Using AI to transform case management — getting under the skin of how work actually happens before designing any solution.
Illustrative engagement — reflects our approach and capability across this type of challenge
Large government departments operate complex case management processes that have evolved over years, often in ways that bear little resemblance to the documented procedures. Staff have developed workarounds, informal prioritisation systems and knowledge that exists nowhere in writing. Any AI solution that ignores this reality will fail — not because the technology does not work, but because it was designed for a process that does not exist.
We begin by mapping how work actually happens — through structured observation, process interviews and data analysis — before recommending anything. In a case management transformation, this discovery phase typically reveals significant divergence between policy and practice, and a set of informal process optimisations that the formal model has missed entirely. Our approach draws on Lean principles to eliminate waste from the value stream before automating it, and on Prosci's ADKAR framework to build individual awareness, desire, knowledge and ability among the people whose behaviour the change depends on. We do not deploy AI into a broken process. We fix the process, then apply AI where it genuinely adds value.
"The most common cause of AI transformation failure in government is not the algorithm — it is the assumption that the documented process is the real process. It never is."
The outcomes for this type of engagement are defined at the outset and measured throughout: reduction in average case handling time, improvement in first-time accuracy, staff capability uplift and — critically — sustained adoption at 6 and 12 months. A technology deployment that is not being used six months later is not a success.