What Can We Learn from Moderna’s AI Strategy?

What Can We Learn from Moderna’s AI Strategy?

I’ve spent a lot of time looking at how different companies approach AI adoption.

Back in early 2024, OpenAI released a flagship case study about Moderna’s use of ChatGPT. It was impressive, showing fast adoption and the creation of hundreds of internal AI tools.

At the time, I found it interesting, but I’m always a little cautious about initial case studies. The real test is what happens later. Is it just a short-term success, or are they building something that will last?

So I’ve kept an eye on Moderna’s progress – I wanted to see if that early momentum would lead to deeper, more structural changes. Turns out, it did.

This is a longer form blog post than I usually write, so I’m including a quick executive summary. The full article will take ~11 mins to read.

Recently – in mid-2025 – Moderna announced they are significantly expanding their collaboration with an R&D platform (Benchling) to create a single “AI-ready” digital environment for hundreds of their scientists.

This is the important part. It shows their strategy is evolving beyond the initial, impressive wins with chatbots. They are now re-architecting the foundation of their research process to fully support AI.

As Wade Davis, a Senior Vice President at Moderna, put it, “AI is creating extraordinary opportunities in science, but realising its full potential requires entirely new ways of working.”

This latest move shows that Moderna’s AI journey is deepening. It makes this the perfect time to re-examine their entire strategy – from their cultural initiatives to their technology choices – to understand how they are building such a durable advantage. According to their leadership, their team of around 6,000 people is now working towards a goal that might typically need a workforce of 100,000.

So, how exactly are they doing this?

Their success comes from a clear strategy built on four key ideas. If you’re a business leader looking for a real-world model for AI, I believe Moderna’s journey offers a clear and practical example. Let’s walk through it.

1. Vision from Leadership, Not IT. At Moderna, AI is a core business strategy. Their CEO, Stéphane Bancel, consistently talks about AI as the main way to scale the company’s mission. This is important because it aligns everyone, secures investment, and gives the work a shared purpose.
2. Culture Before Code. Their success with AI tools began with a focus on people. They made a deliberate effort to educate and empower their entire workforce before rolling out advanced platforms. Through their AI Academy, they built AI literacy across the company. This created an organic, employee-driven demand for AI solutions, which is far more effective than a top-down mandate.
3. Build Integrated Systems. Moderna uses a mix of the best tools available, combining partnerships with OpenAI and Amazon Web Services (AWS) with their own custom platforms. This system is built on a cloud-native foundation they established a decade ago. This early decision prevented the data silos that stop many AI projects at other companies. It’s why they can now deploy thousands of custom AI assistants across every department.
4. Redesign Your Organisation for the Future. Moderna understands that new technology requires a new way of working. They made a radical move by merging their HR and IT departments into a single “People and Digital Technology” function. This structural change shows they are proactively building a company ready for human-AI collaboration.

The Digital Foundation – Preparing Your Organisation for AI

Moderna’s recent success with generative AI wasn’t an “overnight success.” Rather, it was the result of a deliberate, decade-long strategy to be a “digital-first” company. Their work started long before the current AI trend and created an organisation that could adopt new technology with incredible speed.

If we want to replicate their success, understanding this foundation is the first step.

For leaders today, this means recognising that the foundational decisions for AI success are made now, by actively building the digital and cultural infrastructure that allows for rapid AI deployment and impact.

A Core Philosophy: Treating Biology as Software

Moderna’s core philosophy is that messenger RNA (mRNA) is information – it’s the “software of life.” This idea has guided their structure and investments since 2010.

Because mRNA is a literally set of instructions, they realised they could create a repeatable platform for developing new medicines. This is very different from traditional drug discovery, where each new molecule often requires a whole new process.

Their platform approach allows for many R&D programmes to run at the same time, with teams sharing learnings in real-time. This created a natural need for a fully digital infrastructure to support it.

A Cloud-Native Company from Day One

To make this vision a reality, Moderna decided to be entirely cloud-native from its founding. They partnered with Amazon Web Services (AWS) over a decade ago.

This commitment to the cloud provides the essential ingredients for a successful AI programme. AWS gives them the computing power for complex research, the machine learning tools to build models, and the analytics to monitor the business.

For example, by standardising their data strategy on AWS, they streamlined the process of managing massive scientific datasets by an estimated 70%. This digital infrastructure has become the company’s “central nervous system”.

