Museums, galleries, and festivals have always been testing grounds for new ways of engaging audiences. Today, they’re also proving grounds for artificial intelligence, not just as a back-end tool, but as part of the visitor experience itself.

AI is now embedded in decisions that affect businesses, heritage organisations, and public institutions, from classifying 3D scans to generating content, prioritising workflows, and supporting risk assessments. Yet the tools used to make those decisions are often opaque.

Earthquakes remain one of the world’s most destructive and unpredictable natural hazards. Yet, despite decades of research, the tools available to assess damage and guide recovery often lag behind what modern technology could enable. Manual inspections, inconsistent data collection, incomplete information, and slow reporting all contribute to delays at the exact moment when communities need clarity the most.

Next week, Aralia Systems will join innovators, broadcasters, commissioners, and digital creators at Content London 2025, where the BridgeAI programme is hosting a dedicated showcase of emerging AI businesses. We’re delighted to be exhibiting within the Battlebridge Room at King’s Place, demonstrating how AI and 3D imaging can support the creative industries as they navigate rapid technological change.

Across the UK, demand for AI-capable workers is rising faster than supply. Yet most of the national conversation focuses on large technology firms, data scientists, and cutting-edge research labs. Left out of the narrative are the organisations who arguably need AI skills the most:
SMEs, microbusinesses, and cultural institutions, the groups who form the backbone of the UK economy and safeguard its collective heritage.

Open-source AI has surged in popularity over the past two years, driven by growing concerns around vendor lock-in, data sovereignty, rising compute costs, and the need for local control. For SMEs, particularly those working in heritage, culture, education, and the wider creative industries, open-source tools promise something commercial platforms often cannot: transparency, affordability, and autonomy.

Heritage organisations are now facing threats that move faster than traditional conservation planning can handle. Climate-driven decay, conflict-related damage, looting, and urban development pressures have each accelerated over the last decade and cultural bodies are being forced to make decisions with incomplete, rapidly changing information.

The regulatory landscape for AI is shifting. As 2026 approaches, UK and EU frameworks are moving from consultation to implementation, bringing both clarity and complexity. For SMEs and heritage organisations already navigating tight budgets and evolving technologies, the question isn't whether to prepare, it's how.

How will future generations remember their past?
As AI and 3D technologies converge, we’re entering a new era of living cultural memory, one where archives can be explored, reconstructed, and reinterpreted in immersive digital form.

The early trials of Elata demonstrated what’s possible when 3D imaging meets education immersive, accessible experiences that bring learning to life. But those pilots were just the beginning.

As XR tools mature, the next challenge is scale: how to move from experimental to essential in classrooms and museums.

For many SMEs, success with AI depends as much on relationships as on technology.
Choosing a supplier, integrating a model, or managing client data are all acts of trust. Yet too often, these relationships falter due to unclear expectations or misaligned values.

The rise of AI has brought extraordinary promise and equally public disappointment. From biased recruitment algorithms to failed chatbots and unfulfilled “AI revolution” headlines, early failures have been both costly and instructive.

AI isn’t just the domain of tech giants or research labs. Increasingly, the most creative uses of artificial intelligence are coming from microbusinesses, small teams with fewer than ten employees, who know their customers, understand their craft, and value efficiency over excess.

In recent discussions on sustainable AI, one idea has become increasingly clear: data tells stories.
Whether we’re modelling the energy use of an algorithm or reconstructing a lost building, every dataset carries a narrative of people, places, and choices.

As artificial intelligence becomes embedded in creative, commercial, and public services, the question of sustainability has moved from the margins to the mainstream. Every AI model, from large language systems to 3D reconstruction tools, relies on compute power. That power has a carbon cost.

Artificial intelligence may be digital, but its footprint is very real.
Every data centre, GPU, and training cycle consumes energy, and the global expansion of AI infrastructure is now a measurable contributor to carbon emissions. For small and medium-sized enterprises (SMEs) and cultural organisations, this raises an important question: how do you know if your AI system is sustainable?

AI’s environmental impact doesn’t end when a model is trained. Each stage: deployment, retraining, inference, and even retirement, carries energy, storage, and maintenance costs.

Artificial intelligence has become synonymous with innovation but behind every model, from generative art tools to industrial systems, lies a growing environmental cost.
Training a single large language model can consume as much electricity as hundreds of UK households use in a year, with additional demands for cooling, data transmission, and cloud storage.

AI has become a defining technology of our era, yet for many in the creative sector, it remains both opportunity and uncertainty. The current wave of data-driven AI has brought powerful new tools for automation, image generation, and 3D reconstruction. But its limitations are becoming increasingly clear.