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Learn how to effectively execute your AI journey in the pharmaceutical industry. Discover strategies for building vs. buying AI solutions, managing change, and addressing stakeholder needs to drive successful adoption and maximize value.

Discover how pharmaceutical companies can overcome challenges and drive successful AI implementation. Learn actionable strategies to ensure smooth adoption, maximize ROI, and transform your business with artificial intelligence

Innovation in artificial intelligence (AI) is advancing at a blistering pace, reshaping the biopharma industry. As AI becomes a buzzword across technology vendors, it’s easy to get lost in the noise. This blog series, 'A Guide to AI in Pharma,' aims to clarify AI's role in life sciences.

ODAIA product approach to Customer Science, finding insights (fishing) vs. providing actionable insights (providing the fish), challenge with NBA and last mile consulting projects, the power of “why,” and MAPTUAL as a system to capture change

AI propelling Customer Science, industry dynamics driving the need for change, and lessons from past disruptions

Customer Science is an emerging practice that integrates data science, behavioral science, and AI to gain a deep understanding of the customer experience – or in the case of life sciences, HCPs and their patients – and what motivates their decisions along the customer journey.

In today's competitive pharmaceutical marketplace, connecting patients with the most effective therapeutic options for them is more important than ever. Marketing tools with predictive analytics are making it easier for pharma companies to engage well-matched healthcare professionals (HCPs) and their patients. By understanding the needs and preferences of HCPs, these intelligent tools can help pharma companies create targeted and personalized sales pitches and marketing campaigns that ultimately improve patient outcomes. 

Over the past three years, supply chain issues and drug shortages have become regular headlines and major stressors for pharma companies, HCPs, and patients. While pharma companies want to avoid this kind of press, there is also a great financial risk in overproducing medications that could expire before they are prescribed. Accurate sales forecasts help companies know how much of a drug is likely to be prescribed so they can prepare for what is ahead, as well as ensuring that every patient gets the treatment needed without excessive surplus. Predictive analytics tools can utilize artificial intelligence (AI) to help companies make better forecasts1, among other benefits, such as more optimized marketing.

Sales reps in the pharmaceutical industry face many challenges in their day-to-day. They are constantly on the go, managing large territories and struggling to get face-to-face meetings with busy doctors. In order to get these conversations, reps put in extra preparatory work that can make days even longer and face higher pressure on time-limited calls. It's no wonder that pharma sales reps experience a high level of burnout. New tools that leverage the power of artificial intelligence (AI) show promising results for reducing burnout and improving work-life balance for these hardworking individuals.