AI in pharma commercial strategy: Predicting the market for undiagnosed patients

Artificial intelligence (AI) has become synonymous with drug discovery, allowing pharma companies to identify drug targets, screen compounds, and optimize clinical trial designs. The market is expected to exceed $35 billion in 2030 as pharma works toward improved preclinical and clinical success and shorter development timelines.

Commercial teams have started to recognize that the same analytics can unlock value later in the product life cycle. Applying machine learning to large-scale diagnostic datasets helps pharma move beyond retrospective analysis to predictive targeting.


From discovery to commercialization

Commercial teams have relied on historical data and broad physician targeting to guide their commercial strategies. The approach overlooked large populations of patients who met the clinical criteria for a disease but remained undiagnosed. 

AI models can detect signals within lab data that point to a high likelihood of a disease. By analyzing diagnostic-testing data alongside real-world data sources, AI models can identify patterns that suggest a patient may have (or be at a high risk of developing) a disease before a formal diagnosis is made, revealing patient populations that traditional datasets miss. 

“If you look only at confirmed diagnoses, you may identify a million patients,” says Parag More, executive director of life sciences data and analytics solutions at Quest Diagnostics. “But when you apply predictive analytics to diagnostic datasets, you may find three million people who meet the clinical criteria but haven’t yet been diagnosed.”

This expanded view doesn’t just change how pharma companies estimate the size of the market; it reshapes how they approach commercialization. Besides allowing more precise targeting of physician-education programs and marketing, predictive analytics streamlines traditional sales and marketing strategies, reducing inefficiencies in sales force deployment and marketing spend.

“Machine learning shifts the model from reactive outreach to proactive engagement,” More says. “It helps identify which doctors are most likely to see undiagnosed patients and uses that data to prioritize outreach.” 


Turning insight into action 

Using AI-enabled insights in commercialization pathway planning can inform initiatives designed to close diagnostic gaps. It could help pharma companies deploy sponsored testing programs in healthcare settings where predictive models suggest a high prevalence of undiagnosed patients or launch targeted physician education to improve awareness of symptoms and testing protocols.

AI also provides feedback loops that help refine strategy over time. As diagnostic testing increases and more patients enter the treatment pathway, machine learning models can incorporate new data to improve prediction accuracy and refine targeting to enhance how pharma companies provide support throughout the patient journey.

Delivering these insights at scale requires more than advanced algorithms. As the pharmaceutical industry becomes increasingly data-driven, the role of technology and analytics partners is growing—but applying AI in commercialization is not without its challenges.

Data fragmentation across healthcare systems, evolving privacy considerations, and the need to integrate new insights into existing workflows can all create barriers to adoption. Predictive models should be transparent, validated, and trusted to realize their full value. Moreover, AI and machine learning platforms require access to large, high-quality datasets and specialized modeling expertise that creates a need to partner with data-solutions providers with predictive-analytics tools designed specifically for pharmaceutical commercialization.

These partnerships can support multiple stages of the product life cycle. During early research and development, real-world diagnostic data may help with validating hypotheses and refining patient-population estimates as well as refining market-access strategies, physician targeting, and patient-identification programs.

“Pharma companies are realizing that commercial success increasingly depends on data partnerships,” More says. “The organizations that can integrate diagnostic data, real-world evidence, and predictive analytics will have a significant advantage in identifying patients and accelerating adoption.”

AI and machine learning are no longer experimental, moving from pilot programs to scaled deployment and becoming foundational to commercial success. As AI adoption continues to grow across the pharmaceutical industry, its effect will extend far beyond the lab to reshape how therapies reach the market and the patients they are designed to help.