Artificial intelligence is often associated with automation, productivity and operational efficiency. For many organisations, AI promises faster processes, improved insights and the ability to do more with existing resources.

When a disaster occurs, attention naturally turns towards response.

Emergency services mobilise. Damage is assessed. Resources are directed towards the areas of greatest need. Technology plays an increasingly important role in helping organisations understand what has happened and coordinate action.

The most difficult decisions are often made when the least information is available.

Following a major disaster, emergency responders, governments and humanitarian organisations must quickly understand what has happened, where the greatest risks exist and how limited resources should be prioritised.

Artificial intelligence is frequently judged by a single number.

Accuracy scores, precision, recall and benchmark rankings provide useful ways of comparing models, and significant progress has been made across many areas of computer vision, natural language processing and predictive analytics. These metrics remain important indicators of technical capability.

When disaster strikes, information becomes one of the most valuable resources available. Satellite imagery is captured within hours, drones survey damaged infrastructure, emergency responders collect photographs from the ground, and local communities contribute reports through mobile devices and social media. Together, these diverse sources of information help create a rapidly evolving picture of events, enabling authorities to prioritise rescue efforts, assess damage and begin planning recovery.

Artificial intelligence has achieved remarkable performance across a wide range of tasks. From recognising objects in images to generating natural language and supporting complex analytical workflows, AI systems are increasingly being integrated into decision-making across both the public and private sectors.

The past decade has seen a significant transformation in how we capture information about the world around us. Advances in 3D scanning, photogrammetry, drones, remote sensing, and IoT technologies have made it possible to collect vast quantities of high-resolution data from buildings, landscapes, infrastructure, artefacts, and environments.

For decades, damage assessment has relied on photographs, plans, sketches, and written reports.

These approaches remain valuable and will continue to play an important role in emergency response, infrastructure management, insurance assessment, and recovery planning.

However, they share a common limitation.

They flatten space.

When disaster strikes, whether through flooding, wildfire, earthquake, storm damage, industrial accidents, or infrastructure failure, the first 72 hours are often the most consequential.

For much of the past decade, advances in 3D imaging, spatial computing, and artificial intelligence have been closely associated with cultural heritage.

Researchers, museums, archaeologists, and conservation specialists have used these technologies to document fragile artefacts, reconstruct lost environments, and create immersive experiences that allow people to engage with history in new ways.

There is a pattern in how many organisations have approached AI over the past few years, and it is becoming harder to ignore. A working group is assembled. A pilot is commissioned, often in a corner of the organisation where risk is low and visibility is high. A demo goes well. Leadership is impressed. A press release is issued. And then, quietly, the project stalls. It never makes it into daily operations. It is not maintained, not measured, and not improved. Six months later, the same organisation launches another pilot.

For the past several years, generative AI has dominated discussion across the technology sector.

Large language models, image generators, code assistants, and multimodal systems have demonstrated remarkable capabilities, attracting significant investment and public attention. Organisations across every sector have explored how generative AI might improve productivity, reduce administrative burdens, accelerate content creation, or support innovation.

AI regulation is no longer an abstract concern. The EU AI Act is in force, its obligations are phasing in on a staggered timetable, and it has already been amended once, significantly, less than two years after its original adoption.

For much of the past decade, organisations focused on what AI could do: automate processes, identify patterns, improve efficiency, and generate new insights. Today, as AI becomes embedded within operational systems and decision-making processes, a different question is increasingly being asked.

Over the past several years, discussion around AI has often focused on capability: larger models, improved performance benchmarks, greater automation, and increasingly sophisticated outputs. While technical capability remains important, a significant shift is now taking place across both public and private sectors.

For many organisations, AI procurement used to be treated like any other technology purchase: compare features, review costs, negotiate support, and move forward.

For many SMEs, the idea of adopting AI can feel intimidating. Headlines often focus on large technology firms employing teams of data scientists, machine learning engineers, and AI researchers, creating the impression that meaningful AI adoption requires significant internal scale.

AI is no longer confined to technical teams. It shapes procurement, strategy, risk, and reputation. Organisations are making consequential decisions about AI-powered tools, vendors, and processes every day and those decisions reach the boardroom whether leaders are ready or not.

Digitisation is often framed as preservation. Scan it, store it, and the problem is solved.

But the reality is more complex.

Without careful design, digitisation can unintentionally strip context, flatten meaning, and obscure provenance, creating a form of digital erasure rather than preservation.

As cultural organisations digitise collections and adopt AI tools, data is often framed as an asset, something to be stored, analysed, and reused.

But cultural data is not just content.

It represents histories, identities, and communities. And with that comes responsibility.