Kew says AI digitization can save plants, pull genetic data from 180-year-old fungi

Royal Botanic Gardens, Kew is essentially betting that AI and digitisation can become a new kind of lifeline in the “race against extinction” facing botanists. In a major report, Kew argues that technology can help scientists track how flowering times have shifted by weeks around the world, rapidly identify new specimens, and even pull crucial genetic data from 180-year-old fungus specimens. That last part is not trivia. Getting genetic information from centuries-old fungi matters because fungi are foundational to ecosystems, and it could unlock what the report calls a “genomic goldmine”.

This is the core play: the rise of AI and digitisation is being positioned as a turning point for conservation work that used to be slow, fragmented, and constrained by access. Kew frames the urgency as time running out for vital plants before they vanish, and then points to new capabilities that could compress the time between discovery and action. Instead of waiting for physical access to collections, digitisation and online access to millions of specimens allow scientists to work from archives that were previously hard to reach. The report also notes that this is producing new insights “especially in the global south,” which is significant for leaders thinking about how conservation value gets created and where expertise can scale.

If you zoom out, the story is about turning biology into searchable data. Botanists, conservation teams, and research institutions typically rely on specimens and field observations, but those are distributed across time, geography, and physical storage. Digitisation changes the workflow. When millions of specimens become available online, the bottleneck shifts from “can we access the sample?” to “can we interpret it fast and correctly?” That is where AI enters the picture, with the promise of rapid identification and analysis. In practical terms, the report highlights rapid identification of new specimens and the ability to track flowering-time shifts by weeks globally, suggesting that AI-enabled pattern recognition and data linking could help scientists detect ecological change earlier than traditional methods alone.

The 180-year-old fungus detail is also a reminder that conservation science is not only about what is happening now, but what can still be learned from the past. The report says scientists can get crucial genetic data from 180-year-old fungus specimens. That matters because it converts dormant institutional assets, like old archived material, into active research inputs. If leaders in biotech, life sciences, and data platforms are watching, this is a preview of how AI can increase the value of existing biological collections, not just generate new lab work. More data, especially genomic data, can improve classification, support ecosystem monitoring, and potentially strengthen conservation decisions.

There is also a strategic implication for governance and accountability, even if the report itself is focused on science. Digitisation and AI depend on data quality, provenance, and access. When millions of specimens move online, institutions must decide how to manage metadata, standardize records, and ensure that researchers can trust what the data represents. For decision-makers, that intersects with a familiar set of operational questions: Who maintains the digitized collection? Who updates taxonomies when classifications change? How do institutions handle rights, access, and collaboration, especially across regions? Kew’s mention of new insights especially in the global south hints at a distribution effect, where digitisation can reduce barriers and broaden who can participate in discovery.

Regulatory framing is another second-order factor. Conservation policies often depend on evidence, and evidence increasingly comes from measurable biological signals: shifts in flowering time, species identification, and genetic relationships. When AI helps detect patterns sooner and extract genetic data from historical specimens, it potentially changes the evidence timeline that regulators and conservation bodies respond to. That is not a claim that any specific regulation is being updated in the source, but it is a logical consequence of how modern decision-making works: faster, richer data can strengthen the case for interventions, and it can also make policy cycles more responsive.

For executives and boards, the stake is not only scientific. It is reputation, partnership power, and budget efficiency. Conservation work competes for funding, and technology that can compress cycles from identification to genetic characterization can reshape spending priorities. Kew’s report positions AI and digitisation as a turning point, which means the organizations that build or support these digitisation pipelines may gain influence across research networks. The strategic question for similar decision-makers is straightforward: if digitised specimen repositories and AI-enabled analysis are becoming the infrastructure for conservation science, how prepared are you to collaborate, invest, or integrate? The “race against extinction” is urgent, but the report’s message is that urgency can be met with infrastructure, not just field labor and goodwill.