Connecting Data with a “Best-of-Breed” Strategy

Moderna’s approach to software is to find the best tool for each specific job and then integrate it smoothly into their system. This keeps them agile and prevents them from being locked into one vendor.

A key part of this is their data mesh architecture. As the company grew, they needed to connect data from different business areas like supply chain and manufacturing.

Instead of creating complex and redundant data pipelines, they created a unified layer that treats data as a “product” with clear ownership. It allows teams to get the accurate, real-time data they need, while centralised governance keeps it secure.

The value of these early decisions is immense. Many companies today start AI projects only to get stuck for years cleaning up and moving data from old, siloed systems. Moderna’s ability to build its first internal AI tool, mChat, in just two weeks is a direct result of the digital maturity it built over the last decade. The lesson here is clear: your return on investment from AI is directly linked to the quality of your underlying digital and data infrastructure.

AI Mandate – Leadership, Culture, and People

A modern technology stack is necessary for AI success, but it’s not enough on its own. Technology is just a tool. Its value is only realised by the people who use it. Moderna’s strategy shows a deep understanding of this, focusing as much on the human side of the change as the technical side.

A Clear Vision from the CEO

The AI initiative at Moderna is a business strategy, not an IT project. CEO Stéphane Bancel has consistently positioned AI as a force that will redesign every process in the company.

He frames the purpose of AI in terms of scale and mission. The goal isn’t to cut costs. They want to enable the company to launch up to 15 new products in the next five years with a lean team.

The vision is for a few thousand people to have the impact of 100,000.

This framing connects AI directly to the company’s mission of delivering medicines, and it presents AI as a tool that helps people be more productive, reducing fears about job replacement.

The AI Academy: A Commitment to Upskilling

Moderna knew its vision required a capable workforce. In late 2021, the company launched the Moderna AI Academy in partnership with Carnegie Mellon University (CMU).

The academy’s goal was ambitious: to educate and empower all employees to use AI in their daily work. This wasn’t a programme for a select few. The curriculum covered a range of topics from data visualisation to AI ethics. In 2023, they expanded this by partnering with Coursera. The results have been impressive. In the first 20 months, over 2,000 learners logged more than 14,700 learning hours, with post-training assessments showing a 30% average increase in knowledge.

Building a Culture of Experimentation

With a foundation of AI literacy in place, Moderna focused on creating a culture where people felt empowered to use their new skills. They did this by putting powerful, easy-to-use AI tools directly into the hands of their employees.

The first major test was mChat, an internal AI chatbot built on OpenAI’s API. Developed in just two weeks, it was an immediate hit. Over 80% of employees adopted it within months. This early success built momentum for the later rollout of ChatGPT Enterprise.

To speed up this cultural shift, Moderna introduced several initiatives:

• Generative AI Champions: They ran an “AI prompt contest” to find their top 100 power users. This group became a network of internal champions who shared best practices and drove adoption in their teams.
• Community and Support: They set up local office hours and a busy internal AI forum on Slack, creating a self-sustaining community for knowledge sharing.

This combination of education and empowerment created a powerful dynamic. Employees started asking for AI solutions themselves. This organic, employee-driven demand is the main reason for Moderna’s incredible AI adoption rates.

The Implementation Playbook – AI in Action

Moderna’s AI strategy is not just theory; it’s embedded in the daily work of every department. The company has moved from small experiments to a systematic deployment of AI tools that are creating real value across the business.

Research & Development: Speeding Up the Core Mission

In biopharma, R&D is the engine. Moderna is using AI to make this engine faster and more efficient. AI is central to its individualised cancer therapy programme, where algorithms analyse a patient’s tumour to design a unique vaccine. This process is so core to the therapy that the FDA required the algorithm to be “locked” before the main study began. The company is also using AI to unify lab data through its partnership with Benchling, creating a consolidated, AI-ready hub for its scientists.

Manufacturing & Operations: The Digital Factory

Moderna’s digital approach extends deep into its manufacturing operations. The company’s facility in Norwood, Massachusetts, was designed to be digital from the start, integrating advanced platforms that create a smooth flow of data from the factory floor to the cloud. AI algorithms use this data to optimise the manufacturing schedule, ensuring the timely delivery of complex therapies. The impact is clear: these digital systems led to a reduction in production cycle times from over ten days to just five or six, and an 80% reduction in manual error rates.

