Solstice Agriculture: Enterprise AI Policy and Governance Advisory

The challenge

Following the success of its precision analytics platform, Solstice Agriculture was rapidly expanding its use of AI across operations — from yield forecasting and irrigation optimisation to supply chain planning and market pricing models. Each initiative was being run independently by different teams, with no overarching framework for how AI tools should be evaluated, deployed, monitored, or retired. There were no consistent standards for data quality, no clear accountability when a model's recommendations were followed, and no process for assessing whether a new AI application was appropriate in the first place.

The governance gap was becoming a business risk. A yield forecast model had recently produced anomalous recommendations for a set of paddocks due to a sensor data quality issue that went undetected for three weeks. The error was caught by an experienced agronomist, but leadership recognised that as the organisation's reliance on AI grew, informal quality checks wouldn't scale. They needed a formal governance structure before a more consequential failure occurred.

What we built

A comprehensive AI governance framework tailored to Solstice's operational context, covering policy, process, and organisational accountability:

• AI use policy — a plain-language policy document defining when AI tools are appropriate, what level of human oversight is required for different decision types, and how new AI applications must be proposed, reviewed, and approved before deployment
• Model lifecycle management — standardised processes for model development, validation, deployment, monitoring, and retirement, including performance thresholds that trigger automatic review and defined escalation paths when models behave unexpectedly
• Data quality standards — minimum requirements for data completeness, freshness, and accuracy across all inputs to AI systems, with automated monitoring dashboards that flag when incoming data falls below acceptable thresholds
• Accountability framework — clear role definitions for who is responsible for AI-informed decisions at each level, ensuring that AI recommendations are always subject to human judgement and that accountability sits with people, not algorithms
• Stakeholder communication guidelines — templates and protocols for communicating AI-related decisions to external stakeholders including investors, regulators, and supply chain partners, building trust through transparency

Technology

Advisory engagement delivering policy documents, process frameworks, and organisational design recommendations. Automated data quality monitoring dashboards integrated with existing sensor and analytics infrastructure. No new AI models were deployed — the output was a governance framework, monitoring tooling, and organisational capability owned entirely by Solstice Agriculture.