AI can now write a test script faster than a human engineer can type. Ask a large language model to generate a sequence for a new board revision, and it will deliver working code in seconds. That speed is real, and it's tempting to treat that speed and facility as the whole story—why maintain a shared test framework when a prompt can produce the same result on demand?
The answer is that speed of test creation and test durability are distinct challenges; the first can be solved by AI code generation. The second challenge, durability, is particularly well addressed by OpenTAP, the open source test automation framework.
What AI doesn't replace
Ad hoc, AI-generated scripts are fast to produce but expensive to maintain. A shared framework like OpenTAP earns its keep in four ways above and beyond generation speed:
Consistency – when every engineer builds on the same core engine, plugin architecture, test plans, and drivers, results stay aligned without anyone having to coordinate it by fiat or brute force.
Hardware independence – because test logic is decoupled from the bench, swapping instruments or moving a test station to a new location takes minutes rather than requiring a rewrite and a re-validation cycle.
Traceability and compliance — every test plan, test run, and result is automatically versioned and queryable, so knowing exactly who ran what, when, and on which device under test (DUT) doesn't depend on someone's hand-written notes.
Reusability at scale – composable test steps get built once and dropped into any plan or station, turning one engineer's afternoon of work into a shared library the whole team can draw on today and tomorrow.
Inside this framework, AI adds real value, generating new steps and needed plugins faster, drafting instrument sequences, and summarizing results in plain language. The point isn't AI versus OpenTAP. It's AI working inside the structure that OpenTAP provides, rather than replacing it.
The further downstream, the higher the stakes
That distinction matters more as test programs move from the lab toward the production line, because both risk and infrastructure requirements grow at each stage.
During R&D
In early-stage R&D, AI is well-suited to fast, exploratory work, prototyping sequences before anyone knows what "normal" even looks like. The role of OpenTAP at this stage is reuse across board revisions and the version history, making later debugging traceable. Skipping past the structure provided by OpenTAP at this stage carries a cost – anything worth reusing on the next revision or handing off to DVT (Design Verification Testing) is simply more efficient and cost-effective to have built with OpenTAP from day one.
Design Verification
In DVT and characterization, AI can draft sweep logic, handle edge cases, and summarize large datasets. OpenTAP contributes the sweep primitives, cross-run comparability, and long-term result storage that make datasets meaningful over time. Building tests and test infrastructure without OpenTAP at this stage can be costly: without a shared foundation, results across units stop being genuinely comparable to one another and can become a “test salad”.
In Production
By the time a test program reaches manufacturing and production test, AI can help engineers write new steps faster and analyze yield and failure trends more easily. But OpenTAP is what delivers fleet consistency, hardware abstraction, and full audit trails at volume. Not building on OpenTAP at this stage costs the most of all — inconsistency at this stage can’t be confined to one station, it turns into audit gaps and yield risk across every unit built.
The path forward
The way to think about automation going forward isn't as a contest between AI-driven speed and OpenTAP-based structure. It's a pairing of the two, with engineers still in control of the hardware at every stage.
In R&D, engineers stay in control of the real hardware while AI moves fast on prototypes. The OpenTAP reusable step architecture carries that speed forward instead of needing to rebuild tests and infrastructure from scratch at the next revision. In DVT, AI drafts parametric tests and summarizes results while engineers validate against real hardware at every step. OpenTAP-based shared structure is what keeps comparisons across units transparent and traceable. And in manufacturing, engineers still hold final say over every test program even as AI helps write steps and flag yield trends faster.
AI hasn't made test frameworks obsolete. It's made the case for them clearer: the faster tests get written, the more a test program depends on the structure that makes those tests reusable, comparable, and trustworthy, today and across future revisions.