This week’s theme, if there is one: the adoption curve is steepening, and the accountability frameworks are not keeping pace.
PharmExec declares health communications redefined — and there’s no opting out
Sixty-seven to seventy-six percent of organisations are already using or piloting AI across health communications workflows, according to PharmExec’s latest read of the landscape. The piece is light on specifics and heavy on inevitability — which is itself useful data. When leadership and clients still treat adoption as a strategic choice rather than an operational baseline, this gives you something to put in front of them. The article flags governance and transparency as the central unresolved concerns, which will come as no surprise to anyone trying to get an AI policy through legal.
Ogilvy Health touts AI speed, nano influencers, and a branded bot called Ria
A rare inside view of how a major healthcare agency is publicly positioning its AI stack. Ogilvy Health’s “Ria” is agency-proprietary, so don’t expect to replicate it, but their framing of AI as a speed multiplier rather than a headcount reducer is worth noting as a benchmark for client conversations. The nano-influencer angle is a separate thread — but the combination of AI-accelerated content production and hyper-targeted distribution channels is where large agencies are placing their bets right now.
Gemini deleted 30,000 lines of code — then wrote a fake audit trail to cover it
This is the story to share with anyone still treating AI agents as write-once-check-later tools. Google’s Gemini allegedly purged a large codebase, then generated fabricated consultation logs and a misleading recovery report to satisfy its own rule requirements. The specific failure mode — an agent producing plausible-looking process documentation to mask what it actually did — has direct analogues in document automation and regulatory submission environments. If you’re building agent-based workflows for anything that touches a validation or audit trail, this incident belongs in your risk register.
Research: AI-built evidence tables attach citations that don’t exist
A paper worth circulating to your medical writers and whoever runs QC on AI-assisted outputs. When LLMs construct structured tables, they routinely source content from parametric memory — information baked in during training — then retroactively attach plausible-looking citations to rows that have no genuine source. For teams using AI to populate evidence summaries, SLRs, or any table where citation integrity matters, this is not an edge case. It’s a systematic failure mode that manual review processes weren’t designed to catch.
OpenAI is selling ‘guaranteed’ capacity — with no enforceable SLAs
OpenAI is asking customers to pay upfront for assured access to model capacity, a move that signals the infrastructure demand problem is real and not going away. The catch: ‘guaranteed’ here means a commercial commitment, not a contractual service guarantee. If your agency or a client has AI workflows embedded in regulatory timelines or submission schedules, this matters. A missed capacity window isn’t a vendor inconvenience — it becomes your delivery problem.
That’s it for this edition. Back Wednesday.
— Ned