Every regulated organization evaluating GenAI for compliance work is standing on top of decades of ALM decisions nobody planned to defend in front of an auditor.
Tools got added. Migrations got run. Configurations got patched, then patched again, year after year, by whoever was on call at the time. Almost none of it ever got removed. Regulated organizations have been adding to their ALM environments for decades, and very few have ever deliberately taken anything out.
What's left: orphaned records, deprecated configurations, unmapped relationships, and taxonomy inconsistencies, stacked across every platform migration that prioritized continuity over correctness. The organization knows exactly where the data started. It cannot account for where half of it ended up.
That is the environment most organizations are now proposing to hand to GenAI.
The industry's own numbers make the stakes concrete. Gartner projects that 60% of AI projects will be abandoned before they reach production, because the underlying data was never made AI-ready. Close to nine in ten organizations report using GenAI regularly, but fewer than four in ten can point to a measurable earnings impact from it. Independent research puts the failure rate even higher: 95% of enterprise GenAI pilots produce no measurable financial return at all.
The failure here starts further upstream than the model. A large language model can only reason over what it is given, and in most ALM environments, what it is given is the accumulated residue of every platform decision made since validation went digital: screenshots standing in for records, PDF exports standing in for traceability, spreadsheets tracking eSignatures that should never have needed a spreadsheet in the first place. Nobody would hand a new hire that filing system and expect a defensible answer back. GenAI does not get a pass either.
The scale of what is riding on this is not small. 78% of enterprise data still sits in legacy environments, and the ALM market alone is valued at more than $4 billion today, on its way to nearly $7 billion by 2031. The life sciences AI market is projected to grow from $2.5 billion to $17.7 billion by 2030, well over 600% growth in four years. Organizations without governed data foundations will not participate in that growth. They will spend those years cleaning data before AI can be applied to anything at all, while competitors who did the governance work early move straight to deployment.
The proof point is not theoretical. In a recent two-organization ALM migration engagement, applying disciplined data governance to the archival decision, instead of moving everything forward by default, returned $30 million in five-year savings, an 18x return, and more than ten terabytes eliminated from active systems, all without sacrificing the audit trail regulators expect. The organizations did not choose that outcome by accident. They chose it by treating every disposition decision as one to be made consciously, with documented reasoning, against defensible criteria, rather than waved through on a deadline.
This is the argument behind Archive Manager and AgiliProve. Put plainly: deliberate, governed cleanup is the modernization work itself, the moment an organization creates a defensible transition point instead of carrying technical debt into a new platform.
Archive Manager exists to make historical data immutable and traceable, not just stored somewhere else. No more spreadsheets tracking eSignatures. No more drives full of zipped folders standing in for an archive. What gets preserved carries the context that makes it usable later, by a human auditor or by a model. AgiliProve exists to govern what is active, so the environment producing new records is validated from the start, instead of retrofitted for compliance after the fact.
Together, they create something that platform selection or migration planning cannot produce on their own: a defensible transition point. Historical data preserved with full traceability. Active data governed natively. An ALM environment positioned to produce trustworthy, auditable outputs at the speed GenAI makes possible, instead of outputs that only look fast until the first inspection.
That distinction, speed with traceability against speed without it, is where this gets decided. An organization that treats data governance debt as the last item on a migration checklist is building an AI strategy on top of the same orphaned records and unmapped relationships that created the debt in the first place. The output will look clean right up until someone asks where a specific data point originated, and nobody in the organization can answer with evidence.
The organizations that do the governance work first will not just deploy GenAI faster than the ones that skip it. They will be the only ones whose outputs survive regulatory scrutiny when it counts.
This is Decision Quality Intelligence applied to the least glamorous part of modernization: the data nobody wants to clean before the interesting part starts. Conscious, because someone has to decide deliberately what gets retired and what gets governed forward, instead of letting the decision default to whatever the migration timeline allows. Defensible, because every disposition decision needs a documented rationale an auditor can follow, not a note in someone's inbox. Continuous, because the discipline does not end at go live. It has to hold through the next platform change, the next audit, and the next inspector who asks where a record came from.
What does your ALM environment look like underneath the platform you are about to hand to GenAI, and would it survive someone asking?
[1] Gartner, February 2025, cited in ProcellaRX, ArchiveFlow Case Study, 2026.
[2] McKinsey, cited in ProcellaRX, ArchiveFlow Case Study, 2026.
[3] MIT NANDA, 2025, cited in ProcellaRX, ArchiveFlow Case Study, 2026.
[4] Saritasa, 2025, cited in ProcellaRX, ArchiveFlow Case Study, 2026.
[6] ProcellaRX, ArchiveFlow Case Study, 2026.