How do we drive AI adoption?

🌈 Abstract

The article discusses the challenges of adopting new technologies, particularly AI, in the public sector. It draws lessons from the adoption of internet-era technologies in the public sector over the past 20+ years.

🙋 Q&A

[01] Lessons from public sector digital work

1. Questions related to the content of the section?

• The article highlights several key lessons from public sector digital transformation work over the past 20+ years:
  • Some services lend themselves well to internet-era technologies and can be transformed relatively easily, while most services require more complex changes.
  • Documenting and distilling good practices, such as through design principles and standards, can help drive adoption.
  • Building centralized infrastructure and capabilities, like design systems, can enable consistent and effective use of new technologies across government.
  • Empowering and supporting digital leaders at the mid-level of government can help spread adoption through pockets of innovation.
  • Identifying and developing the necessary skills and capabilities, both technical and non-technical, is crucial for successful adoption.
  • Deeply understanding user needs and co-designing solutions with frontline professionals can make new technologies more useful and adoptable.
  • Lessons from attempts to build cross-government platforms, both successes and struggles, should inform the approach to AI adoption.
  • Addressing issues around data quality, infrastructure, and procurement capabilities are important enablers of technology adoption.

[02] Lessons for AI adoption

1. What are the key lessons from public sector digital work that could apply to AI adoption? The article suggests several lessons from public sector digital work that could be applied to AI adoption:

• Invest in a new generation of leaders who understand AI's capabilities, limitations, and the enabling conditions for responsible and ethical use.
• Identify the skills and capabilities needed for responsible civic applications of AI, and build new professions and communities around them.
• Fund centers of expertise or incubators where AI technologists can work closely with practitioners to build useful AI applications.
• Carefully consider the different challenges and dependencies involved in different AI use cases, and don't assume a one-size-fits-all approach.
• Address data quality and infrastructure issues, as they will be especially relevant for AI applications.
• Ensure robust user research to identify the most promising AI use cases.
• Address the challenges of the broken market for digital services in government, which can slow down the adoption of new technologies.

2. What is the overall takeaway regarding AI adoption in the public sector? The overall takeaway is that there is no silver bullet for AI adoption, and that it requires a sustained, multi-faceted effort focused on the hard work of enabling adoption, rather than just on use cases. The article emphasizes that the lessons from past public sector digital transformation efforts can provide a valuable roadmap for driving responsible and effective AI adoption in government.