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.

XR technology has advanced rapidly. But technical capability alone does not guarantee meaningful user engagement.

Too many XR experiences fail not because they lack sophistication, but because they lack relevance.

Designing XR that people actually use requires a shift in focus: from what is possible to what is valuable.

For years, XR has been associated with headsets, immersive, high-end devices promising fully virtual experiences.

Yet despite significant investment, adoption has remained limited.

The idea of “creative automation” has become one of the most persistent narratives in AI. The promise is seductive: tools that can generate designs, write content, and produce media with minimal human input.

AI is frequently positioned as a shortcut for creativity. Faster outputs, instant variations, endless generation, the implication is that more equals better. But in practice, creative work rarely benefits from unlimited possibility.

AI systems are increasingly used to generate historical narratives, reconstruct artefacts, and simulate environments. But as these tools shape cultural storytelling, a fundamental question emerges:

Digital heritage projects increasingly focus on reconstruction, rebuilding lost artefacts, restoring damaged architecture, or recreating vanished environments in 3D.

For much of the past decade, AI progress has been associated with scale: larger datasets, bigger models, and vast computational resources. But for many UK SMEs, that paradigm was never realistic, nor desirable.

AI sustainability is increasingly discussed in dramatic terms. Headlines warn of soaring data centre emissions and the environmental cost of training large-scale models. While these concerns are valid, they often remain abstract, focused on global trends rather than practical decisions.

For much of the past decade, AI has been framed as an automation technology. The promise was efficiency: fewer manual processes, faster decisions, reduced human intervention.

For much of the AI boom, performance was measured in decimals.

Models competed on benchmark scores, fractions of a percentage point in accuracy, reductions in error rates, marginal improvements in speed. The narrative was clear: better AI meant more precise predictions.

When AI projects fail, the explanation is often framed in technical terms. The model wasn’t accurate enough. The data was incomplete. The system didn’t scale.

For much of the last decade, progress in artificial intelligence has been framed as a race toward scale. Bigger models, more parameters, and ever-growing datasets have been presented as the inevitable path to better performance and, eventually, general intelligence.

Across the UK, organisations are experimenting with AI at an unprecedented pace. Proof-of-concepts, funded pilots, and internal trials have become commonplace, particularly among SMEs, cultural organisations, and public bodies exploring automation, data analysis, or digital engagement.

The past few years have seen AI framed as a technological gold rush. Organisations were urged to move fast, scale aggressively, and adopt ever-larger models or risk being left behind. For many SMEs, cultural organisations, and public bodies, this period was characterised by experimentation, uncertainty, and, in some cases, disappointment.

Artificial Intelligence is often framed as a purely technical challenge: more data, larger models, greater compute. Yet some of the most important questions facing AI today are not computational at all, they are philosophical.

After a turbulent period of rapid growth, consolidation, and regulatory scrutiny, AI is entering a new phase, one defined less by scale and spectacle, and more by strategy, sovereignty, and sector-specific design.

2025 was the year AI enthusiasm met reality.

Across sectors, from creative studios to local authorities to heritage bodies, organisations began deploying AI more boldly. But as adoption scaled, so did the mistakes. Some were small and recoverable.

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.