Innovation in Heritage

The biodiversity monitoring programme at Weaveley Solar Farm has entered its second year and continues to generate a robust body of empirical evidence. Leveraging IoT-enabled sensing, ecological surveying and bioacoustic analysis, the programme is demonstrating how renewable energy infrastructure can coexist with—and in some cases actively support—biodiversity. The emerging methodology is evolving into a practical, evidence-based framework with potential relevance for wider adoption across the renewable energy sector. 

Findings to date indicate that the presence of solar infrastructure measurably influences local microclimatic conditions, including soil moisture, temperature and vegetation dynamics. The deployment of continuous, automated monitoring now provides high-resolution datasets across invertebrate, avian, bat and soil communities. This level of precision enables us to distinguish infrastructure-driven effects from natural environmental variability with a high degree of confidence. 

The next phase will focus on extending longitudinal datasets to strengthen trend analysis and enhance predictive modelling capability. Parallel work will refine automated species identification, adaptive sampling methodologies and scalable monitoring protocols, with a view to replication across additional sites. These insights will also inform a more biodiversity-led approach to habitat management across the wider estate. 

Collectively, the Weaveley programme demonstrates that continuous, automated biodiversity monitoring within solar environments is both operationally viable and scalable, without compromising energy generation objectives. It provides a credible model for integrating ecological performance into the design and management of renewable infrastructure, strengthening both conservation outcomes and long-term sustainability planning. 

The AI Implementation Strategy has been deliberately structured to build organisational fluency in artificial intelligence before pursuing large-scale technical deployment. The initial focus has been on developing practical understanding—introducing widely accessible tools such as ChatGPT and demonstrating their application in day-to-day workflows. This approach prioritises confidence and capability-building as a precursor to more formalised implementation. 

Delivery has centred on a combination of targeted workshops, team-based training and internal communications. Approximately 100 staff have participated in AI training, either through structured sessions or open access formats. This has been complemented by a 30-day email programme designed to reinforce learning, increase exposure to use cases and encourage hands-on experimentation. 

Adoption is now becoming more embedded across the organisation, with current estimates indicating that approximately 42% of staff are using AI tools within their day-to-day roles. While many applications remain relatively simple, they are delivering incremental efficiencies and demonstrating the cumulative value of small, practical interventions. 

The programme has intentionally emphasised learning and experimentation over rapid, top-down deployment, resulting in a measured pace of progress. Value is being assessed pragmatically rather than assumed; for example, the Marketing team is currently piloting AI-driven note-taking to determine its effectiveness prior to any broader rollout. 

The next phase is transitioning towards more targeted implementation. Early use cases are beginning to demonstrate tangible impact, including an automated finance report delivering approximately five hours of time savings, an HR dashboard expected to save around six hours, and a Pye Homes initiative with potential to reduce workload by up to six days. In parallel, management capability is being developed through the PMP programme to ensure leaders are equipped to manage teams operating in increasingly AI-enabled environments. 

The most significant shift to date has been cultural. There is a growing expectation across teams to identify opportunities for efficiency and improvement through AI. This is evident in both incremental workflow enhancements and more innovative internal developments, such as the Tree and Fuel applications. Complementary work exploring the intersection of AI and mental health is also broadening the organisational perspective, ensuring that adoption is considered in terms of both productivity and its wider human impact. 

We are approaching the conclusion of our collaboration within the CULTURATI programme, a Horizon Europe–funded initiative that has materially advanced how visitors engage with Blenheim’s heritage. The resulting digital platform, incorporating a voice model based on former Palace Administrator Archie Illingworth, introduces a more personalised, narrative-led approach to interpretation, combining technical innovation with historically grounded storytelling. 

Through this interface, visitors are able to navigate the Palace’s collections, architecture and social history in a more adaptive and individually relevant manner. The experience is designed to respond to differing interests and levels of prior knowledge, enabling a more self-directed and contextually rich engagement with the estate. 

The programme concluded with a keynote event hosted at Blenheim in January 2026, coordinated by Bilkent University. The event brought together over 200 participants from across the consortium, representing partners from Italy, Finland, Spain, Germany, the United Kingdom and Türkiye. 

This final milestone marked the completion of a three-year collaboration and the delivery of two fully developed prototype applications for AI-supported visitor experiences within the cultural heritage sector. The programme provides a substantive foundation for future development in personalised, technology-enabled interpretation across heritage and arts contexts. 

The Digital Twin programme—built on the Cesium platform in collaboration with the University of Oxford and with input from the Heritage Innovation Laboratory Oxford—is progressing from conceptual development into a functional prototype. The model now integrates asset registers, operational datasets and early-stage predictive maintenance capabilities within a unified spatial environment. 

In addition to real-time and operational data, the Digital Twin incorporates extensive archival records directly linked to the model. This layer, developed by Clara Saliba, effectively transforms the twin into an interactive gateway to the estate’s historical archive, enabling access to decades of maintenance data alongside machine learning models trained to detect patterns in issues such as stone fall, water ingress and material degradation. 

