

Tricentis framed 2026 as a turning point as AI-generated code becomes more common across development teams. It said organisations will struggle if they treat AI tools as add-ons to existing engineering processes. It said boards and regulators will increasingly expect evidence that systems behave reliably under change.

Risk Focus

The first prediction describes a shift away from blanket test coverage targets. Tricentis said UK organisations will place less emphasis on metrics such as 90% to 95% code coverage. It expects teams to focus on the areas of change that carry the highest operational and regulatory risk.

The company linked this change to the rise of AI-generated code, microservices and rapid release cycles. It said manual-heavy approaches will not keep pace with the volume of change. It also said board-level expectations will move toward explaining where risk sits in software estates and how testing aligns with it.

Autonomous Testing

The second prediction centres on the testing process itself. Tricentis said 2026 will bring wider use of AI systems that test AI-enabled software. It expects adoption of autonomous testing platforms that analyse changes, generate tests and execute them at scale.

The company argued that teams will turn to automation rather than increasing headcount as workloads grow. It described a move to continuous analysis of what changed across applications, test cases and infrastructure scripts.

Sector Models

The third prediction focuses on the type of AI models organisations deploy. Tricentis said sector-specific AI systems will become the norm in the UK, rather than general-purpose tools. It highlighted workflows in manufacturing, energy, financial services and life sciences as examples.

The company said models trained on sector workflows can better match the language and processes of specific roles. It also said that organisations will judge outcomes on measurable returns within those domains.

Engineering Guardrails

Tricentis also expects leadership teams to impose stricter controls on the use of AI agents in regulated and safety-critical settings. It said organisations will treat AI systems as high-potential tools that require guardrails and verification.

The company outlined requirements it expects to become more common. These include breaking work into explicit steps, codifying policies, demanding explainability and creating feedback loops. It said UK organisations will not accept confidence from models as a proxy for correctness in mission-critical environments.

Orchestration Shift

The fifth prediction describes changes in day-to-day engineering work. Tricentis said developers, testers and site reliability teams will shift from operating tools through manual interaction to orchestrating outcomes through AI agents. It expects engineers to issue commands to a "quality engineering layer" that routes work across systems and surfaces exceptions that require human judgment.

The company linked this to skills shortages and regulatory pressure in sectors in scope for BridgeAI and industrial strategy priorities. It said AI systems will take on tasks such as impact analysis, test selection and environment provisioning. It said teams will spend more time on validation, edge cases and risk decisions with business stakeholders.

BridgeAI sits within a wider push to increase AI adoption across the UK economy, with a focus on specific sectors and practical deployment. Tricentis positioned software quality as a constraint on scaling AI across services and products, particularly where reliability and compliance requirements apply.

"Today's digital complexity demands a fundamental shift: embedding quality from the start, redesigning processes and building confidence that your software is reliable, resilient, and truly solves business problems," said Power.