There’s no shortage of ambition when it comes to AI. Business leaders, government departments and industry commentators are already painting a bold picture of an AI-enabled future. But beneath the surface, a familiar pattern is emerging: the hype is running ahead of the skills.
The message is clear: AI is the future. However, AI strategies are being launched without the foundations to support them. Tools are being rolled out without clear guidance on use. And big promises are being made without the internal knowledge to deliver them responsibly.
Behind the hype lies a more complex reality, where ambition often outpaces capability.
According to a report by Accenture, while 84% of business leaders believe they must leverage AI to achieve their growth objectives, only 17% say their organisation is “fully ready” to implement and scale AI. The ambition is there. The capability often is not.
There’s growing evidence that many organisations are racing ahead with AI ambitions while struggling to implement effectively. According to IBM’s Global AI Adoption Index 2023, 40% of surveyed companies are still in the early stages of AI deployment, citing lack of skills as the number one barrier to progress. And in a report from McKinsey, just 21% of organisations said they had the in-house capabilities to fully scale AI initiatives.
This gap is not just technical. It’s strategic. Many leaders and teams still misunderstand what AI can and can’t do, how it should be deployed, and what risks need to be managed. As a result, implementation can become superficial at best, and damaging at worst.
In a 2024 survey by Deloitte, 39% of executives said a lack of internal talent is their biggest barrier to AI adoption. McKinsey has also reported that only 30% of companies that invest in AI have actually scaled it across their operations.
Even highly ambitious projects can fail without a skilled foundation. Take the case of Air Canada, which recently made headlines for having to honour a refund policy created by its own customer service chatbot. The AI tool provided inaccurate information about bereavement fares, and a small-claims tribunal ruled that the airline was liable for the chatbot’s mistake. It was a cautionary tale of what can happen when AI tools are deployed without clear governance or oversight.
In 2023, Samsung engineers unintentionally leaked sensitive company information (including source code) by inputting it into ChatGPT while using it for work. It highlighted a major skills gap in understanding how generative AI tools handle data, particularly around privacy, confidentiality, and intellectual property protection. The incident led to a temporary ban on generative AI tools at Samsung.
Meanwhile, Facebook’s content recommendation algorithms have repeatedly been shown to amplify harmful content. Whistleblower Frances Haugen revealed that Facebook knew its AI was contributing to misinformation and divisive content but failed to address the issue transparently or effectively. Again, the lack of understanding, or willingness to act on it, led to reputational damage and regulatory scrutiny.
These are not just isolated incidents. It’s a cautionary tale for any organisation trying to ‘do AI’ without first building the right internal knowledge, safeguards, and skill sets.
When organisations adopt AI without a solid foundation of internal knowledge, the results can be costly, and very public.
The urgency is growing. With the EU AI Act expected to come into force later this year, and similar initiatives gaining traction in the UK and globally, organisations must ensure their people understand how to use AI tools within legal and ethical boundaries.
The EU AI Act classifies AI systems based on risk level and imposes strict obligations on high-risk applications, including transparency, human oversight, and detailed documentation. Businesses using AI in areas like recruitment, credit scoring, or critical infrastructure will face increased scrutiny and compliance requirements. Failing to meet these could result in reputational harm, financial penalties, or operational delays
The AI hype cycle won’t last forever, but the skills gap it exposes will have long-term implications for productivity, risk, and trust. Closing this gap is no longer a nice-to-have. It’s a strategic imperative.
A familiar pattern is emerging: the hype is running ahead of the skills. AI strategies are being launched without the foundations to support them. Tools are being rolled out without clear guidance on use. And big promises are being made without the internal knowledge to deliver them responsibly.
The future of AI won’t be defined by the loudest headlines or boldest claims. It will be shaped by the organisations that invest in understanding, skills, and ethical implementation.
Organisations don’t need more AI hype. They need skills, confidence, and clarity. That’s what Decoded delivers.
This is where Decoded comes in. Our training programmes, including the AI Accelerator (delivered in partnership with the UK Government Digital Service) and our Applied AI courses, are designed to meet this moment.
We help leaders and teams understand how AI systems actually work. We bring clarity to concepts like model training, bias, data privacy, and explainability. We focus on applied learning, giving people the tools and confidence to put AI into practice in a way that is safe, scalable, and strategically aligned.
It’s about moving beyond buzzwords and into real-world capability, so that organisations can not only adopt AI, but do it well.
Click here to explore our AI for Leaders Immersions.
Sources:
Accenture, The Art of AI Maturity, 2023
Deloitte, State of AI in the Enterprise, 2024
McKinsey & Company, The State of AI in 2023
IBM, Global AI Adoption Index, 2023
CBC, Air Canada ordered to pay refund after chatbot gave wrong information, 2024
TechCrunch, Samsung bans use of ChatGPT after data leak, 2023
(Image used is AI generated.)
If you’d like to learn more about Decoded and how we can help transform your
organisation, we’d love to hear from you.
