European businesses cannot quantify the impact of AI on their staff because they are not tracking it

“Businesses in Britain, France and Germany are embracing artificial intelligence. But most struggle to measure its impact properly. Ellie Smith explains how while many firms track business impact of using new technology, they are less able to explain the impact on their staff. Most organisations cannot quantify the impact of artificial intelligence (AI) because they are not measuring it in a meaningful or holistic way. New research from Catalyst and Coqual shows that while AI adoption across Europe is accelerating, measurement is not keeping pace. The vast majority of business leaders (95 per cent) say their organisations are not tracking AI’s impact at all, leaving organisations with little visibility into whether AI is delivering value, and for whom. Even among the minority that do track outcomes, measurement is narrowly focused. Leaders prioritise business performance indicators over people outcomes. This creates a structural blind spot at precisely the moment when AI is reshaping how work is done. Emilia Yu of Coqual argues that trying to work out how to make the most of investments in AI is leaving many leaders caught between urgency and uncertainty. Organisations cannot afford to “wait and see” and employees cannot remain in limbo as AI continues to reshape their roles. The measurement gap This tension is reflected in what organisations choose to measure. Where AI impact is tracked, organisations are far more likely to focus on system-level outcomes, such as innovation, risk management and decision-making, than on employee-level outcomes like engagement, retention or career progression. Across Europe, innovation is the most commonly tracked metric (52 per cent), with people outcomes tracked much less. This reflects two overlapping challenges. First, many organisations struggle to define meaningful metrics for AI’s human impact. Second, people outcomes are often treated as secondary to traditional calculations of return on investment (ROI), despite growing evidence that workforce trust and capability determine whether AI investments succeed. This imbalance is not evenly distributed. In France and Germany, regulatory and cultural constraints around people data may further limit what organisations feel able, or permitted, to measure. What leaders are tracking, and what they are not Among organisations that do measure AI outcomes, business metrics dominate. Just over half track innovation (52 per cent overall), making it the most common indicator of impact. This varies by market, from 49 per cent in Germany and 43 per cent in France to 60 per cent in Britain. Other system‑level outcomes are also prioritised, particularly in Britain. Risk management is tracked by 52 per cent of organisations, compared with 37 per cent in France and 36 per cent in Germany. Improved decision‑making is tracked by 48 per cent overall, rising to 55 per cent in Britain. By contrast, people outcomes remain underexamined. Fewer than four in ten organisations track employee engagement linked to AI (38 per cent overall), with variation across markets (30 per cent in France, 49 per cent in Britain and 32 per cent in Germany). Measurement drops further for longer‑term workforce outcomes. Only 31 per cent track retention overall (37 per cent in Germany, 31 per cent in France and 28 per cent in Britain). Career progression, one of the most consequential long‑term indicators of AI’s impact on opportunity, is among the least measured outcomes, tracked by 27 per cent of organisations in Germany, 28 per cent in France, and 40 per cent in Britain. British businesses are ahead, but there are limits Britain stands out as the most measurement‑mature market in the study. Organisations are more likely to track AI impact across both business and people outcomes, suggesting a greater willingness to experiment with broader evaluation frameworks. A key strength is segmentation. Two‑thirds of British organisations (66 per cent) track AI outcomes across multiple employee groups, with a further 27 per cent doing so for some segments. Yet important gaps remain. Even in more mature markets, organisations are still at an early stage in understanding how AI reshapes employees’ day‑to‑day experience of work and in building systems capable of capturing those effects. The missing question Most organisations can explain what AI is doing for productivity, efficiency or innovation. Far fewer can answer a fundamental question of how AI is changing employees’ lived experience of work. Shifting the question from “ Is it working?” to “For whom is it working and at what cost?” reframes AI measurement as a leadership and governance issue, not just a technical one. What business leaders should be looking for To move beyond surface‑level measurement, organisations need to assess whether they are capturing both ROI and people outcomes. They should consider these four factors: tracking people outcomes alongside technical and financial KPIs; building feedback loops that reflect how AI is experienced in practice; monitoring signals such as workload, confidence in decision‑making, trust in systems and perceptions of fairness; and identifying gaps in current measurement approaches. Why this matters Without tracking people outcomes, organisations risk overestimating the success of AI initiatives, missing early warning signs of disengagement or attrition and embedding systems that optimise performance at the expense of workforce trust and long-term sustainability. Organisations may believe AI is delivering value, while lacking the data needed to understand its full impact. This article gives the views of the author, not the position of LSE Business Review or the London School of Economics. You are agreeing with our comment policy when you leave a comment. Image credit: Frame Stock Footage provided by Shutterstock. The post European businesses cannot quantify the impact of AI on their staff because they are not tracking it first appeared on LSE Business Review .

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