AI is transforming how we work, but not everything labelled “AI” truly is. We answer the real questions business leaders are asking about automation, security, and the future.

Not every system that looks intelligent actually is.

Often, what’s branded as AI is really automation or pre-programmed algorithms doing repetitive work faster. That doesn’t make it useless - it just means it’s not truly thinking.

True AI can interpret, learn, and predict based on data, adapting to new situations rather than following a fixed rulebook.

“Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity.”

- Fei-Fei Li, Co-Director, Stanford Institute for Human-Centred AI

To learn more about how automation complements AI, see our related insight:

👉 Modernisation, Transformation & Automation – How each stage builds toward lasting impact.

Despite the headlines, AI can’t replace the qualities that make humans unique - creativity, empathy, and contextual understanding.

It can help us write faster, analyse better, and automate the mundane, but it can’t understand why something matters. That’s why people remain at the heart of every transformation.

When used responsibly, AI empowers teams to focus on higher-value thinking, not just faster doing.

“AI will not replace humans, but those who use AI will replace those who don’t.”

- Ginni Rometty, Former CEO of IBM

A recent MIT Sloan study found that generative AI boosted the productivity of highly skilled workers by nearly 40% - not because it replaced them, but because it amplified their expertise.

AI’s rise also brings questions about privacy, compliance, and trust. There’s currently no single “AI law” in the UK, but data protection and GDPR rules still apply.

Being “AI-compliant” isn’t just a legal checkbox, it’s a moral one. Businesses should know what data their systems are using, how it’s stored, and who has access.

Transparency builds trust, and trust is what sustains innovation.

👉 Keep an eye out on our upcoming insight on AI Governance and Data Ethics

“AI” has become an umbrella term for almost anything tech-related. It sells, but it often blurs the lines between genuine innovation and clever marketing.

The real opportunity isn’t in chasing the label, but in how it’s applied to solve meaningful problems, reducing inefficiency, unlocking insights, and helping people work smarter.

Globally, McKinsey estimates that generative AI could add between $2.6 trillion and $4.4 trillion in annual productivity value across industries.

One of the most common questions we hear is, “How does ChatGPT actually work?”

Large Language Models (LLMs) like ChatGPT, Gemini (Google), Claude (Anthropic), and DeepSeek are trained on massive datasets to recognise patterns in language and predict the next most likely word or phrase.

They don’t think, they calculate probabilities.

Unlike a search engine that retrieves existing pages, LLMs generate new text based on context and user intent.

Just as “Google it” became shorthand for search, “ChatGPT it” is fast becoming a modern phrase for ask AI for help, even if people don’t fully understand how it works.

While LLMs have captured the spotlight, AI extends far beyond language.

Other key types include:

• Machine Learning (ML): A broad category enabling systems to learn from data.
• Deep Learning: Multi-layered neural networks powering image, speech, and pattern recognition.
• Predictive AI: Analysing past data to forecast future trends or behaviours.
• Generative AI: Creating new outputs - text, images, video, or code.
• Computer Vision: Recognising objects and scenes (used in manufacturing quality control or driver-assist systems).
• Reinforcement Learning: Training AI through feedback loops to make better decisions over time.

AI can also be categorised by capability:

• Reactive machines
• Limited memory systems
• Theory-of-mind
• Self-aware AI (still theoretical)

👉 We’ll explore each of these areas in more depth in future insights.

Generative AI isn’t limited to words. Image and video generation tools like Midjourney, DALL·E, and Runway are reshaping design, marketing, and product development.

For creative industries, this means faster concepting, visualisation, and prototyping but also new challenges around copyright and authenticity.

At True9, we’ve seen how these tools can accelerate ideation when used ethically, guided by human creativity.

But we feel like this whole section deserves it’s own blog, something we’ll be creating soon!

AI isn’t a distant concept, it’s already embedded in the systems and services many industries rely on every day.

Manufacturing

Factories are using predictive maintenance powered by AI to reduce downtime and waste. Machine-learning models analyse sensor data to detect wear before it becomes a problem, improving efficiency and safety.

In some cases, production plants using AI for process control have seen output increase by over 10% within six months (McKinsey, 2024).

We’ve seen this first-hand through our work with an international stairlifts manufacturer, where IoT-driven diagnostics are transforming operational visibility and product support.

Fashion & Beauty

AI is helping brands anticipate demand and reduce overproduction through smarter forecasting and trend analysis. Computer vision supports automated quality control, while recommendation engines personalise shopping experiences based on preferences, body shape, or skin tone.

Some beauty retailers now use AI-driven virtual try-ons that match shades and styles in real time, turning digital browsing into confident purchasing.

Professional Services & Loyalty

In the professional services sector, AI is accelerating data analysis, client insights, and contract management, freeing people to focus on strategy and relationships.

For loyalty and rewards, intelligent algorithms personalise offers by analysing behaviour, timing, and sentiment. That means fewer generic promotions and more moments that genuinely engage customers, driving retention and lifetime value.

Whether it’s predicting when a part will fail, matching customers with products they’ll love, or tailoring the right reward to the right person, AI’s strength lies in context, turning data into smarter decisions that save time, reduce cost, and enhance experience.