DataIQ Congress is where enterprise data and AI leaders will gather to tackle the industry’s toughest challenge: scaling AI beyond pilots into real, measurable business impact.
Designed for CDOs, CAIOs and senior decision-makers, this high-level, peer-driven forum focuses on what it truly takes to operationalise AI across the enterprise — from strategy and governance to execution and adoption.
If you’re responsible for turning AI ambition into enterprise-wide value, this is where you’ll gain the insight, connections, and clarity to lead.
Over the past decade, data and AI leaders have built the foundations. Platforms have been modernised. Governance frameworks defined. Federated models established. Use cases deployed across the organisation.
But capability is no longer the differentiator.
AI is now embedded in operational decisions, customer interactions, financial models and risk processes. Investment is material. Regulatory scrutiny is real. Board attention is sustained. The question facing enterprise leaders has shifted from “How do we build AI capability?” to “What is our mandate in an AI-driven organisation?”
As we return for the 13th year to London this Autumn, the DataIQ Congress will bring together senior data and AI leaders who are shaping how their organisations allocate capital to AI, distribute accountability across domains, embed governance without friction and scale with confidence.
The focus is not experimentation, but institutional design: how organisations scale with clarity and confidence.
For leaders navigating blurred accountability, board-level expectations and the pressure to expand responsibly, this is a forum built around your reality, and the mandate you now carry.
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Explore the core themes shaping the 2026 DataIQ Congress, designed to guide senior data and AI leaders through the key challenges of scaling AI across the enterprise.
Commercial Ownership focuses on taking accountability for value beyond the pilot phase:
Structural Clarity focuses on designing operating models for enterprise AI:
Governance as Infrastructure focuses on embedding assurance into enterprise AI without slowing progress:
Adoption as Institutional Change focuses on moving beyond use case delivery to true behavioural integration:
