Role-based
Operators, Quality, clinical, regulatory, IT and leaders do not receive one generic pathway.
For organizations
Build practical AI capability around your team’s work
Choose the roles you need to support, see their current Academy learning paths, and inspect example evidence. If you need a company-specific program, use that starting point to discuss a separately scoped engagement.
Available now: 13 Academy tracks and 66 lessons. Custom cases, assessor-led services and team reporting require a separate proposal. No account or contact details are needed to explore the planner.
The Academy in 2 minutes
See what your team will learn
Explore role-based pathways, practical learning, and the requirements behind verifiable Academy badges.
Training supports adoption. It does not guarantee ROI or workplace qualification.
Plan your team's starting point
Map roles to relevant learning
Explore a sample scope without providing contact information. This is not a quote, seat allocation, or competency assessment.
Select one or more roles to see their required learning paths.
Enterprise scope and pricing require a written proposal. No team administration portal or bulk-license product is implied.
Organizational engagement
Enterprise pathways
Custom scope and pricing
Individual Academy purchases are priced for one learner. Enterprise work is scoped around your organization’s AI use cases, learner population, roles, systems, controls, delivery needs and evidence requirements.
- Company-specific pathway and cohort design
- Role, model, workflow and control mapping
- Delivery, assessment and reporting requirements
- A written proposal with the agreed scope and price
Please describe the need at a high level. Do not submit patient information, regulated records, passwords or confidential client material through this form.
Invitation to develop a pilot
Help shape an enterprise pilot
The current Academy contains 13 tracks and 66 lessons with knowledge checks, practical exercises and recorded submissions. The executive brief summarizes the sponsor decision and evidence gates. The full 19-module blueprint defines the proposed technical program for pharmaceutical operations. Its observed simulations, oral defense and qualification board are not currently available as an online course.
A focused starting point is AI-assisted SOP drafting and review for QA and document control. Together, we would define one workflow and its approved tools, develop a controlled training case and scoring rubric, arrange subject-matter review, and evaluate a small pilot before making claims about performance or expanding delivery.
- 1. Scope: agree the role, use case, learning outcomes and data boundaries.
- 2. Develop: prepare case materials, expected evidence, an answer key and scoring criteria.
- 3. Review: have qualified subject-matter reviewers challenge the case and calibrate scoring before learner evaluation.
- 4. Pilot: assess learner work, review misses and false positives, and decide what needs revision.
These are proposed development steps. No pilot results or independently evaluated performance outcomes are claimed. Scope, fees, timing and assessment responsibilities would be agreed in writing before work begins.
Risk-proportionate
Controls scale with intended use, data sensitivity, decision influence and regulated impact.
Evidence-bearing
Completion, attempts, practical work, badge criteria and versions can form a reviewable record.
System-aware
Academy evidence supports—not replaces—the sponsor's SOPs, validation, authorization and quality decisions.
Who it serves
Pharmaceutical, biotechnology, medical-device, CRO and CDMO teams
Proposed enterprise programs address organizations that need more than general AI awareness: they need role-specific boundaries, documented readiness, practical qualification and evidence that can be reconciled with training, validation, change-control and quality-system records. Common starting problems include unmanaged tool adoption, inconsistent human review, missing use-case inventories, weak change triggers and completion records that do not show competency.
Proposed enterprise delivery model
Six controlled stages
The sequence is designed to prevent a learning badge from being mistaken for authorization to use an AI system in regulated work.
01
Readiness baseline
A 30-question diagnostic examines governance, validation, data integrity, change control, oversight, vendor controls and workforce capability. Results identify evidence gaps; they are not a certification.
02
Role and risk mapping
Sponsors identify the regulated activities, jurisdictions, approved tools and decision rights for each learner population. The pathway is then assigned by role and risk.
03
Controlled learning
Learners complete the required tracks, knowledge checks and practical exercises using sponsor-approved examples and handling rules.
