BNGAI helps Amazon scale biodiversity assessments across England sites

By AI, Created 3:30 PM UTC, May 20, 2026, /AGP/ – Amazon is using AiDASH’s BNGAI platform to assess biodiversity across development sites in England, helping the company evaluate baselines, development impacts and biodiversity net gain requirements. The case study points to a hybrid model that pairs AI and satellite analysis with ecological expertise, and Amazon is considering broader use across more operations.

Why it matters: - Amazon’s use of BNGAI shows how large developers can speed up biodiversity review across multiple sites without relying only on slow, field-heavy surveys. - The approach matters in England, where projects must meet a minimum 10% biodiversity net gain and need defensible data to prove habitat improvements. - The case study suggests AI tools can help companies make planning decisions that better account for environmental impact at scale.

What happened: - Amazon used BNGAI, the AI-powered biodiversity intelligence platform developed by AiDASH, to conduct biodiversity assessments across development sites in England. - Amazon described the work in a new case study. - The platform combines satellite data, remote sensing, machine learning and geospatial analysis. - Phil Birch, Amazon’s Global Biodiversity Lead, said BNGAI is helping the company assess baselines quickly and consistently across its development sites in England. - Birch said Amazon is exploring how to build on the approach as it continues to measure and minimise its potential biodiversity impact.

The details: - BNGAI supported Amazon in assessing biodiversity baselines at development sites across England. - BNGAI evaluated how development scenarios could affect local habitats. - BNGAI calculated the biodiversity units needed to demonstrate biodiversity net gain. - The case study says the AI-powered approach produced results comparable to traditional ecological surveys. - The findings indicate remote sensing and machine learning can complement conventional ecological assessment methods. - Professional ecologists still validated outputs, conducted site visits and refined analytical models. - AiDASH said BNGAI was designed to help organisations understand biodiversity quickly and at scale using AI and satellite data. - The platform is built on AWS infrastructure. - BNGAI combines remote sensing, satellite imagery, machine learning and geospatial analytics to support biodiversity assessment and development planning at scale.

Between the lines: - The shift toward a hybrid model reflects a practical divide: AI can accelerate screening and analysis, while ecologists provide ground truth and regulatory confidence. - The Amazon case study is also a signal that biodiversity metrics are moving from a compliance exercise to a repeatable planning input for large organisations. - AiDASH said the early results show strong potential for broader adoption across Amazon’s operations, including additional business units and international countries. - BNGAI is working with AWS to optimise AI workflows and data processing pipelines, which points to a push for faster and more scalable environmental analytics.

What’s next: - Amazon is exploring how to expand the approach as it continues measuring and reducing biodiversity impact. - The early results could lead to wider use across more business units and countries. - BNGAI and AWS are continuing to refine the data collection and analysis workflow to support biodiversity assessments at scale.

The bottom line: - Amazon’s pilot suggests AI can make biodiversity assessment faster, more scalable and easier to integrate into development decisions, especially where net gain rules demand precise measurements.