CASE STUDY · PUBLIC SECTOR
An AI Literacy programme that trained hundreds of officials and seeded the first wave of bottom-up automation.
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A central administration body committed to broad AI education for its workforce. The aim was to prepare officials for AI-augmented work across policy, operations and citizen service, without delivering the kind of awareness programme that wears off in a quarter.
AI literacy among officials was low and unevenly distributed. Worries about job loss and data security were widespread, and there was little practical sense of what AI could realistically do for daily administrative work. Generic training would have produced engagement without traction.
We designed and ran a Digital Academy programme: an integrated mix of inspirational webinars, online courses, and in-person workshops for change leaders. Content was differentiated by audience. A cadre of internal AI Ambassadors was trained to carry the work after the formal programme ended.
The programme opened with an information campaign that established the stakes and the framing. Training tracks split between decision-makers (strategy, governance, regulatory implications) and operational staff (tools, prompt engineering, day-to-day applications). The AI Ambassador group received deeper training and a mandate to support colleagues after the programme closed.
Hundreds of officials trained. Survey data showed a measurable shift from anxiety to curiosity, and willingness to experiment rose substantially. The first bottom-up automation initiatives began to emerge: small, owned, and defended by the people now trained to ship them. The Ambassador network sustained the work after delivery.
"Thanks to these trainings, I stopped fearing AI and started wondering which boring tasks it could take off my hands."
Application verification time compressed dramatically. An on-premise hybrid system for grant intake, GDPR-compliant by design.
Read CASE STUDY · IT / SOFTWAREA hands-on programme. Three months in: three new AI modules shipped to production.
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