Data and AI adoption in a national service organisation

A UK service organisation operating across multiple regions – with thousands of staff and a large contact centre operation – had invested in modern data tools and pockets of AI experimentation. Like many organisations, it found that pilots were not turning into scale and that unclear governance left business units unsure how to prioritise use cases. This reflects broader UK trends where AI has moved from experiment to operational use, but adoption is uneven and still developing.

The executive team had a set of main concerns:

• AI work was scattered across teams without a coherent view of value or risk.
• Data quality varied across regions and systems, limiting trust in outputs.
• There was no shared framework for deciding which AI and data initiatives to progress.

They wanted to move from a collection of experiments to a managed set of AI and data initiatives supporting service quality and cost performance.

Over six months, Ozkora worked with leaders from operations, digital, finance and HR. We:

• Ran a structured AI and data opportunity assessment, starting with existing initiatives and known pain points in service and operations, including workforce planning.
• Mapped the current data landscape and identified a small number of data sources that were essential for high‑value use cases, rather than trying to “clean everything at once”.
• Developed an AI and data governance framework covering ownership, risk, ethics, model monitoring and decision rights, consistent with patterns seen in leading UK firms exploring AI.
• Built a prioritised roadmap for AI and data initiatives, including a small number of lighthouse projects with named business owners and success measures.
• Supported design and early execution of two priority initiatives: predictive models for contact volumes to improve staffing decisions, and a set of decision‑support tools for field operations leaders.

Within nine months of the start of the engagement:

• The organisation reduced avoidable overtime in its contact centres by around 6–8%, driven by better forecasting and staffing decisions. This type of improvement is consistent with documented gains from AI‑supported planning in service operations.
• Service levels during peak periods improved, with fewer missed response‑time targets, as shown in internal reporting.
• A more robust governance structure for AI and data initiatives was agreed by the executive team, with explicit accountabilities for data quality and risk review and a clear owner for benefits tracking.
• A central register of AI and data initiatives was in place, giving leadership a view of where investment and experimentation were happening.
• The leadership team reported greater confidence discussing AI opportunities, because there was now a shared framework and language.

Ozkora stepped back once the organisation had the roadmap, governance and early delivery capability in place, and internal teams took ownership of the next wave of initiatives.