Guidance for AI proof of concept to scale: scenarios

A cross-agency taskforce was established to drive improvements in government services, identifying AI technologies as an opportunity for real-time monitoring and feedback, such as triaging email responses and analysing social media sentiment. To ensure the solution’s feasibility, multiple PoC projects were conducted, rigorously testing large language models (LLMs), data integration approaches, infrastructure and security requirements. Executive sponsors from across government were engaged from the outset, with transparent planning and upfront approval of expert involvement and time commitments.

Insights gained from the PoCs directly shaped the solution’s requirements, design and procurement strategy. Once validated, the solution was developed and piloted with end-user business groups to refine its effectiveness. In production, the system incorporated robust evaluation and monitoring frameworks, seamlessly integrating with enterprise reporting systems. This enabled sponsors and operations teams to proactively identify and address issues, ensuring the solution’s ongoing success and responsiveness.

Strategic collaboration, PoC with clear objectives and transparent planning with early engagement of executive sponsors were key facets for the cross-agency AI initiative. Real-world validation, robust evaluation and monitoring frameworks, integrated with enterprise reporting systems, ensured the solution was both technically sound and operationally effective.

What worked well

• Clear objectives and cross‑agency buy‑in enabled shared ownership of the problem and its solution.
• Multiple PoCs were used to test LLMs, data integration, infrastructure and security before committing to scale.
• Early engagement with executive sponsors secured consistent endorsement and resourcing across agencies.
• Integration with enterprise reporting systems allowed real‑time monitoring, issue identification and proactive adjustment.
• Evaluation and monitoring frameworks ensured the final system was technically sound and operationally viable.

Lessons learned

• Cross‑agency coordination requires up‑front planning of roles, responsibilities and resourcing commitments.
• Delays can arise when agencies operate at different levels of digital maturity or have different risk appetites.
• Shared infrastructure and governance models must be agreed early, otherwise integration challenges multiply.