The Rise of Explainable AI:
Why SMEs Need Transparent Tech
AI is reshaping how businesses operate, from automating workflows to transforming how we understand and interact with data. But as machine learning systems become more complex, the need for transparency becomes harder to ignore.
For small and medium-sized enterprises (SMEs), and sectors like heritage and education, the solution isn’t simply “more AI.” It’s more understandable AI.
Enter Explainable AI (XAI) – a movement that’s gaining traction as organisations seek not just powerful models, but models they can trust, interrogate, and justify.
What Is Explainable AI…and Why Does It Matter?
Explainable AI refers to systems that make their decisions and inner workings understandable to humans. In practice, this might mean:
Showing which features influenced an outcome
Visualising how a model arrived at a prediction
Making it easier to spot errors, inconsistencies, or bias
While black-box models like large neural networks can be highly accurate, they often leave users in the dark. That’s a problem, especially in sectors where decisions have financial, legal, or ethical implications.
For SMEs without in-house data science teams, the ability to understand and trust what a model is doing is not a nice-to-have, it’s a requirement.
Why Transparency Is Essential for SMEs
Unlike large corporations, SMEs often lack the resources to “trial and error” their way through AI deployment. They need systems that:
Support confident decision-making
Align with regulation
Don’t require a PhD to interpret
This is where XAI becomes a differentiator.
Transparent AI can:
✅ Accelerate internal buy-in – stakeholders are more likely to adopt a system they can understand
✅ Reduce regulatory risk – explainability supports compliance with UK and EU AI guidance
✅ Enable better support and troubleshooting – especially when integrating with existing workflows
In short, XAI makes AI more usable, more scalable, and more valuable for the businesses that need it most.
Heritage and Education: Trust and Accountability First
In sectors like cultural heritage, education, and public engagement, transparency is even more critical. These are domains where:
Provenance matters
Bias can cause reputational damage
Trust is part of the mission
If a heritage organisation is using AI to identify artefacts or restore digital archives, they need to be confident that the technology is not just accurate, but explainable to curators, stakeholders, and the public.
Similarly, in education, black-box recommendations are of little value if students, teachers, or institutions can’t understand or justify how they were made. Explainability empowers these sectors to use AI ethically and effectively, and to defend its use when challenged.
Explainability Supports Innovation, Not Limits It
There’s a common misconception that XAI is a trade-off, that to get transparency, you must sacrifice performance.
The rise of tools like SHAP, LIME, saliency maps, and inherently interpretable models (like decision trees or attention-based architectures) shows that it’s possible to design systems that are both powerful and accountable.
At Aralia, we have been using saliency maps as part of our image processing techniques for the last decade. Object saliency was identified as an important aspect of animal vision processing long before AI was used. The interpretation of saliency maps, which provide a direct indication of which data has the greatest influence on outcomes, is also subject to ethical interpretation.
For us, it’s not just about building smarter systems, it’s about building systems people can trust, deploy, and defend.
Looking Ahead
As regulatory frameworks evolve, especially under the UK AI Regulation White Paper and the EU AI Act, explainability won’t just be a bonus feature. It will become a baseline expectation.
For SMEs and public-sector organisations, this represents both a challenge and an opportunity.
The challenge is ensuring your AI tools can stand up to scrutiny.
The opportunity is to lead with confidence, showing your stakeholders that you’re using AI responsibly, transparently, and in ways they can understand.
Final Thoughts
Explainable AI is more than a technical trend. It’s a shift toward building systems that earn trust, not just output results. For SMEs, educators, and cultural organisations, that trust will be the foundation of successful adoption and long-term impact.
We believe the future of AI isn’t just about what the machine can do. It’s about what humans can do with confidence, clarity, and control.
