01
Executive alignment
Surface the real sponsor, the real blocker, and the decision rights that keep change moving.
People + AI operating model
People are the system. Training is the interface. OCM is the control layer.
USDM helps life sciences organizations make transformation stick: align the sponsors, qualify the humans, surface shadow AI, and keep adoption measured inside a governed operating model.
Human-in-the-loop model
1
Govern
Decision rights and executive alignment.
2
Qualify
Role-based evidence and qualification.
3
Enable
Champion network and workflow adoption.
4
Measure
Utilization, outcomes, and renewal.
The ALIGN story
The commercial story is simple: AI fails where people are left out.
ALIGN-AI exists because life sciences teams do not need more hype. They need a way to govern adoption, prove competency, and keep the work inside GxP boundaries while the tools keep changing.
Role-specific training beats generic enablement when teams are under regulatory scrutiny.
Shadow AI matters because informal usage usually shows up before formal governance does.
The output is evidence, alignment, and a practical adoption motion that leaders can sustain.
What it covers
People + process
The human side of regulated AI adoption, not just the tooling.
What it finds
Shadow AI
Qualification gaps, adoption blockers, and where the real work is happening.
Stakeholder map
Qualification path
90-day roadmap
ALIGN-AI in practice
Five pillars. One ugly truth: if you do not qualify the humans, you do not have a defensible AI program.
02
Role-based qualification
Create evidence and training that match the work people are expected to do.
03
Shadow AI visibility
Expose the unofficial tools and workflows so leaders can govern them instead of guessing.
04
Adoption design
Combine communications, champions, and workflow changes so the new behavior actually sticks.
05
Evidence sustainment
Keep records, approvals, and refreshes current as the tools and expectations evolve.
Operating model
The people layer runs on a cadence, not a wish.
ALIGN-AI gives teams a practical rhythm for assessment, qualification, enablement, and sustainment so transformation does not evaporate after launch.
1
Assess
Map the current state: readiness, adoption gaps, shadow AI, and the people process behind the problem.
2
Qualify
Build role-specific training and evidence so teams can defend what they are doing and why.
3
Enable
Give champions, managers, and practitioners the guidance they need to change behavior without chaos.
4
Sustain
Keep the program alive with checkpoints, refreshes, and a clean line of sight to evidence.
People-first adoption is easier to defend than tool-first enthusiasm.
Qualification artifacts should match the role, not the org chart fantasy.
Sustainment matters because change is a process, not an announcement.
What the program gives you
People work gets sharper when the evidence is live.
The point is not a prettier slide deck. It is a credible path to adoption, training, and oversight that can stand up in a regulated environment.
Readiness
Start with the people question
Where adoption is breaking, who needs help, and what has to change first.
Qualification
Built by role
Training, evidence, and oversight mapped to how work actually happens.
Shadow AI
Made visible
Find informal usage before it turns into governance debt.
Sustainment
Kept current
Reinforcement, champions, and refresh cycles that survive the launch.
Program ingredients
Stakeholders
The people who need to sponsor, decide, and participate.
Training
Role-based enablement built for how work is actually done.
Governance
Controls, approvals, and evidence that can be defended.
Adoption
Reinforcement that keeps the behavior alive after launch.
Articles and proof
Resources that carry the story without inventing a single thing.
Why AI Adoption Is Failing in Life Sciences
The people problem behind stalled adoption: governance, trust, and operational readiness.
Open resourceAI Readiness Assessment
A structured way to find the blockers before they become program drag or compliance debt.
Open resourceWork AI for Life Sciences: Why USDM Chose Glean
A practical look at governed knowledge access, adoption, and the human layer around Work AI.
Open resourceAI Governance for Life Sciences: Enterprise Framework
The governance side of the house: controls, accountability, and inspection-ready structure.
Open resourceSource notes
ALIGN-AI centers the people side of adoption: qualification, governance, and adoption support.
The assessment finds shadow AI, readiness gaps, and the missing operating discipline behind transformation.
Regulated teams need evidence, not theater: role-specific training, oversight, and defensible workflows.
The outcome is a cleaner path to adoption that leaders can explain, govern, and sustain.
Next step
If people are the bottleneck, fix the operating model.
Start with the assessment, surface the real blockers, and turn the people side into something measurable instead of mystical.
Start here
Put AI to work in life sciences — with the right guardrails underneath.
Start with a structured AI Readiness Assessment: fixed-fee, executive-ready, and built to surface the highest-value workflows first.
- Workflow inventory and risk classification
- Business value and readiness scoring
- FDA CSA + EU AI Act + ISO 42001 gap analysis
- Prioritized 90-day roadmap by impact, risk, and effort