Clarkston’s team of pharmaceutical and biotech consultants have highlighted the top 2025 pharma and biotech commercial operations tech trends that businesses should consider. See an excerpt of the trends report below, and read all four trends for 2025 by downloading the full report here.

2025 Pharma and Biotech Commercial Operations Tech Trends

The past year has transformed the technology landscape, enabling Commercial functions within pharma and biotech organizations. Economic pressure has also created an environment where many pharma/biotech organizations are measured in their tech spending, prioritizing a right-sized approach to new launches and an ROI-driven approach to the adoption of new technologies like Generative Artificial Intelligence (Gen AI).  

In this report, we are breaking down the 2025 pharma and biotech commercial operations tech trends to explore how AI and the Salesforce/Veeva split is shaping the technology landscape that supports commercial teams as well as the strategies and processes these teams are adopting to drive future implementations.  

Trend #1: Realizing Generative AI ROI 

With AI taking all industries by storm, we’d be remiss not to start there. This year has seen similar experiences for many commercial teams, as everyone has gotten smarter about AI. AI excitement, evaluation of new use cases, pilot initiatives, and even integration into business processes have begun. Firms are navigating an onslaught of net-new AI vendors with wholly different technical paradigms, and familiar vendors have new AI capabilities, with unclear levels of maturity or proven success.   

Amidst these challenges are countless additional nuances, adding roadblocks from realizing ultimate AI value. With all these challenges circulating, it can quickly feel difficult (if not overwhelming) to determine where, who, and with whom life sciences organizations should take their next steps with AI. Fortunately, experience – both successes and failures – have started to distill understanding of the best opportunities for real impact on the business.  

AI for Internal Process Optimization and Cost Savings  

Cost-saving AI use decreases expenses by improving organizational efficiency, particularly internal processes. AI enhances internal processes by either strengthening employee performance to complete tasks more effectively or by fully automating tasks, enabling sales and marketing teams to focus on higher-value activities. Teaching how to use AI tools effectively can greatly reduce productivity inequality among employees. MIT researchers found that, with proper training, AI offers modest efficiency gains and quality gains for experts while significantly enhancing performance of those novice in a field.  

In some cases, AI can replace an employee’s need to complete a task – or the majority of the effort. A most common use case is related to tasks requiring summarization of large amounts of information. There is a massive amount of research that commercial teams can benefit from absorbing, including competitive intelligence, research, and treatment advancements. AI is particularly good at organizing this into outputs that are easily absorbed and formatted to exact requirements. For example, at oncology conferences, teams now use AI tools to summarize presentation abstracts and recommend sessions based on specific criteria. These applications save employees time by handling manually intensive, time-consuming tasks, opening room to concentrate on strategic priorities.   

AI LLM Use Cases  

Life sciences marketing teams use AI to automate digital messaging and personalize marketing based on known attributes or behaviors of healthcare professionals (HCPs). Since marketing communications and HCP data are largely text-based, marketing messaging is an excellent candidate for Large Language Model (LLM) application. Text data, even when incomplete or lacking extensive metadata, often performs well with LLMs due to their ability to extract meaningful context from messy text. For this reason, LLMs are proving themselves to be an excellent tool to supplement modular content initiatives and other tailored HCP communications.  

The AI Scaffolding – Data and People  

As commercial teams rapidly progress in their adoption of AI, organizations must continue to place emphasis on data. Clean, strong, and maintained data is required to build and train internal AI models, and the maturity of existing data governance will reflect an organization’s ability to power AI. Moreover, data ownership is another critical component to consider when advancing AI use cases.  

Mid-sized life sciences firms are shifting ownership of their commercial data from third parties to in-house, and this trend has significant implications on the viability of many AI use cases. There will already be inherent opacity with LLMs and generative AI – in a landscape where the data used to feed the model is also owned externally, outputs would have posed more questions than answers. By bringing data ownership in-house, commercial organizations remove hurdles between the outputs, the original data, and business understanding.  

Additionally, commercial teams must consider the people – their own organizational culture, goals, and personnel – as they expand AI use cases. Developers must quickly learn an entire new skillset, and business partners must learn a dictionary of new technical jargon to interpret. The experimentation at hand must work for the business; in other words, the business must understand its own limits, requirements for success, and technical team’s capabilities. Strong, successful AI use requires significant time investment and realistic expectations – otherwise, organizations risk sinking too many resources into AI initiatives that fail to meet their initial promise.    Continue reading trends #2-4 by downloading the full report below.

Download the Full 2025 Pharma and Biotech Commercial Operations Tech Trends Report Here

Contributions from Lilly Saiontz