and Prepare for the Future in Life Sciences
A Glimpse Into the Future
With a lot of room for improvement as it relates to leveraging data and technology, the future looks bright for life sciences. Even with the bad experience associated with digital initiatives, there are many successful projects that have taken center stage.
In 2015, AstraZeneca launched an adaptive microlearning AI-powered platform to meet the training needs of its frontline salesforce. This algorithm builds a profile for each sales representative incorporating data on their confidence, knowledge, behavior, and business results. The AI-powered platform then ingests these features and delivers personalized learning activities to the reps, eliminating the need for lengthy online or in-person modules.23 This project yielded early results that continue to this day, with 82% of sales teams still engaged in continuous learning and completing 24 learning lessons per month per rep on average.23 Most importantly, the implementation of the AI-powered training platform directly impacted business results, helping the business unit exceed their sales goals in 2018, with 25.7% of the results being attributed to the AI-enabled sales training model.23
Another notable initiative is the collaboration between Microsoft and Walgreens Boots Alliance (WBA) to increase responsiveness to patients’ needs during the COVID-19 pandemic.24 By leveraging Microsoft Azure technology, WBA was able to add a healthcare bot to provide quick answers to customers visiting their website and leverage insights from that bot to offer a higher level of CX to patients when they needed it most.24
These examples offer a small glimpse of what is possible when life sciences organizations embrace digital innovation and harness the potential of advanced capabilities including AI, automation, and omnichannel marketing. In the future, successful organizations will be data-driven, harboring a highly efficient future workforce and offering their HCPs and patients personalized and omnichannel experiences. But in order to get there, we must start building the appropriate infrastructure today by unifying data, aiming for interoperability, and relying on technology that delivers real-time predictions and recommendations powered by robust AI algorithms.
References
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Source: Omnipresence
