Analytics has been at the heart of sales force strategy for a long time. Most large pharmaceutical companies mine data to guide sales goals, sales force size, business structure and planning.

However, the focus of all Pharma Commercial Insights is to identify the source of activity. 25 years ago, pharmaceutical companies focused their analytics on identifying the right doctor to work with, based on sales or prescriptions generated by doctors. Things are no longer simple for them. Regulations have led to structural changes in the healthcare market since the turn of the decade, making it much more difficult to sell and market pharmaceuticals, even if operating margins are subject to pressure.

As pharmaceutical industry executives grapple with an ever-increasing array of complexities, costs, and regulations, many are turning to artificial intelligence (AI) as a possible solution. In fact, a recent global pharmaceutical industry market report shows that spending on AI will exceed $3.6 billion by 2026. Use cases for this technology include applications for discovery, manufacturing, diagnostic support, drug marketing and business operations. However, despite the industry's commitment of capital and resources to this promising technology, executives are still confused about how to best use AI.

Especially in the pharmaceutical industry, all of the requirements for data integrity, compliance and government oversight create an environment where risk reduction often trumps continuous innovation. However, pharma companies can use best practices to ease the transition to AI tools that can provide insights and shortcuts.  

It's important to note that AI algorithms don't just invent information. They need to derive actionable insights from the data you already have. An important first step for any organisation looking to use Artificial Intelligence is to put their data in order. The benefits of AI-driven insights are clear, but these capabilities will not be achieved if an organisation's data management practices remain at a fundamental level. And no matter how innovative AI's algorithms are, the results will be disappointing if they run on distributed, inconsistent, and outdated data.  

While it can take years for AI to collect sales data and make scientific discoveries and organise that data in minutes to uncover valuable insights, for companies that have never worked with AI in the past, that could mean completely reorganising their databases and digitising offline records.