There’s something oddly predictable about how society reacts to new technology. First come the promises – of productivity, profit, and possibility. Then the panic sets in. Artificial Intelligence (AI), and especially large language models (LLMs), have become the latest battleground for these cultural extremes. One moment we’re told that AI will solve our hardest problems. The next, that it will destroy everything from creativity to employment. Somewhere between these poles lies a quieter truth.
AI is just a tool. A powerful one, yes – but a tool nonetheless. Like an axe, it can clear paths or cut corners. It can build, but it can also wound. Whether it helps or harms depends entirely on how it is wielded – by whom, and for what purpose.
Business is already changing
In practical terms, AI has already reshaped large parts of business life. Some of its most useful applications are actually quite mundane: automating reports, transcribing meetings, summarising case files, or flagging anomalies in large data sets. These aren’t glamorous innovations – but they free up time, eliminate duplication, and help professionals focus on work that requires human judgement.
The real power of AI lies in decision support. Financial institutions now use it to model credit risk and detect fraud. Retailers use it to forecast demand and optimise inventory. HR teams use it to pre-screen CVs or monitor engagement. In each case, the system is not replacing a human – it’s helping them work more effectively by surfacing patterns or insights they may not otherwise have had the time to see.
For SMEs and professional service providers, AI can be an enormous leveller. Many are now using open-source or subscription-based AI tools to do work that previously required a dedicated data science team. That is democratisation at scale – but it is not without consequence.
Privacy and data – a growing regulatory storm
Perhaps the most immediate risk of AI in business is not financial, but legal. In 2025, regulators are paying very close attention to what data AI models use, and what rights individuals have when those systems are deployed in real-world settings.
The EU’s AI Act – now in effect – creates a tiered regulatory regime. It imposes strict obligations on “high-risk” uses of AI: employment, biometric surveillance, credit scoring, and law enforcement among them. In the UK, the ICO continues to hold that AI systems must not undermine individuals’ rights under the UK GDPR, regardless of whether the AI was developed internally or by a third-party vendor.
Privacy-by-design is no longer an aspiration. It is a requirement. Businesses deploying AI internally – whether through chatbots, analytics platforms or content tools – must understand what data is being processed, whether it is subject to special category protections, and what risks it may create if things go wrong. Crucially, they also need to know what happens when that system fails, and who is accountable for the fallout.
Stanford’s most recent AI Index Report recorded a sharp uptick in AI-related data incidents – up 56% on the previous year. These include everything from inadvertent leaks of sensitive training data to models generating personally identifiable information by accident. Many of these events were not malicious – but they were avoidable.
Good governance matters more than ever.
Copyright and the creative squeeze
Alongside privacy, one of the most contested issues in AI is copyright. At its heart lies a deceptively simple question: what material was used to train the model?
In many cases, developers have trained generative AI tools on vast corpora of publicly available material – including copyrighted works, scraped at scale, without permission or payment. That has prompted anger from authors, musicians, filmmakers, and designers who believe their work is being repurposed without recognition.
The British Film Institute recently warned that more than 130,000 screenplays may have been absorbed by AI systems, posing a “direct threat” to the UK’s screen industry. In the US, courts have so far been lenient on the question of “fair use” – but public sentiment is shifting, and legal risk remains live for both developers and users.
Businesses using generative AI need to tread carefully. If your marketing team relies on AI to draft blog posts, generate images, or summarise articles, you must ask: who owns the output? Was it trained on infringing content? Will the vendor indemnify you against any IP claims?
If you can’t answer those questions, you may be exposing your organisation to unnecessary risk. Open-source models with known data provenance, or tools with contractual guarantees around copyright safety, are rapidly becoming best practice.
The mirror metaphor – and why it matters
Large language models – like GPT-4, Claude, or Llama – are often misunderstood. They don’t think. They don’t reason. They reflect.
LLMs are trained on huge volumes of text from the internet, books, forums, transcripts, and more. They learn patterns, probabilities and associations. They learn what tends to follow what. In that sense, they are a mirror – not of logic, but of language. And more often than not, they reflect the mainstream, the dominant, the repeated.
This raises a profound issue. If these systems are shaped by the data they ingest, and that data reflects existing social biases, assumptions, and gaps, then the model will inevitably reinforce those same tendencies. That’s not a design flaw – it’s the nature of statistical learning.
Populist ideas appear more frequently in public discourse. They are louder, more widely repeated, and more likely to dominate the training corpus. So when you ask an LLM for a perspective on a topic, it may well give you what most people have written – not what is necessarily best reasoned or most accurate. That is not inherently dangerous – but it is easily misunderstood.
LLMs do not provide truth. They provide likelihoods. And it is precisely because of this that critical engagement matters. These tools become far more powerful in the hands of people who know when to trust, when to question, and when to double-check.
To return to the axe: a mirror may help you see yourself. But it cannot correct your stance. That requires awareness – and practice.
Bias, accountability, and public trust
One of the greatest risks facing AI adoption is not data leakage or copyright litigation – it is public trust. People want to know that the systems making decisions about them – in employment, healthcare, housing or finance – are fair, explainable, and accountable.
Bias in AI is not hypothetical. It has already been found in recruitment tools, predictive policing systems, and credit scoring models. The problem is not that the technology is malicious, but that it learns from data shaped by human history – history which includes exclusion, discrimination and oversight.
In the UK, equality law continues to apply to AI systems. The burden of proof still rests with the organisation using the tool, not the developer. In other words, saying “the algorithm did it” won’t wash in court – nor should it.
That’s why the emerging best practice is clear: keep a human in the loop. Use explainable models. Conduct regular audits. Train staff to understand the tools they’re using, and build internal challenge into your decision-making processes.
Final thoughts – and a call to leadership
There is no going back. AI is already here, and it is already changing the way businesses operate. But how we adopt it – and whether we do so responsibly – remains entirely within our control.
This is not a question for technologists alone. Boards, managers, and advisors all have a role to play. Good AI adoption is not just a technical problem – it is a leadership discipline.
If we treat AI like a shortcut, it will disappoint us. If we treat it like a black box, it will outpace us. But if we treat it like a tool – powerful, flexible, and sometimes dangerous – we stand a better chance of using it well.
We don’t ban axes. We train woodcutters. The same logic must now apply to AI.
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