Column: Silicon systems design
Figure 1: “AI in DV” maturity view
Common failure pattern – the “tools first” adoption Many organisations begin “AI in DV” from the wrong end. They start with a model, a tool demonstration, or a vendor feature, then search for a problem that appears to fit. This can produce a collection of disconnected pilots: one team using AI for log analysis, another experimenting with test generation, another building a knowledge assistant, and another manually copying outputs into existing reports. The result is often duplicated effort,
inconsistent data handling, uncertain ownership and limited evidence of improvement. In some cases, AI- generated artefacts are useful but unverifiable. In others, the output is plausible but not reproducible. The danger is not simply that AI gives the wrong answer; the bigger risk is that
the organisation can’t explain how an AI-assisted conclusion was produced, reviewed, accepted, or rejected. That is a serious limitation in verification. Engineering sign-off is not based on convenience; it is based on evidence.
What is “AI in DV” maturity? Maturity should not be measured by how many AI tools a team uses, but whether AI
Figure 2: Local optimisation vs system-level verification impact (Source: semiengineering)
improves verification capability. A mature “AI in DV” approach starts with data readiness. Verification data is oſten fragmented across specifications, RTL revisions, regression logs, coverage databases and methodology documents. If this data is inconsistent or weakly governed, AI adoption will struggle. Workflow maturity and toolchain
integration are equally important. “AI in DV” must fit into regression systems, coverage
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