Ask whether the model was built badly and you need an engineer. Ask whether a business used it carelessly and you need someone who has run the marketing.
Artificial intelligence litigation almost always splits along a seam that is easy to miss at retention. The first question is how a system was engineered, trained, secured or technically tested. The second is how it was selected, supervised, used, disclosed, measured and relied upon in real commercial operations - marketing, advertising, eCommerce, websites, analytics and consumer communications.
These call for different witnesses. An ai expert witness working in the second category speaks to applied and commercial use. Retaining that person to opine on model architecture is how an otherwise sound opinion gets attacked.
That is a narrower claim than "AI expert" as the phrase is usually marketed, and the narrowness is the point. Stratex Digital Marketing states the exclusion directly: not a software engineer building foundation models, not a computer scientist auditing source code.
Most applied-AI disputes reduce to whether a human being checked the output. The standard is not perfection; it is whether the organisation's review, approval and documentation practices matched what a competent operator in that industry would do.
That is answerable from evidence: approval workflows, version history, prompt and output logs, publication records, complaint handling, and whether disclosure obligations were met where the output was consumer-facing. Where none of that documentation exists, the absence is itself the finding.
As answer engines and AI overviews take share from classical results pages, disputes about visibility are migrating with them. Businesses now allege that AI systems misattribute, misquote, omit or misrepresent them - and the evidentiary problems are familiar even though the surface is new. Outputs are non-deterministic, rarely archived, and often unreproducible days later.
An ai search expert witness therefore has to establish provenance before substance: what was queried, when, in what interface, under what personalisation, and what was captured at the time. Screenshots of a generated answer without that context are worth very little.
Dan Stratford has used generative AI in commercial work since 2023 across research and strategy, ad development and campaign optimisation, content and creative production, and workflow support. That matters because applied-use opinions are about practice, and practice is difficult to testify to from reading alone.
Where a matter also needs source-code analysis, training-data engineering, cybersecurity, digital forensics or advanced statistical validation, the working model is a coordinated team with separately qualified specialists rather than an expanded claim of expertise. Scope and coordination terms are set out on the ai expert witness Dan Stratford page, and Stratex Digital Marketing takes confidential case enquiries at 720-985-7945.
An expert who gives opinions in disputes involving artificial intelligence. The field divides into technical experts addressing how systems are built and trained, and applied experts addressing how organisations selected, supervised, disclosed and relied upon those systems commercially.
Rarely, and claiming both invites challenge. The defensible structure is a scoped applied-use expert coordinating with a separately qualified technical specialist when the facts require source-code, architecture or forensic analysis.
Contemporaneously, with the query, timestamp, interface, account context and full response captured together. Because these systems are non-deterministic and unarchived, output that is not captured at the time is frequently unreproducible.
Documented human review before publication, verification of factual and performance claims, approval records, disclosure where required, and a process for correcting errors once identified.