Structured AI Experimentation

Your AI mandate deserves a properly designed experiment.

The Lab is the structured AI experimentation practice inside Proxa Labs. We define the right use case, design the experiment against your real constraints, measure what matters, and build the business case that turns results into action. Four phases. Each time-bounded.

Why Structured Experimentation

The discipline that matches the environment.

Structured experimentation is the only discipline built for environments where you can't afford to be wrong. It's the approach Bell Labs used to test hypotheses against real constraints before committing to production. It's the approach that shaped 25 years of practitioner biopharma commercial operating decisions — where the constraints are MLR review, launch timelines, and federated commercial structures.

We apply that discipline to AI pilots: define the hypothesis, design for evidence, measure honestly, decide based on data. The method is old. The application to commercial AI is new. The result is a Lab engagement that produces defensible evidence — not engagement metrics — and business cases the CCO, CFO, and CHRO will fund.

Every Lab engagement is structured around a single four-phase model. Each phase has a deliverable. Each deliverable feeds the next phase. The engagement produces either a funded AI roadmap or a defensible decision not to invest — both are valid outcomes.

In biopharma, a failed AI pilot doesn't just cost a budget line — it costs launch momentum and CCO trust. We exist to prevent that outcome.
Nina Patel, The Lab Research Lead, Proxa Labs
Why AI Pilots Fail

Three failure patterns account for most pharma AI pilot postmortems.

Our engagements are designed around these three most common patterns.

The use case is too broad
"Use AI in training" is not a use case. It's a mandate. Organizations that succeed with AI start with a specific problem, a measurable outcome, and validation that the problem is real — before they build anything.
The experiment isn't designed for evidence
A pilot scoped to succeed in controlled conditions proves nothing. A properly designed experiment tests the solution against your real constraints — governance, data, workflow, compliance — so the results hold up outside the demo.
There's no path to a business case
Pilots that work still fail to scale because no one built the business case while the evidence was fresh. Leadership doesn't fund learning metrics. They fund ROI framing, risk assessment, and a credible implementation path.
The Four-Phase Model

Define → Design → Measure → Business Case.

Each phase is time-bounded with a concrete deliverable. Most Lab engagements complete in six to ten weeks.

01
Define the Right Use Case
Map your landscape, identify where AI creates value, and produce a prioritized use case hypothesis. Deliverable: validated one-paragraph use case statement with success criteria, scope boundaries, and known risks.
02
Design the Experiment
Structure a pilot against your real constraints — compliance, IT, governance — not a controlled demo. Deliverable: experiment design canvas, success and failure thresholds, governance checkpoints, and exit criteria.
03
Measure Success
Define metrics before the experiment runs. Commercial performance, not engagement theater. Deliverable: pre-pilot baseline, instrumented measurement plan, and pre-registered analysis approach.
04
Build the Business Case
Translate results into a leadership-ready ROI model and implementation roadmap. Deliverable: CCO-ready business case with a phased rollout plan, investment model, and go/no-go decision points.
Active Research

Research that shapes how we advise.

Open research projects that inform our engagements and the Proxa Labs platform.

Actively recruiting
Open-Source HCP Avatar Engine
Crowdsourcing a real-time, open-source conversational avatar system via hackathon. $10K prize pool. Goal: reduce vendor lock-in for AI roleplay deployments across the industry.
Early results
Behavioral Analytics Correlation Study
Early data shows r=0.84 correlation between AI-assessed behavioral competencies and field performance outcomes. Full study ongoing with Stage cohort data.
In development
AI Readiness Predictive Model
Predictive model for pilot success probability based on pre-deployment organizational readiness scores. Training data drawn from 30+ biopharma advisory engagements.

Bring us your AI mandate.

We'll show you what a properly designed experiment looks like in your environment — and what evidence your CCO needs to fund the next phase.