Stonegate Group partnered with UAI Labs to transform how operational labour insights were surfaced and consumed across one of the UK’s largest hospitality and pub operators, managing hundreds of venues and significant labour spend across its estate.
Stonegate already had extensive operational and labour data available within existing Power BI reporting environments. However, operational managers and regional teams rarely engaged consistently with dashboards due to time pressures, fragmented reporting experiences and the operational complexity of interpreting large volumes of data quickly enough to drive action. At the same time,
FP&A teams were spending significant time responding to repetitive operational queries that existing reporting already contained.
The business wanted to move beyond static dashboards and create a more proactive, AI-enabled operational decision-making model that could surface insights automatically, improve labour optimisation and enable faster operational responses across the organisation.
UAI Labs designed and implemented an AI-driven labour reporting and operational insight solution that augmented Stonegate’s existing reporting infrastructure with intelligent automation, proactive alerts and conversational AI capabilities.
Rather than replacing existing dashboards, the solution introduced an AI layer that transformed reporting into an operational decision-support platform capable of proactively surfacing actionable insights directly to managers.
The platform was designed to:
- Identify labour inefficiencies, staffing mismatches and forecast variances
- Deliver proactive AI-generated alerts via email and SMS
- Enable managers to ask labour and cost-related questions in natural language
- Provide contextual responses using operational and invoice data
- Reduce dependency on manual dashboard analysis and FP&A intervention
- Improve responsiveness to operational performance issues at site level
The solution also introduced a closed feedback loop, allowing operational managers to validate recommendations, provide contextual input and share updates with regional leadership teams directly through guided AI interactions.
By embedding AI-driven insights directly into operational workflows, the platform enabled managers to make faster and more informed decisions without needing to navigate multiple reporting systems or manually interpret complex data sets.
The programme delivered measurable operational benefits, including:
- Approximately 30 minutes saved per operational query through automation of repetitive FP&A analysis activities
- Reduced volume of repetitive reporting requests directed toward finance teams
- Faster rota adjustments and operational decision making through proactive alerts
- Improved visibility into labour cost drivers and staffing performance across sites
- Increased operational engagement with reporting insights through simplified AI-enabled access mechanisms
Beyond the operational gains, the initiative helped establish a more proactive and intelligent operational model across Stonegate’s finance and operations functions. Operational teams were empowered with AI-supported decision making embedded directly into day-to-day workflows, while FP&A teams were able to focus more time on higher-value analytical and strategic activities.
The programme demonstrated how AI can significantly enhance the value of existing business intelligence investments by transforming static reporting into actionable operational intelligence, creating a scalable foundation for future AI and agentic operational capabilities across the organisation.