This capability has been enabled through the large-scale digitisation and structuring of previously unindexed archival material. By combining OCR and LLM-based workflows, narrative records have been converted into structured datasets, making them suitable for visualisation and advanced analytical use. 

The integration of these datasets underpins the development of predictive models aimed at supporting more proactive maintenance strategies and improved resource efficiency. While still maturing, the system is continuously learning from new inputs, with the expectation that predictive accuracy will improve over time, enabling earlier intervention and reduced reliance on reactive maintenance approaches. 

This work forms part of a broader transition towards a more integrated and intelligent estate model. Future development will focus on increasing the fidelity of the twin, extending coverage to detailed architectural features and interior assets—potentially exceeding 50,000 individual elements—and incorporating regular drone-based surveys to provide near real-time updates on building condition and change over time. 

The estate-wide Internet of Things (IoT) network continues to expand in both scale and analytical capability. Beyond data collection, the focus is increasingly on extracting actionable insight—enabling the ‘Smart Estate’ to identify inefficiencies across energy, heat and water systems. The introduction of agentic AI to interpret sensor data is expected to further enhance our ability to optimise resource consumption and drive more autonomous operational decision-making. 

The network supports a diverse set of use cases across the estate. Within the Palace, sensors monitor environmental conditions critical to conservation, including temperature, humidity and light exposure. In ecological zones such as Weaveley Solar Farm, instrumentation captures biodiversity and soil data. Operationally, the same infrastructure underpins visitor flow analysis, energy optimisation and environmental control across facilities, providing a unified sensing layer for both heritage and commercial contexts. 

A particularly applied use case has been the proactive management of mould within estate buildings. By continuously monitoring temperature, humidity and CO₂ levels, the system enables early identification of conditions conducive to mould formation, allowing intervention before structural damage or health risks emerge. The extension of this capability into residential properties—through the deployment of sensors and gateways—also provides tenants with greater visibility and control over their indoor environments. 

System Schematic
IoT Controller Based Solution

The IoT infrastructure is increasingly integrated with control systems, including the Building Management System (BMS), enabling a transition from passive monitoring to active optimisation. Real-time environmental data is used to dynamically adjust heating, ventilation and lighting in response to actual conditions and usage patterns, rather than fixed schedules. This approach reduces unnecessary energy consumption while maintaining appropriate conditions for both visitor experience and conservation. Collectively, these measures support an estate-wide target of achieving a 20% reduction in energy use, demonstrating the viability of applying advanced control strategies within a heritage context. 

Protecting Our Waterways: Smart Monitoring for Cleaner Rivers

In parallel, the monitoring framework is being applied to environmental risk management, including the tracking of pollutants associated with sewage discharge and agricultural runoff. This data-driven approach supports more informed decision-making in relation to water quality, conservation and long-term environmental stewardship. 

As part of the SBRI Enhanced Clean Air Innovation Trials, the team is working with Lightricity to deploy battery-free indoor air quality sensors powered by high-efficiency photovoltaic technology. These devices are being installed across selected tenanted properties, with a focus on translating complex environmental data into accessible, actionable insights. This collaboration demonstrates how low-impact sensing technologies can be integrated into heritage settings to improve living conditions while reducing maintenance overhead. 

IoT Innovation: Enhanced Clean Air Innovation Trials

As part of the SBRI Enhanced Clean Air Innovation Trials, our Innovation Team is working with Lightricity to revolutionise indoor air quality monitoring in heritage properties. Working with Lightricity's battery-free sensors, powered by high-efficiency indoor photovoltaic technology, we're deploying these devices throughout selected tenanted properties across our estate. Our team is leveraging our expertise in data visualisation and user engagement to transform complex environmental data into accessible, actionable insights for our tenants. This innovative approach not only helps residents understand and improve their indoor air quality but also demonstrates how historic properties can embrace sustainable technology to enhance living conditions. By combining Lightricity's cutting-edge sensor technology with our experience in heritage property management, we have established new standards in environmental monitoring that enhance living conditions while reducing long-term maintenance costs.

Taken together, these initiatives represent an initial phase in a broader strategy centred on water stewardship and environmental management. By combining IoT infrastructure with active intervention and open data principles, the programme aims to support the long-term health of the estate’s natural assets while promoting transparency and collaborative approaches to conservation. 

In partnership with Reuniverse, Mastercard and the Eden Project, Blenheim Palace continues to advance more sustainable visitor operations through the deployment of a returnable cup system. The initiative has removed the need for over 300,000 single-use cups annually at Blenheim, delivering a measurable reduction in waste and associated carbon emissions. 

The model integrates sustainability objectives with digital infrastructure, utilising Mastercard Send to enable near-instant deposit returns via smart vending machines. With more than 5,000 cups in active circulation and a return rate of approximately 83%, the system has already avoided in excess of 1,150 kg of CO₂ emissions. The associated digital platform also supports user engagement and contributes funding towards further environmental initiatives. 

This programme forms part of Blenheim’s broader pathway towards achieving carbon neutrality by 2027. Its demonstrated impact and operational viability have attracted interest from major UK retailers and sector stakeholders, positioning the estate as a credible testbed for scalable, technology-enabled sustainability solutions within the visitor economy.