Legal, Regulatory & Compliance: The Unlikely Power Users

One of the best signs of Moderna’s success is the enthusiastic adoption of AI by its non-technical departments. The legal team was the first department to reach 100% adoption of ChatGPT Enterprise. They have become power users, developing custom tools to automate tedious document review and provide instant answers to policy questions, freeing up lawyers to focus on high-value strategic work.

Commercial & Medical Affairs: Finding Actionable Insights

Moderna also uses AI to understand and engage with the healthcare community. To analyse feedback from healthcare providers, the company developed Nitro, an AI model built using the Dataiku platform. It uses natural language processing to analyse unstructured data from medical inquiries. This has saved the team an estimated 40 hours per month and reduced the time it takes to analyse sentiment from months to days, allowing for near real-time adjustments to communication strategies.

Corporate Functions (HR & Brand): Driving Efficiency

The AI transformation at Moderna includes corporate functions like HR and Brand Communications. These departments have become hubs of GPT development, creating custom tools to streamline everything from employee benefits questions and performance reviews to the creation of investor presentations and brand messaging.

The Strategic Choice: Why Custom GPTs Were a Game-Changer

When Moderna chose ChatGPT Enterprise in early 2024, they weren’t just buying a better chatbot. They were making a critical strategic decision: to adopt a decentralised development platform instead of a centralised tool. This distinction is the key to understanding their success.

Other enterprise AI solutions were powerful, but they were tools provided to employees. ChatGPT Enterprise, with its built-in “GPT Builder,” did something different. It gave every employee, regardless of their technical skill, the ability to become an AI developer.

1. It Solved the “Last Mile” Problem. A general AI doesn’t know your company’s specific jargon or internal processes. Custom GPTs bridge this gap. The legal team could build a “Contract Companion” that understood their specific contract templates. The HR team could create a “Benefits Assistant” trained on their unique employee handbook. This made the tools hyper-relevant from day one.
2. It Unleashed Grassroots Innovation. Instead of a central IT department trying to guess what 6,000 employees needed, Moderna empowered its people to solve their own problems. An analyst who spent hours formatting data could build a “Data Formatting GPT” in an afternoon without waiting in a development queue. This massively increased the speed of problem-solving.
3. It Drove Ownership and Adoption. People are far more likely to use a tool that they or their direct colleagues built. When the legal team created their own contract tool, it wasn’t an external piece of software being forced on them; it was their solution to their problem. This sense of ownership is why they were the first department to hit 100% adoption.

The impact was profound. Choosing a platform that enabled customisation was the difference between deploying 10 tools and sparking the creation of 3,000. It transformed employees from passive users of AI into active builders, cementing the innovative culture that has become their true competitive advantage.

A Showcase of Custom GPTs

While high-level strategic projects are critical, the true measure of Moderna’s AI adoption is the explosion of custom GPTs built by employees to solve their own specific problems. The company reports over 3,000 such tools have been created. This portfolio of employee-driven innovation demonstrates how deeply AI has been integrated into daily workflows across the entire organisation. The following table highlights ten representative examples.

Moderna AI Rollout Timeline

Architecting the Future – Merging HR and IT

Perhaps the most radical part of Moderna’s AI strategy isn’t a piece of technology, but an organisational redesign. In late 2024, the company merged its Human Resources (HR) and Information Technology (IT) departments into a single function: People and Digital Technology. This is a forward-looking move to create a structure built for a future where human and artificial intelligence work together as a single, integrated workforce.

The Reason: From “Workforce Planning” to “Work Planning”

The main driver for this merger is the realisation that in an AI-powered company, the old silos separating people strategy from technology strategy are a barrier to progress. The decision to use a new AI agent (an IT decision) directly affects job roles and skills (HR’s domain).

Moderna’s solution is to shift to a holistic “work planning” model. This new model asks a more fundamental question: “What work needs to be done, and what is the best mix of human talent and machine intelligence to do it?”

A New C-Suite Role

To lead this new function, Moderna created the role of Chief People and Digital Technology Officer and appointed Tracey Franklin, the former head of HR. The choice to put an HR leader in charge is significant. It signals that Moderna believes the main challenge of the AI era is human-centric organisational design and change management.

Treating AI as a Teammate

This merger structurally supports the view that advanced AI agents are becoming less like passive tools and more like active “teammates.” These agents perform tasks and contribute to business outcomes. In this new reality, it makes sense for the department that manages the human workforce (HR) and the one that manages the digital workforce (IT) to operate as a single unit.

