Commercial teams in major pharmaceutical companies remain largely unchanged from their 2010s structure, even as AI reshapes the industry’s commercial environment. Despite significant investment in commercial AI tools, these teams focus primarily on incremental efficiency gains—faster call planning, quicker reporting, and accelerated content approval—rather than redefining what commercial operations could become.
The Siloed Nature of Pharma Commercial Operations
Pharma commercial teams continue to function in silos despite decades of integration efforts. Sales teams battle daily for limited time with healthcare professionals, while marketing departments analyze agency data for tactic performance. Digital teams optimize customer relationship management platforms like Veeva and Salesforce, and market access teams manage complex negotiations and compliance requirements. Each function operates as a separate thread in a tangled web, attempting to manage AI-driven changes within rigid, single-threaded systems. For instance, sales representatives may receive AI-powered next-best-action tools that remain bolted onto static quarterly planning cycles, failing to address fundamental operational improvements.
The core question remains: if commercial functions were designed from scratch today, would they resemble current structures? Jackson hypothesizes they would not, suggesting digital health offers a contrasting model. Unlike pharma’s broad focus across therapeutic areas and patient populations, digital health firms prioritize narrow, targeted solutions. This focus shapes commercial functions built around fewer, clearer outcomes rather than multiple sub-functions optimizing individual metrics. Jackson likens current AI adoption in pharma to the early internet era, when organizations merely digitized existing processes without reimagining workflows. She contrasts this with digital health’s ability to deploy features in weeks based on user behavior, whereas pharma teams might spend an entire quarter gathering requirements for minor adjustments.
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They adopt partnership-driven models from inception, while pharma often builds internally before considering collaboration, leading to organizational fatigue and delays. Jackson envisions a pharma commercial function redesigned with radical focus: merging disparate dashboards into a unified data layer for HCP engagement and pipeline tracking, replacing traditional field forces with AI-powered cognitive command centers that coordinate market access and content teams simultaneously, and shifting from static sales cycles to deeper, less frequent HCP interactions via AI-native platforms like OpenEvidence.
Consolidating Data Infrastructure
The source highlights how pharma’s current approach creates fragmented visibility across commercial functions. Marketing teams analyze agency performance while digital teams manage CRM platforms separately, leaving sales representatives without unified insights. This division becomes particularly problematic when AI tools are implemented within existing silos rather than across them. The result is technology that accelerates inefficient processes instead of enabling better ones.
AI-Powered Coordination Models
The source describes how traditional pharma commercial operations route issues through sequential approval chains. When a sales account encounters complications, it might pass from field team to market access to content before resolution. This linear process creates delays that AI tools struggle to overcome when they remain integrated into existing workflows. Each function operates independently, using its own metrics for optimization rather than considering portfolio-wide outcomes.
