The evidence already exists. Stop rebuilding it for every market.

Where the rebuild actually happens

Ask a market access team where their time goes on a new submission and the honest answer is rarely "analysis." It's re-assembly: pulling the same trial data, real-world evidence and literature into a new country template, reformatting citations to the local body's style, and reconciling numbers that quietly drifted between the last two versions of the dossier. See the full teardown in the HTA submission workflow: where teams actually lose time. The judgement calls — which comparator, which endpoint, how to frame the economic argument — are a small fraction of the elapsed time, and they're the part a person should actually be spending their time on.

A governed evidence base attacks the re-assembly, not the judgement. See AI value dossier automation for how dossier generation actually works over that base, and what the review step looks like once a first draft exists.

Keeping the base current without a project every quarter

A dossier is only as good as the evidence underneath it, and clinical and real-world literature doesn't stop accumulating the day a submission is filed. Continuous, automated systematic literature review — screening, extraction, and structured update of a living evidence base — keeps the underlying corpus current without turning every refresh into a fresh research project. See AI for systematic literature review for the honest account of where reviewer judgement stays in that process, because screening and extraction are not the same as deciding what a body of evidence means.

Localising without drifting off-label

A local value story has to speak the payer's language, follow the local evidence hierarchy, and lead with what that market actually weighs — while never introducing a claim the global dossier doesn't support. That's a narrower task than it sounds: adapting framing and evidence order per market, not generating new arguments per market. See localising payer value stories across markets for how that traceability is maintained when the same core narrative reaches eight different markets in eight different formats.

What MLR review actually needs to see

Medical, legal and regulatory review isn't slowed down by AI-generated content per se — it's slowed down by content it can't trace. Every claim in a generated dossier or value story carries provenance back to its source trial, real-world data point, or literature reference, with version history intact, so a reviewer can approve a specific sentence rather than re-deriving the whole argument from source. This is the same discipline that makes the evidence base reusable market to market: one schema, one provenance trail, regardless of which output it feeds.