Artificial intelligence is rapidly changing how natural history collections move from dusty cabinets into searchable, global datasets. Instead of treating digitization as a slow, manual chore, institutions are beginning to use machine learning to accelerate the work and reveal patterns that were effectively invisible on paper. The result is a shift from basic scanning to a deeper form of digital stewardship that can reshape research on biodiversity, climate and culture.
As archives, museums and libraries experiment with new tools, they are discovering that AI can not only speed up transcription and georeferencing but also expose bias, surface hidden stories and connect scattered records into coherent narratives. The challenge now is to harness that power without losing the curatorial judgment, context and care that make natural history archives more than just data.
From static scans to living data infrastructure
For years, digitization projects in natural history focused on volume: scan the labels, photograph the specimens, and move on. That approach created vast image repositories but left much of the scientific value locked in unstructured text and inconsistent metadata. I see the current wave of AI tools as a pivot from simple imaging to building a living data infrastructure, where every specimen record can be searched, analyzed and recombined across collections.
Corporate and institutional archivists have already learned that Digitization Unlocks Institutional Memory Digitizing is not just about preservation, it is about turning dormant material into strategic, trustworthy information that can support research and decision making. Natural history institutions are applying the same logic to specimen labels, field notebooks and habitat descriptions, treating them as a foundation for high quality AI training data rather than static scans. When digitization is framed as infrastructure, investments in better metadata standards, controlled vocabularies and linked data become central to the mission instead of afterthoughts.
AI-assisted georeferencing and the speed problem
One of the biggest bottlenecks in natural history digitization has always been georeferencing, the painstaking process of turning vague locality descriptions into precise coordinates. AI is beginning to attack that problem directly, using natural language processing and spatial models to infer likely locations from historical place names, collector routes and contextual clues. That shift matters because it turns a task that once took specialists hours per record into something that can be triaged at scale, with humans focusing on the hardest cases.
Researchers working on natural history archives have shown that by deploying AI powered georeferencing, scientists may soon be able to rapidly digitize vast natural history collections and attach usable coordinates to them, a change that directly affects how quickly biodiversity data can be mapped and modeled, as described in work on AI Boosts Speed of Digitizing Natural History Archives. A related study at UNC-Chapel Hill has underscored the stakes, reporting that AI driven workflows can dramatically speed up processing and give researchers unprecedented opportunities to advance understanding of global biodiversity distributions, a promise highlighted in the UNC-Chapel Hill study on AI and specimen data. Together, these efforts suggest that georeferencing is moving from a chronic bottleneck to a proving ground for AI assisted curation.
Turning messy labels and manuscripts into machine-readable text
Even when specimens are photographed and roughly georeferenced, the real scientific gold often sits in cramped handwriting and idiosyncratic abbreviations on labels and field notes. Traditional OCR has struggled with these materials, especially when ink has faded or scripts vary from one collector to another. I see AI based handwriting recognition as the bridge between those analog quirks and the structured text that modern databases require.
Specialists in digital archiving have pointed out that once you have scanned your holdings, the next question is, as one guide bluntly puts it, So You have Digitized Your Materials, Now What, and the answer increasingly involves Smarter Text and Handwriting Recognition that can outperform traditional OCR on complex documents. In the realm of historical manuscripts, researchers have stressed that merely digitising manuscripts and codexes is not enough and that a further step is needed, the digitalisation of content through AI models that can segment lines and interpret scripts in almost all cases, a point made explicit in work on However digitising manuscripts and codexes. When those techniques are applied to specimen labels, they can turn decades of backlogged handwriting into searchable, analyzable text that feeds directly into AI models for species distribution, trait analysis and climate impact studies.
Mining habitat descriptions for ecological insight
Natural history collections are not just lists of species and coordinates, they are also rich narrative records of where and how organisms lived. Detailed habitat descriptions often accompany natural history specimens, capturing information about vegetation, soil, associated species and human land use that rarely appears in modern databases. AI is particularly well suited to mining those free text descriptions for ecological signals that can be compared across time and space.
Curators at institutions such as the Ohio History Connection have emphasized that Detailed habitat descriptions often accompany natural history specimens and that these narratives can be linked to modern environmental data, including work with partners like the Natural History Museum in London. When AI models are trained on those descriptions, they can flag patterns such as shifting plant communities, changing water regimes or the arrival of invasive species long before those trends show up in satellite imagery or contemporary surveys. For biodiversity researchers, that means historical archives become not just a record of what was collected, but a time series of ecological context that can inform conservation decisions today.
Confronting bias and harmful language in digital collections
As institutions race to digitize, they are also confronting the uncomfortable reality that many historical records contain derogatory language, colonial framing and biased taxonomies. Scaling up access without addressing those issues risks amplifying harm and embedding skewed perspectives into AI models that will be used for decades. I see this as one of the most urgent reasons to integrate ethical review into AI driven digitization from the start.
