Generative AI assistants now draft VPAT narratives, ticket descriptions, and remediation summaries in minutes. For SEBI readiness packs and ADAprocurement, the question is not whether to use AI, but how to keep humans accountable when models hallucinate success criteria or mis-state product scope — especially on low-bandwidth networks where cloud co-pilots may time out mid session.
Where AI helps today
- Turning verbose audit PDFs into Jira-ready acceptance criteria
- Summarising retest evidence for board slides
- Suggesting plain-language alt text drafts engineers can verify
- Clustering duplicate defects across regional clones
Where AI creates regulatory risk
- Invented WCAG success criterion numbers or wrong conformance levels
- Boilerplate claims of \u201cfull compliance\u201d without sampling notes
- Leaking customer data into public model training (watch SaaS data policies)
Low-bandwidth India realities
Teams on shared office Wi-Fi or intermittent mobile hotspots should prefer tools with offline or progressive-upload modes. Chunk reports, compress screenshots, and sync when stable. Maintain a local Git-based source of truth so AI editors cannot silently overwrite canonical issue IDs.
SEBI & ADA evidence hygiene
Pair any AI draft with a sign-off matrix: author, reviewer (IAAP-certified), and release version. Store prompts and outputs alongside the PDF to prove diligence if regulators ask how conclusions were derived. Align narrative structure with readiness reporting templates and US Title II procurement norms referencing WCAG 2.1 AA.
See also
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