AstraZeneca’s Chief Architect on Scaling Enterprise AI and the Rise of Agentic Platforms

At Ai4 2025, AstraZeneca’s Chief Architect Wayne Filin-Matthews — an important AI celebrity in experience and knowledge in the AI business world, important for CIJ EUROPE not to miss — joined Tricia Martínez-Saab, Co-Founder & CEO of AI infrastructure company Dapple, for a wide-ranging fireside chat on the realities of scaling enterprise AI in one of the world’s largest and most regulated industries.

Filin-Matthews, who brings nearly four decades of experience across senior technology roles at Microsoft, Dell, HSBC, and Accenture, leads global strategy and architecture for AstraZeneca’s IT operations spanning 126 markets. The company, the top pharmaceutical player in China, is in the midst of an ambitious expansion — targeting 20 new medicines in five years, doubling annual revenue from $50 billion to $100 billion, and building a $50 billion manufacturing site in Virginia. AI, he stressed, is central to enabling this scale without a proportional increase in AstraZeneca’s 10,000-strong IT workforce.

From Machine Learning to the “Agent Economy”
While AI has been part of AstraZeneca’s workflow for years, Filin-Matthews noted a marked industry shift in early 2024 toward “agentic” AI — platforms that operate autonomously and collaborate like teams. “Agents are the only way we can meet our science, operational, and sustainability goals at this pace,” he said. But implementing them at enterprise scale is not straightforward.

One challenge lies in building a “composability layer” — the infrastructure that allows agents to work across different platforms securely, traceably, and cost-effectively. Without it, enterprises risk budget overruns, compliance breaches, and inefficiencies. AstraZeneca’s approach is to democratise agent-building across multiple ecosystems — Microsoft for productivity, ServiceNow for automated IT processes, financial tools for optimisation — while enforcing strict controls over agent-to-agent communication to maintain compliance, particularly around sensitive cross-border data transfers.

The Overlooked Factor: Cognitive Behaviour of AI Teams
Filin-Matthews warned that too much focus is placed on the technology layer, with insufficient attention to the “cognitive behavioural” dynamics of agent teams. Just as human teams have diverse roles, effective AI teams require a mix of agent “personalities” — for example, even a procrastinator agent can serve a productive function in scientific collaboration. AstraZeneca is working with institutions like Stanford to model these dynamics for R&D, aiming to accelerate molecule prediction and drug discovery without proportionally expanding headcount.

Governance, Sovereignty, and the Cost Challenge
Operating in one of the most regulated industries, AstraZeneca must design AI systems that meet strict governance, sovereignty, and compliance standards. This includes rethinking not just tools but organisational structures — envisioning, for example, agent teams that autonomously manage governance workflows, with humans intervening only at key decision points.

Forecasting costs for complex agent-to-agent communication remains another major hurdle. Agents may shift between multiple models and platforms, incurring token, API, and processing fees that are difficult to predict. Filin-Matthews called for more mature forecasting tools from cloud providers and a sharper industry focus on optimising long-term and short-term agent memory for both compliance and efficiency.

Looking Ahead: Five Years to Maturity
Despite the momentum, Filin-Matthews cautioned that large-scale, fully mature agentic AI is still at least five years away. “In five years, we’ll have learned a lot, failed in some areas, and still not be where we need to be in governance and control,” he said. The most transformative developments will come from more sophisticated agent platforms and from restructuring enterprise operating models to fully leverage them.

Audience Concerns: Accuracy and Model Performance
In response to an audience question about accuracy loss when chaining multiple agents, Filin-Matthews pointed to emerging approaches that embed reasoning directly into the agent layer, slowing computation slightly to improve precision. He also predicted a potential shift away from the “large language model” terminology as models evolve to address performance and accuracy limitations.

While the technical challenges are considerable, Filin-Matthews’ message was clear: the future of enterprise AI will be defined not only by advances in agent platforms but also by how organisations adapt their structures, governance, and thinking to harness them safely and at scale.