The future of quality engineering: trust, velocity, and AI readiness

The Central Tension

Quality Engineering (QE) has moved beyond testing to become a strategic enabler of innovation and competitive advantage. Yet only 15% of organizations have scaled AI across the enterprise, while 43% remain in experimentation and 11% have abandoned AI initiatives entirely – a striking increase from just 4% the previous year. This isn’t a story of inevitable progress. It’s a story of organizations grappling with fundamental questions about how to modernize quality practices while managing risk, building capability, and demonstrating value.

AI’s Impact: Real Gains, Uneven Results

Generative AI is delivering measurable productivity improvements—about 19% across test design, requirements analysis, and script maintenance. AI copilots and self‑healing frameworks are gaining traction.

However, one‑third of organizations see limited impact due to:

• Integration complexity (64%)
• Data privacy concerns (67%)
• AI/ML skills gaps (50%)

AI adoption is primarily an organizational transformation issue, not a tooling problem.

Automation: Progress Without Scale

Automation coverage remains low at 33% despite years of investment. Gen AI now contributes to 25% of new script generation, but most organizations struggle to scale beyond isolated wins. The missing ingredients are enterprise frameworks, business‑aligned metrics, and governance that prevents tool sprawl and fragmentation.

Test Data: The Underdeveloped Core

Although 95% of organizations use AI for test data generation, only 10% have full lifecycle integration. With synthetic data adoption rising (35%) and regulatory pressure increasing, Test Data Management (TDM) is becoming a strategic capability. Today, TDM remains fragmented, with unclear ownership and immature tooling – misaligned with the data needs of modern AI systems.

Agile Integration: The Unfinished Journey

Only 20% of organizations have fully embedded QE into Agile teams, and just 25% link quality metrics to business outcomes. Skills priorities – Gen AI fluency, core QE expertise, and domain knowledge – reflect the need for hybrid models that balance speed with governance. The goal is adaptable quality practices, not rigid methodology choices.

Shift‑Right: Present But Not Powerful

While 94% of organizations use production monitoring, only 13% apply insights proactively. Advanced practices like chaos engineering remain niche. Most treat shift‑right as monitoring rather than a continuous feedback loop. The opportunity is to turn telemetry and user feedback into systems that improve both product quality and QE effectiveness.

The Human Element in an AI World

AI isn’t replacing testers – it’s redefining what valuable testing expertise looks like. The emerging model is Collaborative Intelligence: human judgment and creativity amplified by machine scale and consistency. Future-ready QE professionals will need technical fluency with AI tools, but also critical thinking to validate AI outputs, ethical judgment to navigate AI limitations, and domain expertise to ask questions machines can’t formulate. Success depends on systematic upskilling, not ad hoc training completion.

Five Strategic Priorities for 2025

1. Connect QE to Business Impact: Move beyond technical metrics (defect density, automation coverage) to outcome measures – revenue protection, risk reduction, customer satisfaction, time-to-market. Quality must speak the language of business value.

2. Build AI-Ready Capabilities

Invest in Gen AI fluency, prompt engineering, and AI governance. Measure training effectiveness through application and impact, not course completion. Create career paths that blend testing expertise with AI skills.

3. Establish Intelligent Governance

Define clear ownership for AI in QE. Build frameworks covering ethical usage, compliance, data privacy, and AI model validation. Governance that enables rather than constrains is the goal.

4. Scale Beyond Pilots

Create explicit roadmaps with ROI milestones and success metrics. Most organizations know how to start AI initiatives – few know how to scale them. Bridge this gap with structured approaches to capability building and knowledge transfer.

5. Complete the Quality Lifecycle 

Integrate shift-left and shift-right practices into closed-loop systems. Use production insights to improve testing, testing insights to improve design, and design insights to improve requirements. Quality as continuous feedback, not sequential gates.

What Success Looks Like

Leaders in QE will be defined not by the most advanced AI or highest automation rates, but by strong fundamentals: clear strategy, skilled people, effective governance, and focus on business outcomes.

Quality Engineering in 2025-26 means enabling trust and velocity simultaneously – preventing costly failures while accelerating valuable innovation. It means using AI to amplify human expertise rather than replace it. And it means building organizational capabilities that adapt as technology evolves.

The report’s title “Adapting to Emerging Worlds”  – captures the essential challenge. The plural matters. There isn’t one future of quality engineering. There are multiple paths, shaped by industry context, organizational maturity, and strategic priorities.

The question isn’t whether AI will transform QE – it already is. The question is whether your organization can transform ‘with’ it.

Download and explore the full report and detailed benchmarks.