Why UK companies aren’t seeing the payoff from their AI investments

UK firms are achieving limited returns on AI investments

UK companies are spending serious money on artificial intelligence, yet many aren’t seeing meaningful returns. QA’s latest research shows an average of £235,000 invested per company in AI and emerging technologies. Still, only 16% of employees report significant productivity improvements. The problem isn’t the technology, it’s the people using it.

Almost one-third of UK employees, about 32%, have received no formal AI training. Only 15% have access to continuous or advanced programs. Workers who lack foundational skills can only use AI for routine functions like drafting emails or summarizing documents. That’s not a productivity revolution, it’s automation at the margins. If businesses want transformational outcomes, they need to align human capabilities with technological ambition.

Decision-makers should think of AI and skills as a dual investment strategy. The return on AI doesn’t come from the tools alone but from the people who know how to apply them effectively. Without that capability bridge, even advanced AI systems quickly turn into underused assets. Building AI literacy across all levels allows companies to scale innovation faster, achieve measurable ROI, and prepare for the next stage of digital competitiveness.

AI proficiency remains uneven across organizational roles

The adoption of AI inside organizations isn’t balanced. QA’s study found that 9% of employees describe themselves as advanced or expert AI users, most of them in IT or technical roles. Staff in operations, customer service, administration, and sales typically use AI only for low-level tasks. The skills divide is clear and limits the overall business impact.

Executives need to see this unevenness for what it is: a bottleneck on performance. Advanced adoption confined to technical departments means that strategic gains, such as faster decision-making or smarter process automation, stay isolated. Frontline teams, which often have high potential for efficiency improvements, are still operating without proper AI training or confidence in the tools available to them.

Closing this gap isn’t about forcing every employee to become an AI expert. It’s about ensuring every role has access to targeted, relevant upskilling. The one-size-fits-all model doesn’t work because each department interacts with AI differently. When organizations invest in role-specific learning, they turn scattered adoption into coordinated performance. Broader proficiency across functions doesn’t just raise productivity, it embeds technological adaptability into the company culture, ensuring the benefits of AI reach every corner of the business.

Building broad-based AI literacy and providing role-specific training

Many organizations are rushing into AI adoption without ensuring their workforce understands how to use it. QA’s findings show that only 15% of employees receive continuous or advanced training. That’s a small fraction of the workforce truly capable of leveraging AI’s potential. Dr Vicky Crockett, Portfolio Director for AI at QA, makes a clear point: before pursuing large-scale AI transformation, companies need to strengthen data and AI literacy across all roles.

Foundational understanding is the starting line. Once people know the fundamentals, what AI can do, how it processes information, and where its limitations lie, they’re more confident in applying it effectively. Then, organizations should move into role-specific training. AI affects each function differently: marketing teams use it to spot patterns in consumer behavior, engineers use it for design optimization, and finance teams use it for forecasting accuracy. Training that reflects those realities enables each department to generate real value from AI systems.

For executives, this is more than a training initiative, it’s a strategic enabler of scalable productivity. Companies that integrate ongoing learning into their operations will adapt faster and extract greater value from AI over time. The message is simple: real transformation happens when every employee, not just the technical elite, is equipped to work intelligently with technology.

Treating AI adoption solely as a technology rollout undermines its potential as a transformative workforce strategy

Many businesses still see AI implementation as a technology exercise rather than a workforce evolution. That mindset limits results. Jo Bishenden, Chief Learning Officer at QA, explains that “AI is being adopted at pace, but too many organisations are still treating it as a technology rollout rather than a shift in people capability.” Her perspective reflects a growing reality, AI’s effectiveness depends less on software deployment and more on how employees use it.

When AI programs are managed solely by IT departments, their reach remains narrow. The outcome is a set of advanced systems with minimal day-to-day impact. To change that, companies need to embed AI capability building into their culture. Employees should not just know how to operate an AI tool; they should understand how to make informed decisions supported by AI insights. That’s where judgment, context, and confidence come into play.

For leadership teams, the implication is clear. Investing in people must go hand in hand with adopting technology. The organizations seeing the strongest results are those scaling training across their workforce while embedding AI into everyday processes. Once employees are empowered to apply AI thoughtfully and strategically, productivity gains expand from isolated teams to the entire business, turning adoption into measurable, sustained advantage.

The disconnect between widespread AI access and low-impact usage

Many companies are introducing generative AI tools across their operations, but actual impact remains low. QA’s research confirms that while a growing number of employees have access to AI systems, most use them only for simple, repetitive tasks. Only about 9% consider themselves advanced or expert users. This limits potential returns, leaving much of the investment untapped.

The issue isn’t access, it’s application. Employees know these tools exist but often lack the training and support to use them meaningfully. Around one in ten employees believe they could achieve more with AI if they had better guidance. This shows a persistent capability gap between the availability of technology and the level of skill required to make it productive.

For executives, this is a signal to reassess how AI budgets are allocated. Focusing solely on platform deployment overlooks the human side of adoption. High-impact outcomes come when teams know how to integrate AI into core workflows, beyond drafting, summarizing, or formatting. Businesses that connect capability building with technology integration can measure stronger ROI, faster decision-making, and consistent operational improvements.

Increasing proficiency across roles also ensures sustainability. As organizations expand their use of generative and agentic AI models, the demand for applied knowledge will only grow. Long-term value will depend on whether leaders turn current access into practical expertise, making AI not just present in the workplace, but truly productive.

Key takeaways for decision-makers

• AI investments need workforce alignment: UK firms are investing heavily in AI but seeing modest gains. Leaders should match technology spending with structured training programs to turn adoption into measurable ROI.
• Close the skills divide across roles: Advanced AI use is concentrated in IT while other departments lag. Executives should invest in targeted training across non-technical teams to unlock companywide efficiency and innovation.
• Prioritize literacy and role-specific learning: Strong AI foundations and tailored training drive stronger adoption. Leadership should make AI literacy a core competency across all levels to boost confidence and utilization.
• Treat AI as a people strategy: Viewing AI adoption primarily as technology deployment limits its impact. Leaders must embed capability-building into culture, ensuring employees can apply AI effectively in daily work.
• Link AI access to productivity outcomes: Access alone doesn’t create returns, applied skill does. Decision-makers should connect training, adoption, and performance metrics to turn AI availability into sustained business value.