Pharmaceutical companies in the United States are looking to artificial intelligence (AI) to help them transform their commercial operations, sales and marketing divisions, according to a Verix survey of 100 sales, marketing and brand employees at pharma firms.

The results of the study, “AI and Commercial Pharma: A State of the Industry Report,” highlight a pivotal challenge faced by nearly three quarters (74%) of commercial pharma executives – the pressing need for automation to facilitate timely insights extraction.

More than three quarters (76%) of respondents said they currently must employ consulting agencies to achieve business insights, which often have limited abilities to perform advanced data analytics.

The study also underscores the profound impact of AI adoption on revenue optimization and operational efficiency.

About seven in 10 (69%) of executives surveyed expressed confidence that fortified insights could significantly bolster their organization’s revenue. Moreover, marketing teams emerge as particularly concerned about revenue loss, highlighting the urgency for enhanced analytics capabilities.

The vast majority (81%) of respondents advocate for the dismantling of silos through the implementation of holistic platforms. This sentiment suggests the industry’s recognition of the need for integrated solutions to navigate the complex landscape of disparate technologies and tools.

The report noted that by centralizing operations, such platforms promise to streamline processes, fostering cross-functional collaboration and goal attainment.

Furthermore, 82% of executives advocate for scalable AI deployment across their brand portfolios, citing the inefficiencies associated with creating bespoke models for individual products and treatments.

Streamlining AI deployment holds the promise of expediting insights generation and enhancing overall operational efficiency. In line with evolving industry dynamics, 88% of sales executives emphasized the importance of solutions that seamlessly integrate with existing environments.

This finding underscores the need for agility and flexibility within pharmaceutical teams, as fragmented tech stacks risk impeding productivity. The results indicated platforms boasting robust API integrations offer a pathway towards enhanced data interoperability and streamlined workflows.

Doron Aspitz, founder and CEO of Verix, explains the problem these pharma executives are facing is not being able to digest the vast amounts of available data.

“They realize that using disparate technologies prevents them from seeing the big picture – how a marketing campaign influences sales or how the choice of channels improves HCP,” he says. “A lack of automation hinders continuous refresh of data, which results in misleading insights.”

He explains quality AI solutions are comprehensive and automated, analyzing a broad set of the latest data. These AI-powered platforms are able to deliver precision insights at a fine grain level, something that can’t be achieved with manual or even semi-automated solutions.

“One good example is the generation of dynamic target lists – using fine grain micro segmentation a quality AI platform can recommend a set of small lists with the right message to the right HCP via the right channels,” Aspitz says.

As the survey results indicated, a centralized platform could help in breaking down silos and optimizing processes across different functions within commercial teams in pharmaceutical companies.

“Replacing disparate technologies with a centralized platform, connects all commercial operations teams over a single data foundation, with a single version of the truth,” Aspitz says.

The data foundation holds factual, measured and predicted information for the entire therapeutic landscape of the teams’ brands, and all teams collaborate over this data foundation that is continuously refreshed with the latest and most relevant data.

He cautions trying to reinvent the wheel and developing in-house solutions from scratch more often than not leads to unnecessarily complicated projects with disappointing results.

“It’s better to start off with a tried and true platform and deploy your solution on top of that,” he says. “Choose a platform that can integrate into your workflow. A pharma specific platform is always preferred.”