16/09/2025
Across industries, skills shortages are becoming structural rather than cyclical. Banking, insurance, manufacturing, and retail are all facing persistent gaps in critical capabilities such as cybersecurity, compliance, engineering, data analytics, and digital operations.
As a result, CIOs are increasingly turning to AI not as a replacement for talent, but as a force multiplier. The strategic question is no longer whether AI can support skills development, but how organisations can deploy it in a way that strengthens human expertise rather than eroding it.
This article examines how AI can genuinely amplify workforce capability — and what conditions must be in place for that amplification to succeed.
The talent deficit is here to stay
Skill shortages are slowing growth in banking, insurance, manufacturing and retail – dynamic sectors that rely on an adaptable, well-trained workforce.
The talent deficit shows no signs of disappearing. Industry forecasts consistently indicate that shortages in digital and technical roles will persist well into the next decade, particularly as AI adoption accelerates demand for new competencies faster than organisations can retrain their workforce. CIOs across all four industries already say that IT and data talent gaps are a top barrier to delivering on business expectations.
Even in areas like GenAI and agentic AI where budgets are steadily growing, leaders are struggling to close the gap. Insurance CIOs say skills shortages are one of the biggest obstacles to scaling AI projects, while retail CIOs say frontline enablement is being slowed by the lack of staff who can interpret or trust AI outputs, which is why the need to multiply skills in the workplace is more important than ever.
Organisations increasingly find themselves in a tough position: on the one hand, they want to quickly adopt AI to multiply business-critical skills within their workforce. On the other, skills shortages make AI adoption less effective. Let’s look at how leaders in each industry can break the vicious cycle and actually put AI to work.
Banking: Reinforcing compliance and risk management
Banks are investing heavily in AI to strengthen compliance and risk management functions. However, translating investment into measurable value remains challenging. Many initiatives struggle not because of technology limitations, but because workflows, governance models, and employee confidence are not aligned with the tools being deployed.
This raises the question: what are those CIOs getting right that everyone else is getting wrong? For one thing, they’re focusing on the human side of AI adoption.
The most effective AI initiatives in banking don’t set out to replace humans or devalue their work. On the contrary, they position human decision-making as the gold standard. AI and machine learning can speed up fraud detection and reduce false positives, but these technologies should serve as a filter, not a judge. The real value comes when AI detects and escalates the cases worth attention, and trained staff make the final call.
Insurance: Moving beyond automation
Many insurers have already deployed AI or generative AI tools and continue to increase investment. As deployments mature, attention is shifting from automation alone to governance, explainability, and workforce readiness. For example, Aviva has deployed over 80 AI models, slashing its liability assessment time for complex cases by 23 days on average, whilst improving routing accuracy by 30%. As a result, they’ve seen a 65% reduction in customer complaints.
Automation clearly accelerates key processes, but in an industry where trust is everything, working faster doesn’t always mean working better. As use cases for AI become more mature, forward-thinking CIOs are focusing on potential downsides like bias, accuracy drift and weak explainability.
To mitigate risk, insurers must put bias checks in place, set clear rules for when humans step in and keep proper records so regulators and customers can see exactly how each decision is made. AI upskilling should also be a key focus: to achieve its impressive results with AI, Aviva invested in over 40,000 hours of employee training.
Manufacturing: Strengthening operational resilience
Manufacturers are struggling with chronic shortages in engineering and other key roles. No surprise then that 83% of Manufacturing CIOs are investing in AI, according to Gartner, in areas like product cycle optimisation, automated compliance reporting and quality monitoring.
Predictive maintenance has become a go-to use case, with major manufacturers like Agilent reporting up to a 51% reduction in downtime as a result. AI picks up on patterns people would miss, flagging faults before they cause breakdowns. For plant managers, that means fewer unexpected stoppages, which frees up crews to focus on higher-value work instead of constant troubleshooting.
While AI-driven efficiency gains are significant, manufacturers remain cautious about over-reliance. Sustaining core expertise and hands-on experience remains essential, particularly in safety-critical environments. Some are concerned that over-reliance on AI will erode core expertise over time. To make sure they always have a backup plan in place, they’re investing in multiply skills in the workplace to make sure they still get hands-on experience, even as they increase investment in AI-powered maintenance.
Retail: Building a fully AI-enabled workforce
Retail is one of the world’s most data-driven industries and has pioneered many AI use cases, from dynamic pricing and supply chain forecasting to e-commerce personalisation and market analytics. Amazon considers AI so crucial that it has launched a global project to educate 2 million people with critical, future-proof AI competencies.
Within the company, Amazon Web Services (AWS) takes a simple approach to AI upskilling:
- Everyone benefits from GenAI
- Prompt engineering is a must-have skill
- Use social media as an education platform
- No new tools without new training
While these rules sound easy enough, surprisingly few companies get them right. Gartner reports that 69% of organisations suspect or have evidence of employees using unauthorised AI tools at work – a clear sign that official AI policies and frameworks are lacking. Organisations in retail and beyond can all learn from AWS’s inclusive, employee-focused approach to AI adoption and change management.
Facing the truth
Organisations that generate measurable value from AI treat it as a capability amplifier, not a shortcut. Successful deployment requires three elements: integration into existing workflows, structured employee education, and clear governance over when and how AI supports decision-making.
When those conditions are met, AI can extend human capability — accelerating learning curves, improving decision quality, and enabling teams to operate at a higher level of performance without diminishing professional judgement.
Frequently Asked Questions (FAQ)
It means using artificial intelligence tools and systems not to replace human skills but to enhance, accelerate and scale what employees can deliver, from learning new competencies, improving decision-making, to automating repetitive tasks, so people can focus on higher-value work.
Many industries, banking, insurance, manufacturing, retail, are facing chronic shortages in critical roles such as IT, cybersecurity, data analysis and more. AI helps bridge the gap by enabling better efficiency, speeding up processes, and allowing staff to learn or apply skills in novel ways.
Risks include over-reliance on automation, decision-making without sufficient human oversight, issues with bias and accuracy, and a loss of core expertise if employees are not kept engaged or trained properly. Ensuring clear governance, human judgement, transparency and ongoing education helps mitigate these risks.