This pioneering structure has both opportunities and risks. The main opportunity is tighter alignment between people and technology decisions, leading to faster and more data-informed organisational design. The risks include a potential culture clash between the different mindsets of HR and IT, and the danger of over-relying on automation for tasks that require human empathy and judgement.

Ultimately, Moderna’s HR/IT merger is a profound strategic statement. It forces a level of integrated thinking that is impossible in a traditionally siloed organisation, proactively designing the company for the next era of work.

Governance, Risk, and Ethics

Moderna’s strategy of giving powerful AI tools to every employee is a key reason for its speed and innovation. However, this approach also comes with significant risks, especially for a company in the highly regulated biopharmaceutical industry. Moderna is actively developing a strong framework for governance and risk management to ensure its AI deployment is responsible, ethical, and safe.

The Central Challenge: Empowerment vs. Control

The main governance challenge is finding the right balance. A model that empowers all employees to build and share custom GPTs encourages rapid innovation. A centralised control model offers more oversight but could slow progress. Moderna’s leadership is working to find a middle ground that captures the benefits of empowerment while managing the risks.

These risks include:

• Inaccuracies and “Hallucinations”: Large language models can sometimes generate confident but incorrect information. In a science-driven industry, the risk of a “hallucination” leading to a flawed conclusion is a major concern.
• Misuse in Critical Processes: There is a risk of employees improperly using these tools in sensitive areas like clinical trial dosing or employee performance reviews.
• Algorithmic Bias: AI models trained on historical data can learn and repeat existing human biases, for example in hiring.
• Data Privacy and Security: Using AI tools requires feeding them data, which can include sensitive employee or research data. Protecting this information is a critical challenge.

Moderna’s Governance Framework

To manage these challenges, Moderna is using a multi-layered governance framework.

• The AI Code of Conduct: Moderna has a formal AI Code of Conduct that outlines its commitment to the responsible and ethical use of AI, grounding its principles in its core corporate values.
• Human-in-the-Loop (HITL) Models: For high-stakes decisions, Moderna uses a “human-in-the-loop” model. AI is used to inform human judgement, not replace it. The Dose ID GPT is a good example. It serves as a powerful analysis assistant, but the final decision on a clinical trial dose stays with human experts.
• Structural and Cultural Guardrails: Governance is built into the company’s structure and culture. The merger of HR and IT creates unified oversight. The AI Academy includes mandatory training on AI ethics.
• Proactive Regulatory Engagement: Moderna is taking a proactive stance with regulators. The company is actively considering its obligations to inform bodies like the FDA about its use of AI in core processes.

Your Actionable Blueprint – 7 Lessons from Moderna

Moderna’s journey provides a powerful and practical guide for any business leader looking to implement AI in a way that creates real, sustainable value. Here are seven lessons that distill their approach into an actionable framework.

1. Lesson 1: Start with Culture, Not Technology. Before you evaluate any software, invest in building AI literacy across your organisation. Replicate the spirit of Moderna’s AI Academy. This builds confidence and creates an educated workforce that is ready to adopt new tools.
2. Lesson 2: Secure a Clear C-Suite Mandate. A large-scale AI initiative needs a clear and compelling vision from the CEO and the executive team. Frame AI as a core business strategy tied to your company’s mission. Focus the narrative on value creation and productivity, not just cost-cutting.
3. Lesson 3: Audit and Modernise Your Digital Foundation. You can’t build a successful AI programme on a weak foundation. Before you scale AI, conduct a thorough audit of your digital infrastructure. Invest in modern, cloud-native platforms. This upfront work is the single greatest accelerator for future AI success.
4. Lesson 4: Empower Your People, Then Govern. Resist the urge to lock down AI tools with a small group of experts. Follow Moderna’s model: give employees access to safe, sandboxed tools and encourage them to experiment. Use creative techniques like prompt contests to find and empower your power users.
5. Lesson 5: Target High-Value, Cross-Functional Problems. Don’t try to solve everything at once. Build momentum by targeting specific, high-impact problems in different business units. This demonstrates the technology’s versatility and builds a broad coalition of support.
6. Lesson 6: Rethink Your Org Chart for a Human-AI Future. The nature of work is changing. You need to be willing to redesign your organisation to support it. Critically evaluate whether your traditional silos, especially between HR and IT, are holding you back.
7. Lesson 7: Build a “Human-in-the-Loop” Governance Model. For all critical business processes, establish a governance model where AI informs, but does not replace, human expertise and accountability. Implement a clear AI Code of Conduct and be proactive in engaging with stakeholders to build trust.