AI for Smarter Solutions: Inside AstraZeneca’s AI Strategy

As AI transforms how organizations operate, AstraZeneca’s AI strategy stands out for its deliberate and disciplined approach. In a sector where errors can cost lives and compliance is non-negotiable, the company treats AI not as a tactical tool, but as core infrastructure – designed for scale, reliability, and compliance.

This case study examines how AstraZeneca implemented AI across the enterprise – aligning technology, people, and governance to accelerate science while maintaining trust, accountability, and human oversight.

The article bridges technology strategy, operational execution, and ethical practices, offering a blueprint for tech leaders who want to harness AI safely, effectively, and at scale.

AstraZeneca AI strategy and background

AstraZeneca’s business spans oncology, cardiovascular/renal/metabolism, as well as respiratory and immunology. Its scale and R&D intensity is vast.

To meet its “Ambition 2030” target, delivering 20 new medicines and achieving $80 billion in revenue by 2030, AstraZeneca has explicitly made AI a strategic priority.

AstraZeneca has publicly stated that AI and data science form a ‘foundational pillar’ of its corporate strategy. Hence, they must be woven into all processes from R&D to manufacturing.

To support this vision, AstraZeneca has invested heavily in AI infrastructure and partnerships. The company has developed proprietary data assets (e.g., the Biological Insights Knowledge Graph) to fuel AI workflows. It also collaborates with various academia and AI startups (e.g., licensing machine learning platforms and partnering on immunology AI models), to accelerate knowledge transfer and remain at the forefront of technological innovation.

Upskilling AstraZeneca employees through AI accreditation

Recognizing that technology alone is insufficient, AstraZeneca has pursued a company-wide culture shift.

To support their employees in the best possible way, the company has partnered closely with Global IT, HR, Compliance, and Business Functions. Together, they have structured an enterprise-wide AI upskilling and literacy program.

Launched in 2024, this program gives all employees the chance to learn about AI, regardless of their experience or comfort with technology. It helps them understand how AI can be applied in their everyday work.

The training includes keynote lectures, lab sessions, reflection activities, and strategic thinking workshops.
It encourages participants to be curious, think critically, ask questions, speak up, and use AI ethically and responsibly.

Likewise, learners can progress through a tiered competency scale – Bronze, Silver, Gold, Platinum, and Diamond. This allows them to develop the AI skills and literacy that are most relevant to their role.

To date, 12,000+ employees have participated in the program. This company aims to enhance the program further by providing upskilling opportunities in local languages to empower AstraZeneca employees globally.

AstraZeneca’s strategic AI implementation

AstraZeneca’s approach to implementing AI unfolded in stages, integrating technology pilots with people and process changes.

Establishing leadership alignment required candid discussions about where AI could realistically drive value beyond industry hype. According to Shubh Goel (Vice President and Head of the Immuno-Oncology and Gastrointestinal Tumors Franchise within the US Oncology Business Unit), her team spent nearly a year conducting proofs of concept before formalizing the AstraZeneca AI strategy.

This period involved retrospectively assessing existing platforms, capabilities, and data assets, and then aligning leadership around specific use cases. Prioritization focused not just on what AI could do, but on what was most likely to succeed.

That ‘year of innovation’ ultimately led AstraZeneca to adopt a highly targeted, pragmatic approach to AI deployment.

Different use cases of AI at AstraZeneca

Here are a few ways AI is used at the pharma company:

Using AI to better understand disease

At AstraZeneca, data science and AI are used across research and development to gather, connect, and analyze large amounts of information.

This helps experts better understand different types of diseases and identify more promising drug targets, ultimately accelerating the development of new medicines.

Advanced molecular imaging

By combining mass spectrometry imaging with AI analysis, scientists can decode the molecular composition and spatial distribution of proteins, metabolites, drug molecules, and other biomolecules within human tissues at an unprecedented level of detail.

This use of AI helps experts spot patterns and relationships in large imaging datasets that would otherwise be difficult to detect. It deepens insights into disease mechanisms and guides in making more informed drug discovery and development.

