Building trust at scale: using AI to strengthen the scientific record

Author: Saskia Steinacker

Chief Product Officer

Each year, more than three million scholarly articles are added to the global scientific record, shaping decisions across medicine, public policy, climate action, economics and technology. As research volumes grow and AI becomes embedded across writing, discovery and evaluation, a central question emerges: how can science scale without losing credibility while meeting the challenges of our time?  

For science, scale without trust is not progress. At Springer Nature, trust is not assumed — it is the outcome of deliberate technical and editorial choices about where automation belongs and where responsibility must remain human. 

Quality and integrity are design choices 

Scientific publishing is fundamentally a validation process. From millions of annual submissions, only a proportion becomes part of the permanent scholarly record. In 2025, we received 3.1 million submissions and published 539,000 primary research articles, showing increased selectivity compared with the previous year and a deliberate focus on rigour and quality, supported by improved systems – not automation. 

At Springer Nature, we are using AI to augment human judgement, not replace it. Editorial responsibility remains with people, while technology operates within defined and auditable boundaries. This is because we believe, used well, AI improves consistency and sharpens focus. It allows editors and reviewers to direct their expertise where it matters most, without compromising quality, relevance or ethics. Publishing, at its best, makes responsibility explicit through shared standards, transparent decisions and consistent expectations across the whole research cycle. 

This approach depends on coherence. Today, more than half of our 3,000 journals run on a single end-to-end publishing platform, helping integrity and accountability scale with output.  

AI that sharpens focus, not noise 

Applied consistently, AI helps direct attention where expertise matters most. In 2025 alone, our AI‑assists enabled 586,000 journal transfer recommendations, 427,000 reviewer suggestions, and 1.58 million uses of journal‑finding tools. 

The impact is practical: better matching of work, reduced reviewer burden and faster time to decision. Faster outcomes do not mean weaker standards — they mean less wasted effort across a growing system. For researchers, this delivers clarity and confidence rather than complexity, reduces friction across the process, and ultimately frees up time and capacity for what matters most: discovery. 

Creating AI‑ready knowledge responsibly 

We are focusing on this because high‑quality research does more than inform today’s debates. It shapes the data foundations on which future discovery — more and more AI‑enabled — will depend.  

As search and discovery increasingly rely on summarized and synthesized outputs, trust depends on traceability: clear links back to primary sources, evidence, and editorial context. High‑quality metadata, attribution, and structured content are therefore essential to preserve the distinction between evidence and inference. 

This is why investment matters. Since 2021, Springer Nature has invested €188 million in technology and talent, including in research integrity, supporting resilient infrastructure and the long‑term stewardship of the scientific record. 

Designing the future of trusted discovery 

We believe technology can accelerate research and strengthen trust at the same time. Doing so requires focus, system‑level coherence and a whole‑cycle view of how scientific knowledge is created, assessed and shared. 

For researchers, this reduces friction and frees time for new discoveries. For society, it means confidence in the evidence underpinning critical decisions. And for the future of science, it means progress built on dependable foundations. 

At Springer Nature, this is why we invest in people and systems, not shortcuts, and apply AI deliberately, with human judgement at the core. Trust at scale is not a constraint on innovation — it is what makes innovation meaningful. 

We will explore these themes further at TECH by Handelsblatt Europe 2026, examining how AI can speed up discovery while safeguarding trust and credibility in science.