How to prove the value of AI in global mobility
A K2 Thinking Paper, November 2026
AI has moved quickly from experimentation into operational use. Across global mobility and HR, organisations are using generative AI, predictive analytics and agentic systems to support immigration, relocation, policy management, cost forecasting and employee communications. Yet one question is becoming harder to ignore: is any of this actually delivering value?
The evidence suggests that adoption is running well ahead of proof. McKinsey & Company's, The State of AI in 2026 found that 80% of respondents said AI had improved their individual productivity, but only 37% attributed any EBIT impact to it. The IBM Institute for Business Value's 2025 CEO Study reported that only 25% of AI initiatives had delivered their expected return on investment. As Gartner's Alicia Mullery put it in October 2026, "volume is not value".
For global mobility leaders, this is a real challenge. An AI assistant may answer thousands of questions or cut the time spent drafting documents. These results sound positive, but on their own they don't prove that an organisation has saved money, reduced risk, improved the employee experience or made better workforce decisions.
Our latest thinking paper, How to prove the value of AI in global mobility, tackles this challenge head-on. It draws on research from McKinsey, IBM, EY, Gartner, Deloitte, the World Economic Forum and the International Labour Organization. It explores why so many AI initiatives get stuck at the activity stage, and what it takes to move them all the way up the value chain.
Inside, you'll find:
- Why AI maturity is ultimately constrained by data maturity, and why only 51% of mobility teams trust their data enough to move to the next phase of AI adoption
- Four pillars for measuring AI value: efficiency, risk reduction, strategic value and trustworthiness
- Why time saved isn't automatically money saved, and why trust should never be expressed as a single, universal metric
- The role of the "humans in the loop", and why the competitive advantage lies in what people do with the technology, not in the technology itself
- A practical six-stage framework for proving value, from setting the objective and the baseline through to A/B testing and calculating net value
The paper doesn't argue for or against AI. It argues for discipline. Clear objectives, robust baselines, rigorously tested hypotheses and an honest calculation of net value are what separate experimentation from transformation.
If you're responsible for a mobility programme, an HR technology budget or a business case that has to stand up to scrutiny, this paper will help you ask better questions of every AI initiative, whether it's built in-house or provided by a supplier.
Download the full thinking paper