These AI-enhanced imaging insights contribute to more precise assessments of how potential medicines interact with biological systems. They ultimately support the design of more effective treatments.

Patient recruitment for a clinical trial

Finding the right patients for a trial can take months or even years. AstraZeneca uses AI to analyze large sets of health data, such as electronic health records, to identify patients who match the trial criteria. This reduces the time needed to recruit participants and helps trials start sooner.

AI-powered development assistant

An AI-powered ‘development assistant’ seeks to address common interdepartmental needs such as data extraction, search functions, and speedier analytics.  

By layering a large language model (LLM) on top of in-house data products, the assistant enables teams to generate charts, support clinical analyses, and compare quality and clinical data across the portfolio with ease.

This capability has also helped break down organizational silos, enabling stronger collaboration across departments that previously worked in isolation.

Measuring impact and feedback

As part of the AstraZeneca AI strategy, the company measures the success of its AI initiatives through internal surveys and regular employee feedback. These assessments help the company understand how widely the tools are used and the value they are creating.

While specific financial impacts have not been publicly disclosed, the company reports clear gains in operational efficiency and productivity, particularly within data science and research teams. These positive outcomes have strengthened their leadership’s confidence in expanding and continuing investment in AI across the organization.

AI governance and best practices

AstraZeneca places strong emphasis on using data and artificial intelligence responsibly, especially given the sensitive nature of healthcare and patient information.

The company has engaged a diverse range of experts, both within and outside AstraZeneca, to develop principles for ethical data and AI that align with its Code of Ethics and values. These practices ensure ethical and responsible use of AI and data, enabling AstraZeneca to make a positive contribution towards society.

“An ethical approach is vital to ensuring the benefits of AI are shared by all. We welcome the guidelines which have already emerged from regulators around the world – but there is more to be done. We want to work with regulators and partners to help shape a sustainable environment for innovative and responsible AI to thrive.”

– says Pam ChengExecutive Vice President, Global Operations, IT & Chief Sustainability Officer, AstraZeneca

Likewise, human oversight is built into every stage of AI usage. Outputs from AI systems are never used directly in decision-making and are diligently reviewed and validated by qualified experts.

The team also actively monitors for hallucinations or inaccuracies. When an issue is identified, it triggers a structured review process that includes examining data inputs and model configurations to prevent recurrence and continuously improve system reliability.

Together, these practices reinforce AstraZeneca’s position with AI.

Challenges on the path to enterprise AI at AstraZeneca

Despite the significant benefits, AstraZeneca encountered several challenges during the AI integration process. These included ensuring seamless integration of diverse data sources, managing a complex infrastructure that required constant upkeep, and creating a cohesive environment for various ML tools. Additionally, the company had to address ethical considerations related to data privacy and the accuracy of AI-generated outputs.

Future outlook

AstraZeneca remains committed to advancing science and innovation through the strategic use of AI. The company plans to further leverage generative AI to enhance predictive modeling, clinical trial design, and drug development.

By integrating advanced data science tools, AstraZeneca is improving data-driven analysis and decision-making. While maintaining a strong focus on sustainability and ethical considerations in AI deployment.

Lessons to learn for tech leaders

AstraZeneca’s AI journey depicts that real enterprise value emerges from treating AI as core infrastructure rather than a quick productivity tool. By commencing with clearly scoped, high-impact use cases, building strong data foundations, implementing AI governance and human oversight from day one, and capitalizing on AI workforce literacy, the organization has been able to scale innovation without compromising safety, trust, or compliance.

Key takeaway for tech leaders:

AI works best when technology, culture, and accountability grow together. Responsible AI then becomes a long-term competitive advantage, not just a compliance requirement.

In brief

AstraZeneca’s AI strategy exemplifies how a global pharma leader integrates AI across R&D, clinical trials, and operations while maintaining governance and ethical standards. It demonstrates how leaders can unlock the benefits of technology without compromising trust or accountability.