Chris Hester’s session at Gartner HR Symposium/Xpo London 2026 brought AI ambition back to its data foundation. My central takeaway was that AI-ready HR data has to be considered in relation to a use case. Leaders need to know what decision or activity the data will support before deciding whether it is suitable. That makes readiness a practical business discussion, supported by clear ownership and sustained governance.
My notes describe data that may be poor, inaccessible or locked in silos, with consequences that become more significant as HR scales AI. Hester’s official session description similarly focuses on building trustworthy, representative and effective data for AI-driven HR solutions. The useful connection for me was between the condition of the information and the value an organisation hopes to create from it. More ambitious use of AI increases the importance of getting that connection right.
Chris Hester’s action plan moved from immediate ownership to targeted improvements and sustainable governance. Photograph from Alison’s session notes.
Asking the right questions first
The data-governance photograph organised readiness around quality and trust, ownership and accountability, and privacy and security. It asked whether the data could be trusted, who owned it and whether it could be used responsibly. These questions were linked to reliable decisions, sustainable operations and responsible deployment. The slide provided a clear way to begin a conversation that could otherwise become abstract or overly technical.
My interpretation is that each question should be answered for the intended use. A dataset that is adequate for one activity may leave important gaps for another. Leaders should therefore examine the information a proposed use case depends on, its accessibility and the responsibilities around its use. This follows the session’s emphasis on asking better questions early, before weaknesses become embedded in a larger initiative.
Starting with a defined priority
Hester’s action-plan slide was particularly useful because it set out different horizons. Its immediate actions included empowering the executive accountable for HR data readiness, agreeing which AI use case mattered most over the next year and commissioning a review of one critical supporting dataset. The examples included skills, organisational structure, reporting relationships and employee records. That made the starting point specific enough to act on.
Over the next 90 days, the slide proposed assessing readiness for one or two priority use cases, identifying high-risk gaps and clarifying ownership of critical HR data domains. It also called for a small number of improvements with high impact. I appreciated the discipline of that sequence. It creates a route from a chosen outcome to an assessment of the information needed, and then to focused work on the weaknesses that matter most.
Sustaining the foundation
The longer horizon concerned sustainable governance, data-quality monitoring within HR operations and the development of data stewardship capabilities. I read this as a reminder that readiness is maintained through the way an organisation works. A one-off improvement may help an initial use case, but people still need responsibility for the information as records, roles and requirements change.
For me, the workforce implications are substantial. Skills decisions, mobility opportunities and new reporting arrangements can all depend on information that leaders assume is current and meaningful. My reflection is that HR should make those assumptions visible before using data to support a more consequential decision. The practical benefit is a clearer discussion about where confidence is justified and where further work is needed.
As the founder of AmpliTask, I found the link between a use case and its data requirements especially relevant to work transformation. Hester and Gartner provided a grounded route into AI readiness: choose what matters, examine the foundation, assign ownership and sustain the improvements. I would begin with one valuable decision about work and ask what information it requires, who maintains that information and whether people can use it responsibly. That is a manageable first step towards making AI more useful.
Sources
Session notes and photographs by Alison, Gartner HR Symposium/Xpo London 2026.