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.

Until recently, XR and 3D tools were the preserve of large organisations with the budgets and technical teams to match. High-end VR headsets, LiDAR scanners, and custom-built applications priced out smaller players, leaving schools, councils, and SMEs to watch from the sidelines.

AI tools promise insights, predictions, and recommendations. But for SMEs and cultural organisations, the real challenge is not generating outputs, it’s turning them into actionable decisions. A dashboard full of metrics is only useful if decision-makers know what those numbers mean and how to act on them.

Much of the public debate around AI in the creative industries frames it as a threat: machines competing with humans, replacing jobs, and eroding originality. But this narrative overlooks the many artists who are already using AI not as a rival, but as a collaborative partner.

The EU’s AI Act, agreed in 2024 and now moving into implementation, is the world’s first comprehensive framework for regulating artificial intelligence. Alongside it, the UK has taken a lighter, sector-led approach, with guidance framed by principles rather than prescriptive rules. For SMEs operating in or trading with Europe, these policy shifts may feel abstract, yet their impact will be very real.

Extended Reality (XR) is often associated with big-budget museums, immersive exhibitions, or tourism experiences designed to attract visitors. Yet its potential stretches far beyond those spaces. XR can be a tool for communities, helping residents engage with local issues, shape shared spaces, and preserve cultural memory in ways that feel personal and participatory.

For SMEs, AI feels both like an opportunity and a puzzle. Larger organisations can afford data science teams and enterprise AI platforms, but most small businesses are working with limited budgets and generalist staff. So how do you build AI capability when you don’t have deep pockets or a dedicated R&D department?

For many heritage organisations, cloud platforms offer an appealing solution. They provide scalable storage, global accessibility, and integration with powerful AI and XR tools. Vast datasets, from digitised manuscripts to 3D scans, can be made available to researchers, educators, and the public with unprecedented reach.

Public sector organisations, from councils and healthcare trusts to libraries, museums, and heritage bodies, face increasing pressure to adopt AI. The promise is clear: efficiency, better services, and new ways to engage communities. But choosing the right supplier is not just a technical decision; it’s an ethical and strategic one that directly impacts public trust.

When disaster strikes, time is everything. Earthquakes, floods, and fires leave emergency services with an urgent need: rapid, reliable information about what lies ahead. In those first critical hours, decisions must be made with imperfect knowledge, often putting responders and communities at risk.

AI is no longer confined to labs or tech companies. It shapes everyday experiences, from healthcare to education to the way we consume culture. Yet public understanding of how AI works, and what its limitations are, still lags far behind adoption.

The arts have always been quick to experiment with new technologies. From generative music and AI-assisted visual art to interactive installations, creative projects often push boundaries before businesses catch up. For small and medium-sized enterprises (SMEs), these experiments offer more than spectacle, they provide practical lessons in agility, adoption, and customer engagement.