Each year we survey colleagues and customers from the life science industry to collate their perspectives on the state of digital transformation in their own organization. From industry satisfaction to digital marketing budgets, Cx, AI, …the resulting Maturometer report aims to summarize pharma’s progress towards omnichannel maturity – as viewed by the people who work in it. Simply tap the button below and complete a short form in order to access this unique insight into the state of digital maturity in life sciences in 2024!
Please note: The Maturometer survey was issued by Across Health earlier this year; we have since rebranded to Precision AQ.
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To give you an idea of what this year’s Maturometer offers, we are pleased to provide you with two highlights below.
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MLR ISSUES AND ROI CONCERNS TOP THE LIST OF COMMON BOTTLENECKS FOR DIGITAL
Looking at those factors thought to be getting in the way of digital progress among EU biopharma organizations, MLR (Medical, Legal & Regulatory) issues and uncertainties around ROI continue to be an important obstruction for digital success - and the gap between these has closed significantly since last year’s survey. Meanwhile, lack of internal knowledge, technologies and strategy consistently fill out the top 5 bottlenecks, presenting opportunities for pharma to make use of better strategic planning, tracking and upskilling.
Are current investments failing to show results…or is there rather a lack of confidence/knowledge regarding the appropriate metrics for digital initiatives?
THERE IS SIGNIFICANT INTEREST IN AI, BUT ITS EFFECTIVE IMPLEMENTATION REMAINS CHALLENGING
When asked "What's the current level of adoption of Al in your company's customer engagement efforts?" only 11% of European biopharma respondents reported no interest at all in AI in their organization. In fact, the data indicate a strong inclination to incorporate AI in customer engagement efforts, with over a third having progressed beyond the “interested” stage to pilots and beyond.
Are they finding it challenging to turn their AI ambitions into reality because the basics - particularly data quality and organization - are not yet in place?