Big Pharma Is Investing Billions In AI— And The Value Is Finally Starting To Show

Forbes - Apr 25th, 2025

Artificial intelligence is transforming the pharmaceutical industry by significantly enhancing drug discovery and development processes. Over the past two years, major healthcare and pharmaceutical companies have increasingly adopted AI technologies, which initially were met with skepticism due to unclear value propositions. Key players like DeepMind have paved the way with innovations such as the AlphaFold ecosystem, revolutionizing computational biology and chemistry. Partnerships, like Eli Lilly's with BigHat Biosciences, are leveraging AI to expedite biologics development with improved therapeutic profiles, while companies like SkyCell use AI for advanced supply chain management.

The integration of AI into the pharmaceutical sector signifies a paradigm shift, offering substantial advantages in efficiency and innovation. By moving into the cloud, pharmaceutical companies can manage vast amounts of data more effectively, enhancing their capabilities in drug development life-cycles. Although the fusion of AI and pharma is in its early stages, the emerging benefits suggest that ongoing investments will lead to better consumer outcomes. Companies embracing AI face initial challenges but stand to gain a competitive edge as they refine their use of this transformative technology.

Alpha Fold Eli Lilly Supply Chain Optimization Cloud Computing Deep Mind Drug Discovery Ai In Pharma Big Hat Biosciences Sky Cell

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RATING

6.0

Moderately Fair

Read with skepticism

The article provides an insightful overview of AI's impact on the pharmaceutical industry, highlighting its potential to revolutionize drug discovery and supply chain management. It effectively uses specific examples to illustrate these points, making the content engaging and relevant. However, the article could benefit from more balanced reporting by including potential challenges and ethical concerns associated with AI. The lack of direct citations or expert opinions affects the credibility and authority of the information presented. Overall, the story is timely and of significant public interest, but it would be strengthened by greater transparency and a more comprehensive exploration of the topic.

Accuracy

The article presents a largely accurate portrayal of AI's impact on the pharmaceutical industry, particularly in drug discovery and development. It accurately describes the role of AI in improving processes and mentions specific examples like DeepMind's AlphaFold and Eli Lilly's partnership with BigHat Biosciences. However, while the claims about AI's transformative potential are generally true, the article lacks specific data or studies to substantiate the extent of these impacts. For instance, while the mention of AlphaFold's impact on computational biology is accurate, the article could benefit from citing specific studies or expert opinions to reinforce this claim. Similarly, the story about AI's role in supply chain optimization through SkyCell's partnership with Microsoft is plausible but would be strengthened by more detailed evidence or case studies.

Balance

The article predominantly highlights the positive impacts of AI in the pharmaceutical industry, which may suggest a bias towards the benefits of AI without equally addressing potential drawbacks or challenges. While it acknowledges 'growing pains' that companies might face, it does not delve into the potential risks or ethical concerns associated with AI in healthcare. The article could be more balanced by including perspectives on the limitations of AI, such as data privacy issues, the risk of over-reliance on technology, or the challenges in integrating AI into existing systems.

Clarity

The article is generally clear and well-structured, making it easy for readers to follow the narrative. It uses straightforward language and provides specific examples to illustrate the points being made, such as the use of AI in drug discovery and supply chain management. The logical flow of the article helps in understanding the sequence of developments in AI's integration into the pharmaceutical industry. However, the article could benefit from more detailed explanations of technical terms like 'AlphaFold' and 'Milliner platform' to aid readers unfamiliar with these concepts.

Source quality

The article lacks direct citations or references to authoritative sources, which affects the credibility of the claims made. While it mentions companies like DeepMind and Eli Lilly, it does not provide links to press releases, studies, or expert commentary that could support these claims. The absence of attributed sources or expert opinions diminishes the reliability of the information presented. Including quotes from industry experts or references to scientific studies would enhance the article's authority and trustworthiness.

Transparency

The article does not clearly disclose the basis for its claims or the methodology used to gather information. It lacks transparency in terms of how the examples were selected or the criteria used to evaluate AI's impact. Additionally, potential conflicts of interest, such as affiliations with the companies mentioned, are not addressed. Greater transparency could be achieved by providing background information on the sources of information and any potential biases in the reporting.