Last week Faculty and Microsoft brought together a number of senior government leaders and digital & data specialists to share notes about what a year of generative AI implementation has taught us about successfully deploying these rapidly evolving technologies in a public service context.

The launch of ChatGPT over a year ago was a significant moment in the history of AI. The barriers to entry of using powerful AI dropped significantly. These models no longer needed an expert team to train them on one specific use case, but came ready formed to competently manage a bewildering range of general tasks, and they could be accessed through a simple chat interface and, later, an easy to use API.

Public adoption of ChatGPT surged at an unprecedented pace, surpassing 100 million users within months of its launch. The allure of its capabilities and user-friendly interface fueled a hype cycle of remarkable intensity. Despite initial concerns regarding data security, the practical utility became evident but apprehensions lingered about the responsible handling of sensitive data, prompting swift action from Government to offer guidance on secure usage. OpenAI’s connection with Microsoft and adoption into the Azure environment meant models could now be brought to the government’s existing data environments, rather than data being passed overseas.

Practical experiments within public services ensued, guided by internal government teams initiating smaller proof of concepts. And the centre of government has played a role to brigade some of these early examples and share learning.
After more than a year of collective experience with GenAI there are important lessons to be shared. While the barriers to entry of using GenAI have lowered significantly, making these tools work as desired in the real world remains hard. Without continued practical improvement the government risks falling rapidly into the ‘valley of disillusionment’.

At Faculty, our practical experience has shown that teams using GenAI need to:

We see use cases clustering into three distinct categories:

Our overarching lesson for government remains just start. Building small, careful services and then iterating and learning from this is critical to developing the skills and confidence to make the most of this new era. Public service teams need that capability to avoid taking wrong steps in a market where promotion can override expertise. And senior leaders need to help their teams maintain their ambition and momentum – there are plenty of incentives to slow down, from imperfect data, concern about existential threats, and a general fear of change. But vital efficiency savings are on offer and the stakes are too high for government to fall too far behind.

Explore how we’re improving citizens’ lives by using data to its full potential here.

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