2025 Commercial Predictions: Adapting to New Science, Adopting Standards and AI

New engagement models will meet complexity with simplicity

Biopharmas have been pursuing customer-centricity for more than two decades, but few would say they have achieved it. As commercial teams optimized within their own functions, systems and data have grown more disconnected, and engaging the customer as one company has gotten harder. At the same time, increasing therapeutic complexity, evolving engagement preferences, and shifting HCP access are deepening the need for a unified approach.

In response to this rising complexity, commercial organizations will simplify. Eliminating complex integrations and stitched-together workflows across systems will allow teams to meet HCP needs with greater speed and efficiency. A single view of the customer across commercial teams will reduce friction and enable collaboration across each customer interaction.

With all commercial teams operating in a single system, a marketing campaign will seamlessly incorporate a sales rep’s approved email as a tactic, target a message to an HCP’s preferred channel, and route HCP inquiries to the contact center, enabling instant cross-team collaboration for a quick and accurate response.

This simplified, unified engagement model will lay the foundation for true customer centricity — and a more innovative commercial operation.

Commercial and medical will converge to deliver complex medicines

Speed and innovation in the R&D pipeline are a boon for patients, but they’re creating a new challenge — quickly educating HCPs on an unprecedented volume of new and complex therapies. According to an analysis, the number of distinct drugs in development nearly doubled between 2012 and 2022. And nearly 70% of medicines in the pharmaceutical pipeline are potentially first-in-class treatments.

Biopharmas will shift focus to meet HCP’s complex informational and support needs tied to these new launches. Differentiating on science and service will be paramount, and companies will connect traditionally siloed responsibilities across commercial and medical. In addition, incentives will require a shift to rewarding patient support, delivering scientific information to HCPs, and removing barriers to treatment initiation — a model that both commercial and medical will align to for shared success.

New operational data standards will drive efficiency industry-wide

Operational data sources often use different names and formats to describe the same information. As biopharmas continuously add new data sets, this leads to tremendous inefficiency and waste. It can also negatively affect downstream processes.

A simple set of open data standards will create common understanding and interoperability. With a consistent structure for names, data types, and definitions, biopharmas don’t have to debate taxonomy to get started and can access insights across systems quickly to engage with customers more effectively.

This is especially critical for those deploying AI and advanced analytics applications at scale. Widespread adoption of new standards for operational data will advance the industry by making integrations faster, easier to maintain, and more accurate.

Dynamic state laws will drive digital privacy core competency

Currently, 20 U.S. states, including California, Virginia, and Colorado, have enacted digital privacy laws. Each law has distinct guidelines and definitions, making the landscape complex and challenging to navigate. As a result, biopharma privacy teams will significantly increase their focus and involvement with digital targeting and measurement in 2025. This shift will reshape how brands, media agencies, and technology partners collaborate with biopharma privacy teams to deliver effective campaigns that adhere to these dynamic state privacy standards.

With in-house core competencies around digital privacy, we’ll see brands streamline, working with fewer, highly trusted partners to manage risk. This will drive a pivot from traditional implicit data capture methods, such as digital tagging, toward a reliance on first-party data to measure advertising effectiveness.

Privacy compliance will become a foundational element in partnership evaluations, with more rigorous criteria for assessing both publishers and data partners to uphold the highest privacy standards.

MLR content review will emerge as an early winner in AI

Growing AI use cases for content creation, hygiene, and quality checks will drive record-high content volume, making it more difficult to get relevant messages to market. As a result, content teams and agency partners that focus AI investments on improving MLR review will be the first to see ROI.

By decreasing review cycles and reducing cycle times, AI-empowered MLR teams will accelerate compliant, accurate commercial content, despite the growing volume. These efficiencies will help move MLR review from the last stop in the content cycle to a proactive role with greater visibility and input into the content creation engine, further reducing rework.

Organizations that eliminate complexity in their content supply chain by classifying content using standard taxonomy, removing bespoke solutions, and harnessing hosted large language models will move faster and more efficiently. Content and MLR teams that prioritize content quality over content quantity will be early commercial leaders in delivering value from AI.

AI wins in advanced analytics will put people first

As commercial organizations consider AI use cases for advanced analytics, they will also be forced to confront AI’s limits. An AI-generated “next best action,” for example, would need to consider a wide range of variables that are not easily accounted for, from a rep’s call plan to challenging HCP access to incentive compensation plans. If the recommended next best action is one a rep can’t — or won’t — act on, AI is just adding more noise to the system.

As a result, early leaders will focus on investments in people to get the best ROI from carefully selected initiatives. AI use cases in advanced analytics will be most successful when companies spend time upfront defining problems, structuring data, and training users to act on insights. Early leaders will build a change management culture that addresses knowledge and skills gaps.

But AI tools that are bolted onto disconnected systems or that need complex integrations with business intelligence tools will also slow down insights and discourage user adoption. AI that augments decision-making and is embedded in users’ workflows — similar to how navigation apps guide drivers through traffic in real time — will see wider use and set the stage for long-term ROI.

New leaders will push medical from activity-led to outcome-led

Innovative and complex medicines are driving increased demand for scientific exchange. And data shows that when medical affairs effectively engages HCPs and KOLs pre- and post-launch, treatment adoption improves. To deliver on this growing customer need, biopharmas will shift investments and increase resources for scientific engagement.

But, without clear, quantitative, and compliant measures of success, medical affairs will limit its effectiveness and struggle to demonstrate medical impact. Medical leaders who take ownership of data-driven approaches and challenge traditional models will be the first to evolve from activity-led to outcome-led. Early focus areas will be identifying and quantifying unmet patient needs, clinical practice change, and improving patient outcomes.

To attract leaders with the right mindset, companies will prioritize investment in medical affairs, including the necessary resources to pursue more non-traditional measurement applications. And outcome-driven leaders must grab the reins, own their narrative, and take action.