Work with museum collections has shown that Museums can use AI to uncover derogatory language and bias in digital collections, helping staff identify problematic terms that might otherwise remain buried in catalog records. Staff at the Harvard University Her collections have been involved in these efforts, using AI to surface patterns of description that reflect historical prejudice and to guide remediation strategies that respect both the archival record and contemporary values. For natural history archives, similar tools can flag outdated racial terminology in field notes, extractive language about Indigenous lands or gendered assumptions in collector biographies, giving curators a starting point for contextual notes, content warnings or revised metadata.
AI as a partner, not a replacement, for archival expertise
Despite the hype around automation, the most thoughtful voices in digital stewardship are clear that AI should augment, not replace, human expertise. Natural history archives are full of edge cases, from mislabeled specimens to ambiguous locality descriptions, that require deep domain knowledge and sometimes direct consultation with descendant communities. I view AI as a way to triage and prioritize that expert attention rather than a shortcut that can safely run on autopilot.
Archivists and records managers have argued that AI is opening exciting new frontiers for archives and records management by augmenting what professionals can do while still centering the mission of preservation and access, a point captured in discussions of Conclusion and the role of AI in archival access. Others have framed AI’s Role in Preserving Digital Archives as a powerful but double edged tool, noting that AI holds great promise for improving discovery and preservation but that issues of authenticity, bias and accessibility must be carefully managed, as outlined in analyses of Role and Preserving Digital Archives alongside The Current Challenges in Digital Archiving. For natural history institutions, that means building cross functional teams where data scientists, taxonomists, community advisors and digital archivists share responsibility for how AI is deployed and evaluated.
Surfacing hidden histories in natural history collections
Natural history archives are not only about species, they are also about people, power and the politics of knowledge. Field notebooks, correspondence and specimen tags can reveal who collected what, on whose land and under what conditions. AI can help surface those human stories at scale, but doing so responsibly requires sensitivity to the ways in which colonialism, extraction and exclusion shaped the collections in the first place.
Archivists reflecting on AI have described how machine learning can bring hidden histories to light by connecting scattered references, identifying under documented contributors and revealing patterns of omission, a theme explored in work that looks at Looking ahead and offering Insights for skeptics about AI and the future of digital stewardship. In natural history, that might mean using AI to trace the contributions of local guides whose names appear only in passing, to map collecting expeditions that crossed Indigenous territories without acknowledgment, or to highlight the labor of women and assistants who prepared specimens but were rarely credited as authors. By making those patterns visible, AI can support a more honest reckoning with how collections were built and who has historically benefited from them.
Designing AI ready archives from the ground up
As institutions plan new digitization projects, a key lesson is that AI works best when archives are designed with machine use in mind from the outset. That does not mean sacrificing human readability, but it does mean standardizing fields, documenting provenance and capturing relationships between records in ways that algorithms can understand. I see this as a shift from retrofitting AI onto existing databases to building AI ready pipelines that treat metadata quality as a core asset.
Guides on digital archiving stress that the digital age has transformed expectations for access and that AI’s Role in Preserving Digital Archives depends on addressing The Current Challenges in Digital Archiving, including inconsistent formats and incomplete metadata, as discussed in analyses of The Current Challenges in Digital Archiving. Specialists in special collections have also argued that as AI becomes more sophisticated, the potential for further innovation in the digitization of special collections is vast, but only if materials are described and structured in ways that keep them discoverable and useful to future generations, a point made in discussions that begin with the phrase As AI becomes more sophisticated. For natural history archives, that translates into practical steps such as adopting shared taxonomic backbones, using persistent identifiers for specimens and people, and documenting uncertainty in ways that AI models can interpret rather than ignore.
Balancing speed, ethics and long-term stewardship
The promise of AI turbocharging digitization is real, but so are the risks of moving too fast without clear guardrails. Natural history institutions are custodians of irreplaceable records that will outlast any current technology stack, and they have to weigh the benefits of rapid processing against questions of consent, representation and long term preservation. I see the most responsible projects treating AI as one layer in a broader stewardship strategy rather than a silver bullet.
Commentators on digital stewardship have urged archivists to think beyond short term efficiency gains and to focus on how AI can support a future they are excited about, not one they feel pushed into, a perspective that runs through the Insights for skeptics about AI and archives. For natural history collections, that means setting clear policies on how AI generated annotations are labeled, how communities can challenge or correct algorithmic interpretations, and how models are archived alongside the data they were trained on. The institutions that get this right will not only digitize faster, they will build trust in the digital versions of their collections as reliable, accountable records of the natural world.
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