5 strategic levers for successfully digitizing Research and Innovation (R&I) organizations
Published July 3, 2025
- Strategy & Transformation
This article has been translated by artificial intelligence.
In an increasingly competitive global landscape, with mounting regulatory, environmental, and technological pressures, innovation is no longer optional—it’s a business imperative.
To meet these challenges, research and innovation (R&I) environments must undergo deep transformation. Digital technologies should not be seen merely as optimization tools, but as true catalysts for disruption. Harnessing the vast data generated from monitoring, research activities, and collaborations can fuel innovation, accelerate time-to-innovate, ensure license to operate, and optimize resource management. The promise of truly data-driven R&I is clear: more innovative, agile, and efficient.
Yet many R&I organizations struggle to initiate and sustain their digital transformation. Barriers such as lack of executive support, cultural resistance, data complexity, and organizational silos often stand in the way. Until these are addressed, digital initiatives in R&I will fall short of delivering their full value.
Key takeaways
- Digital transformation is essential to make R&I more agile, innovative, and efficient.
- Success requires strong leadership, a value-driven vision, and tangible use cases like data visualization to demonstrate early impact.
- Cultural resistance and data complexity must be addressed through researcher support and dedicated roles.
- Clear data governance, including roles like Data Stewards, is critical.
- Close collaboration between R&I and IT is vital to align scientific agility with technical requirements.
#1 A leader to drive digital transformation and prioritize use cases
Digital transformation in R&I is gaining momentum, fueled by promising use cases in labs—automation, robotics, data acquisition, predictive assistants, and more. But to avoid fragmentation, one golden rule applies: prioritize based on value.
Every digital project must address a concrete researcher need and align with a measurable impact strategy. This calls for centralized vision and leadership. A Digital Transformation Leader or Chief Digital & Transformation Officer should champion the initiative, structure the roadmap, and coordinate execution. Working closely with scientific teams, they define performance indicators such as:
- Error reduction
- Improved reproducibility
- Shorter development cycles
- Elimination of low-value tasks
Their role also includes demonstrating that digital R&I is a strategic lever for the business:
- Productivity gains
- Risk reduction
- Accelerated innovation
- Support for ESG goals
These outcomes are key to securing executive buy-in and sustaining investment.
#2 Data visualization: A high-impact starting point
In R&I, where projects are often long and uncertain, digital transformation must quickly prove its worth to win team support. One of the most effective early levers is data visualization. With minimal data preparation, tools like Power BI make it easy to deploy without full IT support.
In just a few clicks, raw, complex data becomes clear visual narratives, giving researchers insight into results, project dynamics, and previously hidden weak signals. Beyond scientific analysis, it becomes a strategic tool—enhancing executive visibility, making impacts measurable, informing decisions, and strengthening dialogue between R&I and IT, especially around data management priorities.
And that’s just the beginning. As transformation progresses, it generates valuable assets: algorithms, cleaned datasets, digital skills, reusable tools—all of which should be leveraged to fuel innovation and maintain executive engagement.
In a demanding competitive and regulatory environment, R&I must reinvent itself to become more efficient, high-performing, and agile. AI presents a unique opportunity to accelerate this shift—provided it’s embedded in a deep transformation of work practices. Success depends on two pillars: upskilling teams and strategically managing research data.
#3 Overcome cultural resistance and open new career paths
.Data sharing in R&I remains a sensitive issue. Researchers are often reluctant to share data they view as strategic—scientifically and competitively. This hesitation stems from intellectual property concerns and a general mistrust of digital tools and AI, which are sometimes seen as threats to scientific autonomy or as devaluing human expertise. Moreover, management rarely incentivizes data sharing.
In this context, imposing technical solutions is not enough. A collaborative approach is needed—through targeted training, better communication on digital benefits, and above all, clear, transparent, and secure data governance. To drive lasting change, tangible incentives are essential: formal recognition, rewards, and career development opportunities.
#4 Dedicated structures and new key roles to manage R&I data complexity
In R&I environments, digital transformation is often hindered by one fundamental challenge: data. R&I data is heterogeneous, scattered, and sourced from internal (sensors, simulations, experiments) and external (patents, publications, partners) origins. It’s difficult to exploit and rarely reused. Experimental culture favors immediate results over reproducible models. Metadata is often missing, contextual information is lacking, and “negative” data is neither archived nor analyzed—resulting in lost collective learning.
To overcome this, clear governance and new roles are essential:
