Life Sciences
The era of the AI Co-Scientist is here. How is your organization preparing?
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Surrogate models are reshaping R&D by making research faster, more cost-effective, and more sustainable.
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As we step into 2025, R&D organizations are bracing for another year of rapid-pace, transformative shifts.
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In today’s competitive R&D landscape, selecting the right technology partner is one of the most critical decisions your organization can make.
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Leveraging advanced technology like generative AI through digital transformation (not digital enhancement) is how to get the biggest returns in scientific R&D.
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There is much more to digital transformation than technology, and a holistic strategy is crucial for the journey.
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In the rapidly-evolving landscape of drug discovery and development, traditional approaches to R&D in biopharma are no longer sufficient. Artificial intelligence (AI) continues to be a game-changer.
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R&D leaders and scientists need a working understanding of key AI concepts so they can more effectively develop future-forward data strategies and lead the charge towards groundbreaking discoveries.
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Scattered and siloed data is one of the top challenges slowing down scientific discovery and innovation today. What every R&D organization needs is a data fabric as part of their technology solution set.
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With the increasing importance of AI and machine learning in science and engineering, it is critical that the leadership of R&D and IT groups at innovative companies are aligned. Inappropriate budgeting, policies, or vendor choices can unnecessarily block critical research programs; conversely an “anything goes” approach can squander valuable resources or leave an organization open to novel security threats.
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