In September 2026, Reuters reported that an International Monetary Fund (IMF) paper presented to European finance ministers estimated that artificial intelligence could raise European productivity by about 1% over five years.

The estimate is meaningful but modest enough to challenge unrealistic expectations. In a speech accompanying the analysis, IMF Managing Director Kristalina Georgieva argued that productivity gains depend on organisations redesigning products, processes and workflows rather than simply adding technology to existing work. The IMF also warned that uneven adoption could widen differences between countries and workers.

Insurance and employee benefits provide a useful test case. Both sectors process large volumes of documents, rules and recurring decisions, which creates obvious scope for automation. Yet they are also regulated, data-intensive and dependent on professional judgement. Gains will therefore come from changing how work is organised while preserving accountability.

The Productivity Opportunity Is Operational

Insurers can use AI to extract policy information, summarise claims files, identify missing data and support underwriting analysis. Brokers and global benefits networks can automate market comparisons, local-policy checks, renewal preparation and multinational reporting. These applications can shorten cycle times and give specialists more capacity for negotiation, exception handling and client advice.

The largest gains are unlikely to arise from isolated assistants. They require end-to-end workflows in which data enter once, move through controlled stages and trigger defined human decisions. A multinational benefits placement, for example, could connect census data, local market requirements, quotations, policy documents and renewal outcomes in one traceable process. AI would support each step, but ownership and approval would remain explicit.

The IMF notes that European companies tend to apply AI more narrowly than US firms, which are more likely to redesign entire business processes. That difference is particularly relevant to established insurance organisations. Legacy systems, fragmented data and national operating models can turn a promising pilot into another layer of complexity unless management removes redundant steps.

The Constraints Are Strategic as Well as Technical

The IMF estimates that up to 60% of jobs in advanced economies are exposed to AI and warns of pressure on middle-skilled roles. Insurance employers should distinguish task automation from job elimination. Roles will change unevenly: routine preparation may decline, while data stewardship, model oversight, relationship management and complex judgement become more important. Training needs to be linked to redesigned work rather than offered as a generic AI course.

Energy and infrastructure are another constraint. Data centres already account for about 3% of European electricity consumption, according to the IMF, and AI-related demand could triple by 2030. Cost, capacity and carbon intensity will influence where regulated firms deploy models and how they select providers.

Europe also depends heavily on non-European model and cloud providers. Insurers should therefore build portable architectures, maintain control over proprietary data and define exit plans. Sovereignty does not require every company to develop its own model, but it does require credible alternatives and contractual protection against service, pricing or geopolitical disruption.

Measurement will determine whether redesign produces real productivity. Each use case should have a baseline for processing time, error rates, cost and service quality. Management should also track rework and exceptions, since faster first-stage output can create more downstream correction. Benefits need to be measured after the cost of technology, controls, training and human review.

Boards should expect a portfolio view rather than a list of pilots. That view should identify which workflows are in production, what value has been realised, which risks have increased and where implementation is stalled. It should also show whether gains are shared through better service, lower cost or more valuable work, not simply absorbed as additional output targets.

The IMF’s 1% estimate should be treated as a management challenge rather than a forecast that will arrive automatically. Insurance organisations will capture their share only by selecting measurable workflows, redesigning them, training the people who own them and governing the technology throughout the process.

Also, a 1% productivity improvement means that European employees are not looking at massive reductions in force, at least not in the next five years. Welcome news (or temporary relief?) for employee benefits providers in Europe.