Embedding Non-disruptive Data and AI Governance into Agile Delivery | OLuwasholafunmi Agboola-Osho

Data & AI Governance leader who helps organisations turn governance from a control exercise into a practical business capability. Her approach focuses on three outcomes: control through Data Governance, Confidence through AI Governance, and the Capability to deploy AI safely at scale. With experience spanning banking, retail and the public sector, Aminat has worked across complex and highly regulated environments, translating regulatory expectations and governance frameworks into ways of working that people can actually use. Her expertise spans enterprise data governance, data quality, ownership and stewardship, metadata and lineage, information governance, regulatory compliance and responsible AI. As organisations accelerate data, analytics, and artificial intelligence delivery, governance is often experienced as a late-stage approval layer that slows teams down. This session challenges that pattern and presents a practical approach for embedding data and AI governance directly into agile delivery. ​The session shows how ownership, data quality, privacy, security, lineage, transparency, and responsible AI controls can be built into discovery, backlog refinement, sprint planning, design decisions, release readiness, and continuous improvement. The aim is to make governance proportionate, decision-led and easy to adopt. ​Attendees will learn how to distinguish what AI can do from what an organisation should do, how to keep human accountability visible, and how to measure governance by business outcomes rather than governance activity. The result is trusted innovation: teams move quickly because standards, decision rights and evidence are clear from the start. ​What the session covers ​Why governance becomes disruptive when it is added after delivery decisions have already been made. ​How to embed governance at the point of decision through agile ceremonies and product workflows. ​A four-pillar model covering ownership, quality, control, and delivery. ​How to separate technical capability from ethical judgement through the Can, Should, Control lens. ​How to measure value through customer impact, delivery pace, control confidence and adoption. ​Audience takeaways ​A repeatable model for making data and AI governance practical, proportionate and delivery-aligned. ​Practical prompts for identifying fairness, transparency, accountability and data-quality risks early. ​A 90-day starting approach for proving value before scaling governance more widely. ​Clear language for positioning governance as an accelerator rather than a bureaucratic layer.