Survival of the fittest: How digital drives evolutionary advantage

Fergal Collins

CEO, Aon Centre for Innovation and Analytics - Dublin and Krakow

Has a pandemic created the perfect conditions for the rapid evolution of ‘fitter’ organisations? In this article, we explore how Moderna took a digital-first approach to innovation at pace and scale that should be adopted by other industries to embrace new business models - or face the risk of being left behind.

Fitness is a multi-faceted attribute of course, but for most companies today it almost always comes through digitization. The power of digitization can be recognised as a catalyst, not just for driving enterprise scale and efficiency, but for helping to deliver focussed innovation with much greater precision and speed. But digitization and Artificial Intelligence (AI) are cross-industry challenges - and it’s useful to reference other leading companies to validate the steps you are taking in your own business or indeed to learn from their experiences.

With a background in biotech, I’ve been following how Moderna made itself fit to innovate at scale and is a good marker for measuring our own digital transformation journey. Moderna, formed in 2010, describes itself as “a technology company that happens to do biology” and has famously taken a disruptive approach to innovation. Moreover, its speed-to-market in “solving” for COVID-19 could be the ultimate vindication for a data-first and digital-first approach.

Organising for disruption

Last year, Moderna progressed a COVID-19 vaccine candidate from design to Phase 1 clinical study in two months. The previous record was 20 months, with SARS. That responsiveness, enabled by digitization and scaled deployment of AI, should really resonate with other industries such as insurance.

Traditionally, pharmaceutical companies have taken a cautious approach to product development and have been reluctant to revamp their value propositions by tying digital to the assets that they have - not dissimilar to that of the insurance industry. Moderna took a more disruptive route, postulating messenger RNA (mRNA) as a viable drug but combining that disruptive thinking with a disciplined stage-gated approach for translating the idea to a product.

But Moderna’s success here was not just about having a disciplined methodology - it was also about being attuned to the importance of data in driving better and faster decision-making from R&D, right through to ‘scaled-up’ production processes.

Digitizing the end-to-end processes

Moderna realised that digitization only made sense once processes were understood, defined and optimised. They knew that poor analogue processes led to poor digital processes. A focus on improving efficiency before process automation often begins with process mapping as a basis for then creating a ‘bot’ that can operate and execute the process routinely. But becoming an AI-driven company requires more than just digitizing operations. Digitization is an opportunity to maximise the collection of vast volumes of usable, quality data that can be piped into a centralized analytics platform to drive further waves of insight, innovation and optimization.

Building a data-driven culture

A key enabler in establishing a connected, data-driven enterprise is a modern data and analytics platform. Such a platform enables data sharing and integration and reduces the ‘data friction’ that can impede the cycle of collaboration, innovation and product delivery in an organisation. When data, tools, expertise and processes are brought together on a platform and coordinated in a structured way, we begin to experience reusable ‘delivery patterns’ for change.

At Moderna, three key building blocks were employed to drive the evolution of their platform.

  • First, cloud, rather than building their own infrastructure, was the foundation of everything they did.
  • Second, they wanted business processes and data to be integrated, connected and shared - so having data harmonized across systems, entered once and with the ability to flow freely to whichever team needed the data, was crucial.
  • Third, from both a human and system perspective, they recognized the importance of a common vocabulary for how data is described, logically organized and inter-related. 

Such consistency reduces cross-domain friction when teams come together to solve problems and unlock opportunities, be they sales leaders, product managers or engineers. A common understanding of analytics and data stewardship, established across a much wider group, is also key here.

Platforms are ideally placed to promote and reinforce data literacy and fluency given their central ‘information broker’ role and the fact that policies and behaviours around data usage are more systematic and more easily governed when managed on a platform.

Leveraging the AI ‘factory'

As data, systems and processes become more interconnected and mature, AI really begins to unlock huge value. AI is an umbrella term for several types of cognitive capability including machine learning (ML), natural language processing (NLP), robotic process automation (RPA) and computer vision (which helps machines identify and classify objects – and then react to what they "see").

When properly industrialized, AI serves to deliver new capabilities and optimize existing processes. However, while most companies, including insurers, have already deployed AI to some extent, few have embedded it into standard operating processes across multiple functions (according to McKinsey, about one-third are only piloting the use of AI).

While AI is still in its early days, getting stuck in “pilot purgatory” is a risk. Those who research, innovate and pilot on a global analytics platform, however, are better equipped to promote AI into production. Moderna was able to harvest vast quantities of data as a by-product of their digitized processes early on and was able to bring that data together on a centralised platform. Now when they run experiments, they collect even more data. This allows them to build better algorithms, which helps develop higher quality insights, to develop the next generation of therapeutics. It’s an example of the elusive yet priceless ‘Flywheel Effect’.

Fit and ready

As a devastating pandemic took hold, Moderna was already primed to respond to a rapidly evolving situation. They showed that speed of development would not have been possible were it not for a platform rich with accelerators for innovation. Being a platform company meant that data and capabilities could be re-used to develop multiple products in parallel, and learnings from one product could spill over directly into others (the COVID-19 vaccine was Moderna’s tenth).

Moderna remains driven by the proven advantages of digital and AI in terms of rapid innovation, re-usability and scalability. It also helped that, as a relatively new company, Moderna was largely unencumbered with legacy challenges. But incumbents too, are demonstrating that you can use a similar approach to drive AI and innovation at speed and at scale.

With the right approach, well established organisations can carve out a platform that effectively puts them on the same footing as a digital native. The difference is the pre-existing pool of data, rich network connectivity and deep client understanding that an established firm possesses. Taken together, this is the fuel that powers ‘fitter’ businesses, making them better equipped not only to adapt and survive – but to innovate and thrive.

What you can do now to prepare for the future?

About the Author

Fergal Collins is the CEO for Aon Centre for Innovation and Analytics - Dublin & Krakow and oversees the delivery and operations of analytic products and capabilities using Aon’s Data & Analytic Services Platform. He is a member of the Aon Ireland Leadership team and serves on the Board of Aon Broking Technology (ABT) Ltd. Prior to Aon, Fergal worked for 15 years in Financial Services and Life Sciences sectors. He holds a PhD in Virology from Queen’s University, Belfast.


References to the practices of Moderna within this article have been based on content readily available, in particular the following research papers and sites were used to inform this article: