Industries

• AI-enabled diagnostic, monitoring, and decision-support devices carry obligations under both medical-device regulation and, in the EU, the AI Act — often assessed by different teams against different checklists.
• Post-market performance monitoring for an AI model is a different discipline from traditional device vigilance, and the two are not always connected.
• A change-control process that lets a model be retrained without a full new submission depends on governance a notified body or the FDA will actually accept.

An AI Governance Readiness Assessment maps each AI-enabled device against its device-regulation and AI Act obligations at once. A system-by-system AI Risk Assessment documents intended use, human oversight, and known failure modes for each model, and AI Risk Management keeps post-market surveillance and change control running as models are updated. Manufacturers pursuing a certifiable program carry that evidence into ISO/IEC 42001 implementation.

What changes: one governance file that satisfies device and AI Act reviewers together, a defensible change-control story for model updates, and post-market monitoring a regulator recognizes.

• AI in genomic analysis, target discovery, and lab automation is often adopted by scientists rather than governed centrally — leaving no technical file if a regulator or acquirer asks.
• Investors and acquirers increasingly run AI-governance diligence before a term sheet, and gaps here can delay or reprice a deal.
• Discovery-stage models trained on non-representative data create downstream bias and reproducibility risk that can surface years later, in a trial or a patent dispute.

A right-sized AI Governance Readiness Assessment gives you a model inventory and data-lineage baseline without pulling scientists off the bench. A focused AI Risk Assessment classifies each model's risk level, and lightweight AI Risk Management keeps documentation current as models change. Everything is built to align with ISO/IEC 42001, so a later implementation project — if you need full certification — starts from a running head start.

What changes: a data room that already contains AI documentation, a diligence process that closes faster because the gaps are already closed, and models whose provenance you can defend.

• AI is now embedded in trial recruitment, protocol design, regulatory writing, and pharmacovigilance signal detection — often without GxP-aligned validation behind it.
• Generative AI used in regulatory submissions or medical writing raises data-integrity and audit-trail questions inspectors are now trained to ask.
• Quality and regulatory affairs teams are being asked to validate AI tools using frameworks written before generative AI existed.

Our AI Governance Readiness Assessment maps every AI touchpoint across trials, regulatory writing, and pharmacovigilance against GxP and the EU AI Act. A GAMP 5-aligned AI Risk Assessment follows for each system, and ongoing AI Risk Management keeps validation packages and audit trails current as models and protocols change. Organizations that want a certifiable management system move into full ISO/IEC 42001 implementation.

What changes: validation packages your QA team can defend in an inspection, a pharmacovigilance AI workflow with a real audit trail, and a submission process that does not stall on an AI question no one prepared for.

• Product often ships faster than governance is built — a common pattern at venture-funded companies with thin compliance headcount.
• FDA's Predetermined Change Control Plan pathway can let you update models without a new submission each time, but only with rigorous, auditable change-control governance already in place.
• ISO/IEC 42001 is becoming a procurement gate in enterprise and health-system RFPs the way ISO 27001 did for security — no certification-readiness, no shortlist.

An AI Governance Readiness Assessment benchmarks your current SaMD footprint. The AI Risk Assessment that follows feeds directly into a Predetermined Change Control Plan your regulatory team can use, and ongoing AI Risk Management keeps post-market monitoring and change control running. Companies chasing the procurement gate go all the way through full ISO/IEC 42001 implementation and certification readiness.

What changes: an ISO 42001-ready posture you can put on a sales one-pager, a change-control process that lets you ship model updates without re-filing every time, and a governance file that holds up to investor and enterprise-buyer scrutiny.

• AI in underwriting, claims, and fraud detection makes decisions about individuals — the category regulators and courts scrutinize most closely for fairness and transparency.
• Explainability and appeal rights are becoming explicit requirements, and a model that cannot be explained to a regulator is hard to defend to a customer.
• Governance sits across actuarial, data-science, compliance, and legal teams, and ownership of AI risk is often unclear.

We start with an AI Governance Readiness Assessment that inventories every model influencing a customer decision. A per-model AI Risk Assessment covers bias and fairness testing, data lineage, and explainability, and AI Risk Management establishes clear ownership, human review points, and monitoring for drift. Insurers that want a recognized framework move into ISO/IEC 42001 implementation.

What changes: documented fairness and explainability evidence for every automated decision, a clear owner for AI risk, and a governance position you can put in front of a regulator or a board.

• Enterprise and health-system buyers now send an AI-governance questionnaire with the RFP, and ISO/IEC 42001 readiness is increasingly the entry requirement — the way ISO 27001 became for security.
• Investors and acquirers run AI-governance diligence before a round or a close, and gaps here can slow or reprice a deal.
• Governance built reactively, one customer questionnaire at a time, doesn't scale and doesn't hold up under scrutiny.

An AI Governance Readiness Assessment benchmarks your current practices against ISO/IEC 42001 and the frameworks your customers cite. A focused AI Risk Assessment produces the model cards, risk registers, and technical files buyers ask for, and AI Risk Management keeps them current as the product changes. Companies chasing the procurement gate go through full ISO/IEC 42001 implementation and certification readiness.

What changes: an ISO 42001-ready posture you can put on a sales one-pager, a diligence file that closes rounds faster, and governance that scales with the product instead of chasing it.

• AI tools deployed across donor-funded programs often operate in dozens of jurisdictions with uneven data-protection law and no unified governance framework.
• Donors increasingly expect programs to demonstrate alignment with WHO's ethics-and-governance principles for AI in health — in grant reporting, not just intention.
• A single publicized AI-governance failure in one program can put the next funding cycle for the whole organization at risk.

We open with an AI Governance Readiness Assessment mapped to WHO's six AI-for-health principles across your program footprint, then run an AI Risk Assessment — including algorithmic impact assessments — for every beneficiary-facing AI tool. Ongoing AI Risk Management keeps donor and grant documentation current between audit cycles, and where a single certifiable framework across multiple countries is needed, we deliver an ISO/IEC 42001 implementation built to travel across jurisdictions.

What changes: grant reporting that demonstrates AI governance rather than asserting it, a program that can withstand a donor audit or inspector-general review, and a documented equity and safety case for every AI tool touching a beneficiary.