Pharmaceutical R&D is undergoing significant changes. The need to accelerate drug development and enhance decision-making has placed artificial intelligence (AI) at the core of industry transformation.
According to Norstella’s State of the Industry AI survey, 2024, 81% of organizations already use AI in at least one development program. This reveals that AI is rapidly becoming a critical driver of efficiency and innovation across the sector.
But this shift isn’t just about technology adoption; it’s about rethinking entire R&D models. While large pharmaceutical companies are building robust, in-house AI capabilities to fuel proprietary data strategies, smaller biotech firms are capitalizing on partnerships to bridge expertise gaps and leverage AI’s potential without needing heavy infrastructure.
With 71% of organizations citing a lack of AI expertise as a barrier, these partnerships are proving invaluable to their overall R&D success.
In fact, 79% of companies report that AI partnerships are moderately to extremely valuable, underscoring the importance of collaboration in leveraging AI to its fullest potential.
In this blog series, we’ll begin by exploring companies’ varied strategies used to integrate AI, from scalable partnerships to in-house innovation hubs. Norstella’s insights reveal that these adoption trends aren’t just shaping R&D — they’re fundamentally redefining the competitive landscape across the pharmaceutical industry, driving efficiency, and helping companies get innovative therapies to patients faster.
AI adoption in drug development takes many forms, shaped by an organization’s resources and strategic goals. While some companies with significant resources are building internal AI capabilities to drive innovation, others are focusing on partnerships with AI providers to unlock the technology’s potential without needing heavy investment.
Pharma: Leading the AI integration charge
Large pharmaceutical companies, with their substantial financial resources and R&D infrastructures, are making significant strides in AI adoption. For a few, AI has already become a critical asset, as well as a growth and efficiency engine, integrated across multiple development programs.
Approximately half of all pharmaceutical respondents reported using AI across their development pipeline, with almost a quarter stating they are currently piloting AI in one or more development programs. This demonstrates the widespread commitment to using AI to enhance decision-making and accelerate timelines in an increasingly competitive landscape.
Biotech: Powering innovation through partnerships
For smaller companies, especially biotechs, despite AI adoption in R&D workflows being actively encouraged by leadership, only approximately 50% of biotechs employees surveyed believe that leadership can action these with the current internal resources alone.
Therefore, it is no surprise that 47% of biotechs are instead partnering with one or more organizations that have AI powered technologies, rather than concentrating on building in-house capabilities.
These collaborations enable biotech firms to focus on specific applications, such as improving site selection for clinical trials or identifying promising drug candidates without the heavy costs associated with developing entire AI departments internally.
By working with AI partners, biotechs can leverage AI-driven insights, subject matter expertise and data science resources, without overextending their resources. Partnerships provide the flexibility to innovate and compete effectively while focusing their efforts where they’ll see the greatest return.
AI is tackling some of the industry’s most stubborn bottlenecks and redefining drug development. By turning overwhelming amounts of data into actionable insights, AI allows companies to cut through inefficiencies and make more strategic, data-driven decisions.
With 30% of surveyed companies already incorporating AI for predictive modeling in drug development, the technology is helping to reduce the risk of costly late-stage failures and empowering teams to make faster, more confident choices. The ripple effect is profound: fewer delays, smarter investments, and more streamlined paths to market.
But AI’s influence doesn’t stop there. By automating complex data analysis and refining operational processes, it helps companies cut costs and optimize clinical trials.
Source: State of the industry: AI advances in pharmaceutical R&D survey, Norstella, 2024
Yet, like any transformative technology, AI presents its own set of challenges. Companies face hurdles such as integrating diverse data sources, managing potential bias in algorithms, and dealing with the complexity of large-scale data processing.
As companies look to overcome these obstacles, it’s clear that success hinges on more than just adopting this technology. It requires the right strategies and tools to truly transform R&D into a more efficient and innovative process — a shift Norstella can help facilitate.
For those seeking deeper insights into AI strategies and partnerships, platforms like NorstellaLinQ offer valuable resources to stay informed on the latest trends and best practices.
However, this transformation is just beginning. In our next post, we will explore how AI is set to further drive operational efficiency and how its future applications will shape the industry. We’ll also dive deeper into the challenges of scaling AI across R&D pipelines and how companies can overcome hurdles like data integration and expertise gaps to fully unlock AI’s potential.
Download Norstella’s State of the Industry AI survey, 2024 infographic to learn more and find key insights.
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