Novonesis

Mads Torry Smith

Digital R&D Visionary

Across the life sciences industry, digital tools, AI and automation have become central to R&D strategies. But turning these investments into scalable, enterprise-wide impact remains an ongoing challenge for many organizations. In my experience leading digital transformation within Applied R&D at Novonesis, the limiting factor is rarely the tools themselves—it’s the operating model beneath them.

At Novonesis, we develop biosolutions based on enzymes, microbes, cultures and proteins that improve food production, agriculture, household care and industrial processes. These biosolutions are developed using advanced biological processes such as precision fermentation to solve real-world problems in more sustainable ways. Supporting that work requires digital systems that do more than store data. They must shorten experiment cycles, reduce manual data handling, enable collaboration across teams and make it easier to adopt new AI capabilities.

The Limits of Tailor-Made Systems

Most teams feel their science and workflows are special, and they are. This is especially true in biosolutions R&D, where production scale-up and application testing happen under real industrial conditions. The temptation is to respond with fully tailor-made systems for each group. That works for a while, but the maintenance burden grows, integrations become brittle and scaling insights across functions gets harder, not easier.

The risk is not just inefficiency. Custom-built systems often become a bottleneck as organizations try to scale, and without a shift, AI investments remain stuck in pilot mode.

The alternative is to tailor the experience without tailoring the core. Use modular, plug-and-play tools that can be combined as needed. Done well, this creates a stable foundation with flexibility at the edges and allows teams to evolve without constant rebuilds.

For example, one team can design and execute experiments automatically, while another reuses that same data for downstream analysis without reformatting or manual transfer. Similarly, modular systems allow outdated components to be replaced in weeks rather than requiring full system rebuilds.

At Novonesis, teams use different combinations of shared tools to design experiments, execute them and combine data across functions for analysis. The solution feels tailored to each team, but remains standardized underneath. This makes systems easier to maintain, easier to update and far more scalable across the organization.

Connected Data for Modern R&D

Composable systems depend on data that can move reliably between teams and tools. This requires keeping data with the teams who understand it, while making it accessible across the organization.

That means clear ownership, strong documentation, consistent identifiers and built-in quality checks so data can be shared without losing meaning.

For a global biosolutions company, this is critical. Discoveries in one area often inform another. Enzymes used in household care may have applications in food production. Fermentation insights in agriculture may accelerate progress in human health. When data is structured and accessible, those connections happen faster and more consistently.

In practice, this shifts teams from “sending files” to “serving data.” Instead of searching for exports, teams can access ready-to-use datasets on demand. The result is less manual handling, higher data quality and a foundation that is immediately usable for AI.

From Local Data to Organizational Insight

When data is treated as belonging to one team, its value stays local. When it is treated as a shared asset with clear ownership and structure, it becomes a multiplier.

This shift improves outcomes in tangible ways. Teams spend less time cleaning and moving data and more time running experiments. Insights can be reused across functions instead of recreated. Collaboration becomes easier because teams are working from consistent, trusted data.

Clear roles make this work. Data producers ensure quality and structure. Data consumers use it as intended and provide feedback to improve it.

A simple but effective habit is intentional data planning: defining what to capture, where it lives, how it connects and why it matters. This aligns cross-functional teams and ensures data is useful beyond its original purpose.

Leadership and Change Management Matter Most

This transition is less about technology and more about leadership. Leaders must set expectations that move teams away from one-off builds and toward scalable solutions. Success should be measured not by how much data is stored, but by how effectively it is reused across the organization.

Equally important is helping teams understand why structured data matters. When positioned as a scientific enabler rather than administrative overhead, adoption improves.

At Novonesis, we reinforce this through leadership forums that emphasize data quality, clear data flows, stewardship and a culture of curiosity and exploration.

Preparing Data Foundations for AI

As AI evolves, the organizations that benefit most will be those with clean, connected, well-governed data. Without that foundation, new tools add complexity instead of value.

With the right foundation, the opposite is true. New capabilities can be introduced quickly, outdated tools can be retired smoothly and teams can adopt innovation without disruption.

We think of this as building a plug-and-play AI layer on top of a connected data foundation. That only works if core data is structured, accessible, and ready to use.

It is easy to chase the newest tools, but real progress comes from getting the fundamentals right: intentional data capture, respect for workflows, shared ownership and strong change management.

Instead of betting on tools that might mature into enterprise solutions, we focus on building modular systems that allow new capabilities to be added when they are ready. The specific platforms will evolve, but the ability to work with structured, connected data will remain a lasting advantage.

For life sciences companies, this is not just an IT consideration. It is a competitive necessity.

The companies that win will not be the ones with the most tools, but the ones that can scale insight across their organization.