Our vision

Data and AI are reshaping R&D, driving a massive shift in innovation potential.

AI changes what research and innovation teams can achieve:

Faster time-to-market, by replacing part of physical testing with in-silico evaluation and automated R&D processes.
Optimised R&D costs, with fewer trial-and-error iterations.
Generative ideation and product superiority, exploring far more options than any lab could test.
Predictive product design, anticipating the performance of a product before it is made.

AI for R&D is not AI as usual. R&D works with particular data (experimental, legacy, often sparse and heterogeneous), serves unique usages close to scientific discovery, and requires higher data and AI maturity than most business functions.

Unlocking its full potential takes:

Robust data foundations: turning legacy and experimental R&D data into AI-ready assets.
An integrated organisation: embedding data and AI capabilities directly within research labs.
A hybrid talent strategy: bridging the gap between data science and scientific expertise.
A cultural shift: driving AI adoption through upskilling and change management.
Strategic ecosystems: partnerships and make-or-buy decisions.

Data and AI for R&D require a tailored approach, competencies and tools.

What high-performing R&D organisations do

We conducted a benchmark of the impact of data and AI on R&D with more than 10 industry leaders in pharmaceuticals, food and beverage, consumer goods and beauty. Four lessons stand out.

1. From trial-and-error to AI-driven discovery

A 10 to 20% reduction in time-to-market with AI adoption. AI cuts time-to-market and R&D costs by replacing physical testing with generative ideation, in-silico evaluation, digital twins and the automation of R&D processes.

2. Data as a competitive advantage

Many leading companies are running major data transformations, modernising their R&D data foundations to unlock faster analytics, better collaboration and stronger AI deployment.

3. Dedicated R&D data and AI teams

Leading firms integrate data and AI expertise directly into R&D teams, giving them more autonomy to experiment and treating AI as a core scientific capability.

4. A hybrid talent shift

Successful transformations rely on upskilling, change management and strong recruitment, with the emergence of hybrid scientific and data roles. Organisations that invest in people alongside technology are several times more likely to outperform.

Up to twice the innovation rate in sectors where products are mostly intellectual property or close to scientific discovery.

Turning R&D into a strategic performance engine

R&D and innovation have three core missions:

Research and insight: strategic technology watch and deep scientific exploration, to build long-term competitive moats.
Innovation: translating science into unique product and process benefits.
Development and scaling: moving from lab to factory, and making sure products are viable, cost-effective and scalable.

Across an iterative R&D process (scientific pioneering, product design and prototyping, development and industrialisation, market launch preparation, fed by consumer and market insights and steered by R&D strategy and governance), AI accelerates every stage transition and addresses four stakes:

Align science push with consumer pull: connect scientific breakthroughs with consumer needs to build high barriers to entry while ensuring safety and market relevance.
De-risk innovation and validate market fit: reduce technical uncertainty and ensure product distinctiveness and consumer relevance.
Secure industrial scalability: bridge the lab-to-factory gap and accelerate time-to-market by ensuring formula and process scalability and business viability.
Master compliance and claim substantiation: ensure global compliance and run rigorous clinical trials that provide undeniable evidence for product claims and consumer trust.

AI use cases across the R&D value chain

Scientific discovery

Generative and molecular design
Virtual and molecular screening
Scientific literature scraping
Advanced bio-analytics

Product development and design

Expert prediction
Predictive modelling
Product digital twins
Formulation optimisation (product and packaging)
Causal inference modelling

Clinical trials

Trial design and planning
Trial outcome prediction
Clinical trial data collection
Clinical study automation
Clinical image evaluation
Real-world evidence

Across functions

Consumer insights: consumer behaviour, sensory science, consumer insight mining
R&D process optimisation: knowledge transfer, data management, internal AI assistant, robotic lab automation
Marketing: personalised packaging, 3D product twins for content creation
Manufacturing: factory process optimisation, digital twins of products and production lines
Supply chain: supplier and ingredient intelligence, supply yield prediction

Why Emerton Data

We draw on deep scientific legitimacy to bridge the gap between state-of-the-art research and industrial performance.

Our people: advanced hybrid competencies

Our team brings together hybrid profiles (bioinformaticians, PhDs, data engineers and strategists) to unlock the value of data with state-of-the-art methods and advanced analytics.

30% of the team holds a PhD.
Bioinformaticians specialised in life sciences, healthcare and biotech.
An AI Research Director leading our own research strategy.

Scientific excellence and industrial innovation

We practise what we preach, reinvesting in scientific research to build proprietary assets. By bridging the academic and industrial worlds, we develop unique solutions for non-standard challenges.

Scientific papers, such as GLIDE for evaluating agentic systems.
A collaborative initiative with scientific organisations such as the Lagrange Foundation (four Fields Medals).
Our start-up studio, with deep expertise in AI and deeptech.

Field-proven R&D excellence

We deliver high-stakes assignments in sectors where R&D precision is critical, including healthcare, food and heavy industry, covering the entire journey from strategy to industrialisation:

AI strategy and roadmap for R&D: prioritising high-impact use cases and defining target operating models.
End-to-end implementation: proofs of concept followed by full-scale industrialisation of complex assets such as digital twins or agentic AI tools.
Process optimisation and people: managing the cultural and structural shifts needed to integrate data and AI into scientific workflows.

Our offer

A holistic approach and a complete offer to turn the potential of data and AI into R&D performance.

Data and AI strategy and roadmap

Roadmap definition: identifying, prioritising and framing high-impact R&D use cases with a robust, proven methodology.
R&D target operating model: designing the governance, data flows and organisational structures that support data and AI integration.
Make-or-buy strategy: a clear framework to decide between internal development and external partnerships, to optimise ROI and speed.

End-to-end implementation (“data and AI doers”)

Rapid prototyping and proofs of concept: validating technical feasibility and business value in weeks, not months.
Industrialisation at scale: turning a successful prototype into a robust, scalable solution integrated into core industrial processes, unlocking internal synergies.
Proprietary R&D: drawing on our internal research lab and AI research to bring academic breakthroughs to industrial applications.

People and organisational transformation

Strategic acculturation: dedicated seminars for executive committees and senior management, to align leadership on AI stakes and opportunities.
Change management: supporting new ways of working to ensure long-term adoption of AI-driven tools.
Upskilling and hybridisation: defining and delivering training strategies to build the hybrid profiles that bridge R&D and data.