A re you part of the ‘shadow’ AI economy? This does not necessarily mean using AI for illegal or immoral purposes; rather, it refers to the ‘unofficial’ role that AI plays in the workplace – and it’s an increasingly widespread phenomenon. A sweeping report from the Massachusetts Institute of Technology (MIT)’s project NANDA, State of AI in Business 2025 , finds that workers at over 90 per cent of companies use personal chatbot accounts for daily tasks. Meanwhile, only around 40 per cent of firms have bought official subscriptions for paid AI tools. This discrepancy suggests AI’s role in business already extends well beyond the official uses sanctioned by managers and IT teams. C-suite discussions must now turn to considering how leaders should engage with this invisible AI use and decide if it should be encouraged, as well as how tightly it should be monitored. Such issues are especially pertinent in the context of a sharp genAI ‘divide’. Despite companies pouring millions of dollars into genAI initiatives, the MIT report reveals that only five per cent of organisations have experienced a meaningful return on investment. Why is this important? Because enterprise-level AI solutions deliver robust, scalable and secure support designed to meet a specific organisation’s needs. McKinsey reports that high AI performers – around six per cent of the respondents – claim AI has helped their organisations redesign workflows, boost innovation and scale faster. The incentives for businesses to engage with AI are clear. The question of why firms should embrace AI adoption was answered several years ago. However, the challenge of how they should achieve this remains on the table; this is where training in AI skills proves vital. Training in the era of AI assistance Ensuring abundant access to training is essential at multiple levels: personal, organisational, national and international. Equipping individuals with AI skills empowers them to complete tasks more efficiently and evaluate large data spreads in their decision‑making at a faster pace. Increasing workforce productivity and creativity translates into greater resilience and agility. With AI-empowered workers, the time it takes to conduct market research and launch new product or service lines can be condensed, opening new revenue streams faster than key competitors. For instance, the Centre for Business and Industry
GENERATIVE AI TRAINING
Transformation (CBIT) at Nottingham Business School engages in research and consultancy projects to support AI-enabled transformations in businesses and industries. One of the companies we work with large brands such as Philips and Braun, so smaller or younger brands must have efficient processes for analysing competitors’ products and consumer demand to identify opportunities where they can get a foothold in the market. Previously, this meant assigning someone to manually read reviews and analyse the findings. In the case of this firm, it took roughly three days per week plus a further two days to generate the intel for competitor analysis, with about 70 per cent specialises in grooming and personal care products. Much of this field is dominated by accuracy. This enabled the company to launch one new product every month. But following support from CBIT, the company was able to integrate AI to harvest and distil the data in a matter of hours, rather than days, generating analysis with roughly 85 per cent accuracy. The end result was a massive step up in terms of production, with the company launching 14 new products in a single quarter and generating millions in sales with a team of fewer than 10 people. Now each member of staff operates with multiple specialised AI agents to support research, analysis and product decisions, further compressing cycle times without expanding headcount. Now consider how this kind of agility and resilience proliferates through national economies and international supply chains. Business resilience improves overall stability across industries and the capacity for innovation raises the potential for transitions toward more sustainable business models and practices. For instance, another project CBIT is involved in develops AI solutions for beef farming. AI tools provide end-to-end visibility throughout the value chain – from farm inputs and animal health to processing, logistics and consumer demand – to identify new business models that strike the best equilibrium between profitability, environmental stability and social impact. Better data visibility supports stronger forecasting and risk management, shifting farms from an asset-heavy model to a more asset-light one and creating a more attractive option for potential financial backers. As farmers can now produce more high-quality meat from smaller herds, AI has helped improve animal quality of life, reduced greenhouse gas emissions and raised profit margins for farmers in a sector renowned for being under serious financial pressure. Observing these changes demonstrates the real-world impact of training workforces in AI skills at the executive leadership, middle manager and employee level. This will provide essential support as the business landscape transitions from AI assistance to an AI-first approach to integrating emerging technologies.
Ambition • ISSUE 3 • 2026 31
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