They run in laboratories, impress stakeholders for a quarter, and then quietly fade. This is not a technology failure alone; it is a problem of decision design, operating model fit, governance, and measurable commercial value.
Below, we unpack why pilots stall before reaching commercial teams, show how the “last mile” differs in pharma compared with other industries, and outline pragmatic steps to turn pilots into repeatable commercial capabilities.
Misaligned scope: pilots solve tech problems, not business decisions
Too many pilots begin as technology showcases rather than precise interventions in real commercial decisions. An AI model that extracts adverse-event mentions from call notes or classifies email sentiment is interesting. But commercial leaders ask: “Will this raise HCP conversion? Lower cycle time to access? Increase sample uptake?” If a pilot does not clearly change the decisions that move revenue, it remains an experiment.
This “decision gap” is a common pattern in life sciences: technical teams focus on metrics like F1 score or extraction accuracy, while commercial teams care about behavior change and measurable outcomes. Leading practitioners recommend starting with a specific decision in mind—e.g., “which physicians to prioritize for a high-value launch interaction”—and designing the pilot to improve that exact decision path. McKinsey’s research on scaling AI in life sciences emphasizes this decision-first perspective as a key differentiator between pilots that scale and those that stall.
Data fragmentation: the commercial view is stitched across systems
Commercial teams operate across CRM, MLR repositories, KOL trackers, omnichannel engagement platforms, and payer intelligence feeds. AI prototypes often test on tidy datasets—labelled transcripts, curated slide decks, or anonymized CRM extracts—while production requires integration across all those systems.
Fragmented data undermines trust and introduces hidden engineering work: entity resolution, identity matching of physicians, linking samples to prescriptions, or combining payer rulebooks with field notes. Deloitte highlights that a large portion of enterprise data in life sciences is unused or siloed, and building pipelines to feed production AI is usually the most time-consuming part of scaling.
Regulatory and compliance friction: “explainability” is non-negotiable
Pharma commercial activities are tightly regulated: claims about product benefits, promotional content, medical information accuracy, and interactions with healthcare professionals are all subject to compliance and audit. A black-box model that recommends a messaging change or a pricing exception is not enough—legal, medical, and compliance stakeholders must be able to inspect and, if needed, override the recommendation with a clear audit trail.
That requirement adds cost and time. Pilots built without an “audit-first” mindset may generate useful outputs in a sandbox but fail when reviewers demand traceable evidence—source citations, policy references, and versioned prompt logs—before permitting field use.
User experience and workflow friction: the human-in-the-loop matters
Commercial users—field reps, medical science liaisons, market access analysts—have workflows that reward speed, clarity, and trust. If an AI assistant delivers a longer workflow, requires extra clicks, or produces suggestions that are hard to edit or contextualize, busy commercial staff will ignore it. Adoption is rarely automatic; it depends on embedding AI into the flow of work with minimal friction.
Successful pilots often redesign the human workflow simultaneously: they map how recommendations reach the rep (CRM popup, mobile brief, email digest), what metadata is attached (confidence, lineage, next steps), and how exceptions escalate (MLR or medical review). Without workflow redesign, pilots remain “nice to have” rather than “can’t live without.”
Governance, roles and ownership: pilots lack a product owner
Pilots commonly straddle organizational boundaries: they sit at the intersection of data science, IT, commercial ops, legal, and medical affairs. Too often, no one owns the end-to-end product life cycle. Data scientists stop when a model reaches acceptable accuracy. IT stops when integration is “viable.” But who is accountable for ongoing monitoring, prompt updates, and content changes? Without a clear product owner and a cross-functional operating rhythm, pilots lack a pathway to be hardened, maintained, and governed.
Organizations that succeed appoint an owner in commercial operations (not IT) who can prioritize features based on business impact, run training with users, and coordinate MLR/legal reviews. This mindset converts pilots into repeatable assets rather than one-off experiments. Harvard Business Review emphasizes the governance and ownership gap as a principal cause of pilot stagnation.
The cost fallacy: cheap pilots can create expensive downstream work
There’s a seductive narrative that low-cost cloud models and open-source tools make AI cheap. But in regulated pharma, saving on tokens often creates heavier compliance, data engineering, and oversight costs. A narrow, low-cost pilot that doesn’t account for audit logging, model drift detection, or content-review processes will produce brittle results in production—leading to rework that outweighs initial savings.
A better approach is FinOps-aware product design: optimize where appropriate (classification on lightweight models) and reserve heavier, explainable models or human review for high-impact decisions.
Measurement and value: pilots rarely define the unit of value
Commercial leaders want metrics that map to business economics: uplift in prescriptions, faster access approvals, reduced time-to-launch, or fewer compliance escalations. Many pilots measure proxy metrics—accuracy, recall, or latency—without an economic lens. To cross the chasm to commercial scale, pilots must define and measure the unit of value they affect, and provide a realistic time window for impact.
For example, an AI assistant that helps reps identify high-probability prescribers should be measured not only by identification precision but by conversion lift among targeted HCPs over a quarter, and downstream effect on sample uptake or formulary placements.
The talent and change gap: commercial teams need enablement, not demonstrations
Even when models work, commercial teams need pragmatic enablement: playbooks for how to use AI outputs, training scenarios, and simple guidelines that explain when to accept, edit, or reject a suggestion. Without this, pilots can fail because users don’t understand the trust thresholds or how to use the recommended content. Change management—training, coaching, and performance incentives—is as critical as the model itself.
How to design pilots that are built to scale
Start with a decision—define the commercial decision you will change and how success translates to revenue, access, or cost reduction.
Map the workflow—embed the output directly where users work; minimize clicks and provide editable suggestions.
Build auditability in—every recommendation must carry provenance: data source, prompt version, confidence, and policy citations.
Assign ownership in commercial ops—they own the roadmap, user adoption, and business metrics.
Design for integration from day one—include identity resolution, CRM mapping, and payer data connectors as production costs, not optional extras.
Run a FinOps plan—route cheap models for low-risk tasks and reserve higher-explainability resources for high-impact decisions.
Prepare for compliance—pre-approve templates, redaction rules, and escalation thresholds with legal/MLR.
Measure end-to-end outcomes—track conversion uplift, speed to access, and compliance exceptions, not just model accuracy.
Quick wins in commercial functions
- Lead scoring for launch prioritization
- Auto-drafted MSL summaries
- Payer objection preparation
- Digital content personalization
Each of these can be scoped to a single product launch or market and instrumented for measurable ROI.
External evidence and industry perspective
Industry analysts confirm these patterns. Deloitte and other life-sciences advisors note that AI’s promise is real but that data integration, governance, and organizational alignment are the bottlenecks to scaling pilots into business impact.
Next steps
If you have an existing pilot, here’s a rapid two-week discovery to establish a production path:
- Decision alignment: convene commercial, legal/MLR, data, and IT; pick the single decision to impact; agree on outcome metrics.
- Implementation scoping: map the workflow, identify required system connectors, define audit elements, and assign ownership.
For a production-ready roadmap that tailors orchestration patterns to your commercial stack, schedule a strategy call with A21.ai.