2026年医药预测趋势：今年最重要的是什么

Pharma Forecasting Trends for2026:What matters most this year As the pharmaceutical industry moves into 2026, forecasting teams are operating in amarkedly different environment to even a year ago. The strategic importance of forecastinghas not diminished, but expectations ofhowforecasting delivers value have become sharper, In our 2025 report, we explored how AI, automation, and efficiency pressures were reshaping the forecastingfunction. Many of those themes remain highly relevant. What has changed is the tone of the conversation.Forecasting leaders are no longer being asked whether AIcanhelp, or whether processescouldbe streamlined.Instead, senior stakeholders want clear, defensible answers to a more pragmatic question:how are these The result is a shift from conceptual innovation to applied execution. Below, we outline the key forecasting trendswe see defining 2026, drawing on recent client discussions, project proposals, and evolving operating models From Generic AI Adoption to Practical Forecasting Use Cases AI continues to dominate discussions across the industry, but its role within forecasting is beginning tomature from pilots to workflow-level adoption. In previous years, much of the focus sat around data readiness,experimentation, or broad productivity use cases. Now, forecasting leaders are increasingly expected to Rather than positioning AI as a broad enabler, organizations are now exploring targeted use cases that directlysupport forecast generation and maintenance. These include automated trend extrapolation for in-market The emphasis is less on replacing forecasters, and more on removing friction from the process. AI is increasinglyviewed as a mechanism for efficiency – reducing manual effort, accelerating iteration cycles, and freeing upexpert time for interpretation, challenge, and strategic insight. It is being positioned as an enabler – empowering Importantly, this shift reflects a growing demand forspecific examplesrather than high-level ambition.Forecasting teams want to see what peers are doing in practice, what has scaled successfully, and where human ReadHow AI is and discover how tostay ahead in a marketdefined by innovation and ACCESS ARTICLE Centralization, Automation, and the Changing Ownership of Forecasts Alongside the evolution of AI, we are seeing a more structural shift inhow forecasting is organized – particularly for in-market brands. Severalorganizations are experimenting with increasingly centralized forecastingmodels, where baseline forecasts are generated centrally using automated Several organizationsare experimentingwith increasingly In its most advanced form, this model significantly reduces local forecastownership. Countries are expected to challenge outputs only where there While this approach is not universally adopted, its emergence marks a notable development. Traditionally, theearly years post-launch have been characterized by highly granular, patient-based models owned locally. Movingtoward volume- or sales-based automated approaches at earlier stages represents a meaningful departure from Also, at this stage, adoption appears uneven. Some organizations are taking incremental steps towardcentralization, while others are moving more decisively. The longer-term impact – on forecast quality, insightgeneration, and organizational agility – remains an open question. However, the direction of travel is clear: Pharma Forecasting Trends 2026at a Glance Doing More with Less: Efficiency Pressures Continue The operational pressures highlighted in our 2025 report have not eased. If anything, they have intensified.Forecasting teams continue to face reduced headcount, broader remits, and heightened scrutiny, all while This environment is reinforcing the push toward automation, simplification,and selective outsourcing. Many organizations are reassessing whichforecasting activities genuinely require deep internal expertise, and which This environment isreinforcing the pushtoward automation, At the same time, this is driving greater demand for flexible, on-demandforecasting support. Rather than rebuilding large in-house teams,companies are seeking targeted expertise to bridge capability gaps, A key question emerging for 2026 is whether this model is sustainable in the long term, or whether forecastingteams will eventually need to expand again as complexity continues to grow. The answer may ultimately depend Rethinking Global Consistency: Embracing Local Differences For years, global forecasting conversations centred on alignment: one model, one approach, one standard. Insome regions, experience has shown the limits of this ambition. We are seeing early signs of acceptance thatallowing for local differences can actually improve globalcoherence.Markets vary widely in data availability, forecasting maturity, healthcare systems, and cultural Instead, some organizations are exploring ways to let countries retain local models while ensuring outputsremain