04
Assessment and evidence review
Current badges record scored questions, practical submissions and learner attestations. Any additional assessor-led performance evaluation must be separately designed, reviewed and agreed with the sponsor.
05
Operational transfer
The sponsor decides how the evidence supports training records, role qualification, supervision or change management within its own quality system.
06
Maintenance
Renewal, regulatory updates, role changes and material system changes trigger reassessment or targeted refresher work.
Deliverables to agree in the proposal
A learning and evidence package
- Readiness findings and a prioritized capability plan
- Role-to-track assignments with prerequisite logic
- Published learning objectives, framework mappings and source status
- Assessment attempts, recorded practical submissions and remediation history when persistent records are configured
- Verifiable badge records with curriculum version, issue date and expiry
- A handoff describing assumptions, open gaps and decisions retained by the sponsor
Sponsor responsibilities
What remains with the regulated organization
- Determining legal and regulatory applicability for the actual use case
- Approving tools, intended uses, data classes and authorized users
- Validation or assurance, supplier oversight, change control and incident handling
- Confirming learner identity when stronger assurance than a self-asserted display name is required
- Integrating evidence into the applicable LMS, QMS, training SOP and record-retention process
- Making employment, batch, clinical, quality and regulatory decisions
Illustrative planning sequence
A six-to-eight-week baseline and launch
This example is not a delivery commitment for the 19-module technical blueprint. New case development, assessor calibration and pilot review require separately agreed time; the written proposal controls the schedule.
Week 1
Scope, stakeholders, regulated context and data-handling rules
Weeks 1–2
Readiness assessment, evidence review and risk/role segmentation
Weeks 2–3
Custom pathway mapping, sponsor approval and baseline report
Weeks 3–6
Cohort learning, practical evidence, remediation and progress reporting
Weeks 6–7
Competency assessment, credential decision and exception handling
Weeks 7–8
Management readout, record handoff and maintenance plan
Customized upskilling pathways
Train for the AI your people actually use
The pathway published by the Academy is one working example, not a fixed template. We can build a company-specific pathway around your approved AI environment, operating model and quality controls.
Models and platforms
Approved providers, model families and versions, embedded copilots, agents, deployment settings, capabilities, limits and vendor-change notices.
Use cases and workflow context
The actual tasks, processes, records and decisions in scope, with permitted uses, prohibited uses, required human review and escalation points.
Roles, permissions and decision rights
Different pathways for operators, reviewers, approvers, Quality, IT, administrators and model owners, tied to access and supervision expectations.
Data boundaries
The data classes a learner may handle, prompt and output restrictions, record-retention expectations, residency needs and approved environments.
Controls and evidence
Relevant SOPs, validation or assurance status, risk tier, audit-trail needs, monitoring, exceptions, CAPA and change-control triggers.
Maintenance and change
Targeted refreshers when a model, prompt library, workflow, data source, permission set, policy or regulated use changes materially.
Published Academy tracks remain version-controlled. Each organization-specific overlay is sponsor-reviewed and versioned, so it can add local systems, examples, responsibilities and evidence expectations without silently changing the published credential standard. The sponsor retains approval authority for its AI systems, access, data, procedures and qualification decisions.
Enterprise reporting
An agreed report can show assignment, completion, attempts, safety-critical gaps, remediation, practical evidence status, credentials, expirations and unresolved exceptions. Access, export format, retention and identity assurance are defined before learner data is collected.
Engagement controls
Scope before access
Data: the sponsor defines approved examples and prohibits PHI, patient data, trade secrets or regulated records unless a written arrangement and suitable system controls permit them.
Identity: organization-sponsored cohorts may use roster verification, employer email, facilitated assessment or another agreed control. The method is documented with the credential record.
Change: material changes to curriculum, assessment criteria, authority status or delivery controls are versioned and communicated before they alter an active qualification decision.
Start with the evidence
Discuss an organizational readiness baseline
Contact Brian J. Drapeau directly. The initial conversation defines the population, regulated context, decision needed and data restrictions before any proposal.