The Case for Starting Small

When discussing AI with my public sector clients, emphasizing that we can start small is an important step in securing buy-in. Some business leaders, public sector or otherwise, are skeptical of any form of AI. The thought of implementing large, AI-driven projects seems daunting and risky. By beginning with simpler applications, organizations can ease into AI and start to see its value without introducing new risks. 

A few simple, but impactful, ways to get started include: 

• Automating meeting notes: Imagine how much time could be saved if AI handled meeting minutes, allowing staff to focus more on the discussion and less on notetaking. 
• Summarizing documents: AI could quickly condense lengthy reports, making it easier for public employees to access the information they need without sifting through pages of text. 
• Email Drafts and Responses: Generative AI can assist with drafting email templates or quick responses to common queries. This is particularly useful in customer service or internal communications. It can save time by automatically generating responses based on past patterns or instructions. 

These smaller, more digestible projects can serve as proof-of-concept, showcasing the practical benefits of AI without overwhelming teams with complicated implementation processes. 

Once an organization becomes comfortable with these initial applications, the door opens for more complex, but still relatively small-scale, AI projects. For example, an organization could train custom AI bots, which could streamline processes and provide answers to questions based on a certain set of enterprise knowledge. These kinds of projects, while more advanced, can become realistic goals after the organization has had some success with smaller, lower-risk applications. 

While there are many concerns around AI that are typically beyond our control, e.g., environmental concerns, societal disruption, disinformation, etc., we are keenly aware of how our projects and solutions are vulnerable to privacy concerns, security vulnerabilities, and bias and discrimination. 

Any conversation about AI in the public sector must address the security and privacy concerns that come with it. Many people see generative AI as a vast world where all their data will be exposed, and this simply isn’t the case. However, this is a valid concern for public sector organizations and one that can be mitigated. 

We’ve Been Here Before

Generative AI has the potential to revolutionize public services, but hesitant organizations don’t need to dive headfirst into complex projects to see its benefits. By starting with small, manageable projects, public sector clients can begin to see the value AI brings and be more open to larger AI applications down the road. 

Most new technologies are met with skepticism by businesses and governments. When cloud computing first emerged, many businesses were reluctant to adopt it due to concerns about security, data privacy, and control over their data. Over time, as cloud providers improved security measures and demonstrated the cost savings and scalability benefits, businesses and agencies began to adopt cloud services widely. Today, cloud computing is a cornerstone of modern IT infrastructure. 

The future of AI in the public sector is not some distant concept—it’s already here. The key is to take those first steps thoughtfully and bring our clients along with us on this journey. 

-Lyria Hojnacki, Project Manager