For decades, the healthcare professional (HCP) target list has been the cornerstone of pharmaceutical commercial strategy. It shapes territory design, field deployment, call planning, and marketing investment. But a new opinion piece published by the Forbes Technology Council argues that this static model is obsolete, and that the industry must shift toward a dynamic, AI-driven "living decision layer" focused on decision moments and continuous learning.
The article, published on Sep 01, 2026, at 06:15am EDT, was written by Rahul Saluja, a technology and business leader and Forbes Councils Member. Saluja contends that the traditional approach, analyzing historical performance, segmenting HCPs, assigning priorities, and building a plan, is reaching its limit. The problem, he argues, is that a static target list is a snapshot, while commercial reality is a moving picture.
Why the Static List Fails
An HCP's priority can change due to shifts in patient populations, access conditions, new clinical evidence, or digital behavior. Yet the list often remains unchanged until the next planning cycle. Traditional targeting answers "Where should we focus?" But the market now demands an answer to "Where should we act today?"
Saluja illustrates the gap with a concrete example. A representative may have 100 designated priority HCPs but needs to know which five deserve attention this week. The list tells them who matters in general, not who matters right now.
Access is becoming more limited and selective, compounding the problem. Veeva's 2024 field trends research reported that U.S. HCP access fell from 60% to 45%. That same research found that half of accessible HCPs limited engagement to three or fewer biopharma companies. When access is constrained, interactions must be timely, relevant, and worth the HCP's attention.
More Data Is Not the Answer
Saluja is careful to note that adding more dashboards, alerts, scores, and recommendations does not automatically produce better decisions. Field teams need context, not just a high score. An advanced targeting model can still become another static list, only with better mathematics behind it.
The next generation of commercial intelligence, he writes, will focus on the "decision moment." A decision moment occurs when new information changes what the organization should do. These moments can be triggered by prescribing behavior, emerging patient need, digital engagement, or channel preference.
AI can identify changes faster than a quarterly planning process. But capturing value from AI requires changing how decisions are made, not just deploying new tools. Saluja argues that a useful commercial intelligence system should answer four questions: "What changed? Why does it matter now? What should we do next? What did we learn from the response?"
Learning From Every Recommendation
The final question is what separates a recommendation engine from a learning system. Saluja says the system should learn from both accepted and rejected or modified recommendations. That feedback loop is essential for improvement.
AI can detect patterns across millions of data points, but a representative may understand relationship history, local access dynamics, or customer context not in the data. The stronger model, Saluja argues, is one where AI narrows the decision space, surfaces relevant signals, and explains why an action is recommended.
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The role of AI is to help the field make better decisions, not make decisions for the field. Explainability matters because a recommendation without a clear rationale is just another instruction. The goal should be better decision-making, not blind adoption.
The Target List's New Role
Saluja does not call for the abolition of the target list. It will not disappear tomorrow, nor should it. It will continue to provide a strategic view of the market. But it can no longer serve as the commercial operating system.
The replacement is a "living decision layer" that interprets signals, identifies moments, recommends actions, and learns from outcomes. In this model, the target list becomes an input, not the answer.
The old model asks "Who is on the list?" The new model asks "What changed? Why now? What next? What did we learn?" Saluja argues that the companies that can answer those four questions will build commercial systems that learn, rather than merely report.
A Shift in Human and Machine Roles
The article also reframes the relationship between human judgment and AI. Saluja emphasizes that the HCP has been treated as the primary unit of commercial planning, but the next generation will focus on the decision moment. That shift changes what field teams need from their tools.
More information does not automatically produce better decisions, he warns. A recommendation without a clear rationale is simply another instruction competing for attention. The human representative remains essential, bringing context that data cannot capture.
The piece is part of a series of Council Posts from the Forbes Technology Council, an invitation-only community for CIOs, CTOs, and technology executives. The opinions expressed are the author's own, published under license. The article also includes a voice experience generated by AI.
Saluja's argument is not that segmentation has lost its value. It is that segmentation alone cannot drive action in a market where access is shrinking and attention is scarce. The static list created false confidence in a constantly moving market, he contends.
The shift he proposes is structural. Instead of planning around who an HCP is, commercial teams should plan around what is happening now. That requires systems that can detect change, explain its significance, recommend a response, and learn from the outcome.
The target list will remain useful as a strategic map. But the operating system of pharmaceutical commercial strategy, Saluja argues, must become something far more dynamic. The question is no longer just who to call, but why today, and what to do when the answer changes.