AI in Functional Verification: What the 2026 Industry Data Reveals

For the first time, the 2026 Siemens EDA and Wilson Research Group Functional Verification Study provides industry data on AI/ML adoption in functional verification. Among 604 participants, 82.8% report use beyond evaluation or pilot projects, yet only 9.0% describe AI as broadly integrated across the verification flow. The leading applications are debug and root-cause analysis, testbench and test generation, and coverage modeling and analysis. The most frequently reported benefits are improved productivity and faster debug, while the primary barriers are integration with existing tools and flows, data availability and quality, and trust or explainability—not cost. For engineering leaders, the priority is to move from successful point solutions to connected, traceable, and scalable workflows that use trusted engineering context, protect IP, preserve human oversight, and measure outcomes such as time to root cause, regression efficiency, coverage closure, and design quality.^1,2

Introduction: Integration is Now the Question

For engineering leaders, functional verification has moved from asking whether AI can help to asking where it can be trusted, how it should connect to existing tools and data, and which engineering outcomes it can improve. An assistant that writes a script, a regression system that tunes from coverage feedback, and a workflow that plans and executes several tasks represent different operating modes.

The 2026 Siemens EDA and Wilson Research Group Functional Verification Study separates these forms of use. This paper presents the study's AI/ML results for the first time, examining how broadly AI/ML is used, where it is applied, how it operates, what limits adoption, and what benefits teams report. The findings how widespread task-level use but limited integration across the full verification flow.^1,2

Enterprise surveys report a similar gap between AI adoption and scaled integration, although their definitions and respondent populations differ from those of this verification study.^6,7

The need is growing as modern verification spans software-defined behavior, realistic workloads, multi-die integration, physical effects, security, and power constraints. AI can help teams generate artifacts, interpret results, connect evidence, and decide what to do next.¹⁴

How to Read the Results

The findings are based on 604 participants reporting on their current most important design and verification flow. AI/ML was defined broadly and may include established machine-learning methods, optimization techniques, and newer generative-AI systems. Except for the adoption-stage question, participants could select more than one answer, so percentages should not be added together. Later subgroup comparisons are descriptive and should be interpreted as directional. The Data Atlas and methodology paper provide complete information on sampling, question wording, respondent composition, and analysis.^2,3

Three findings should be read together. Widespread use coexists with limited full-flow integration. More autonomous operating modes coexist with concerns about explainability and incorrect results. Practical benefits coexist with integration obstacles. External comparisons provide context rather than percentage-for-percentage validation.