Generative artificial intelligence (AI) is transforming public agencies by enhancing service delivery and extracting valuable insights from extensive datasets. To address this rapid evolution, President Biden issued an Executive Order in October 2023 on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. This order outlines the necessary steps for agencies to adopt generative AI responsibly.
Integrating generative AI into public agencies is no easy task. “While piloting AI projects is straightforward, turning them into enterprise-level assets requires a solid foundational framework,” states Alex Johnson, EVP of strategy and technology at XYZ Solutions, an Amazon Web Services (AWS) Premier Tier Services Partner. XYZ Solutions assisted a nonprofit organization in developing a robust architecture on the AWS Cloud, laying the groundwork for effective generative AI tool implementation. In this article, AI experts Johnson and Sarah Thompson share five best practices they utilized to prepare this agency for generative AI.
Prioritize Data Privacy and Security
For government entities, security is a paramount concern regarding generative AI, encompassing the datasets being analyzed, the integrity of the AI models, and the data processing itself. The Executive Order underscores the importance of maintaining security through comprehensive mitigation strategies, including identifying and addressing vulnerabilities, minimizing data collection, and anonymizing citizen information.
“To uphold privacy and security, agencies must enforce stringent access controls to safeguard sensitive data,” notes Johnson. “This should include data encryption—both at rest and in transit. Implement security measures akin to those used for regulatory compliance with HIPAA and other data privacy laws.” A zero-trust framework should be adopted to ensure continuous verification of user identities and device integrity accessing the network. This strategy minimizes the risk of unauthorized access, especially when dealing with large datasets that contain private information.
Promote Equity Through Human Oversight
A significant challenge for federal agencies using large datasets is preventing bias and upholding civil rights throughout data collection, processing, and application via AI. Employing a data minimization strategy—collecting only the essential data for specific models—should guide generative AI usage. Agencies must ensure clarity and validation of data sources to avoid unintentional bias.
“Ensuring explainability is crucial for mitigating bias,” emphasizes Thompson. “You must be able to demonstrate how generative AI arrived at its conclusions. If bias is detected, you can trace back through the model to rectify the issue at its origin.” Keeping human oversight in decision-making remains vital to counteract bias in generative AI. “While generative AI excels at numerical predictions from vast datasets, human judgment is essential for applying empathy and ensuring ethical outcomes,” Johnson adds.
Foster a Culture of Innovation and Upskilling
“Government agencies often resist change,” warns Johnson. “They may lack the foundational requirements for digital transformation. Introducing a ‘change agent’ can help leverage innovation.” Establishing “centers of excellence” around AI projects can cultivate innovation, rewarding leaders who explore new generative AI applications within the agency. Sharing success stories and best practices will encourage excitement and further growth in AI utilization.
Concerns about job displacement can hinder generative AI adoption. Agencies should create intentional upskilling opportunities for employees to acquire the necessary skills to work with AI, reassuring staff that AI can reduce repetitive tasks and allow them to focus on more rewarding work. Therefore, internal training programs are crucial for the successful adoption of generative AI. For more insights on effective training programs, check out this excellent resource.
Implement a Modern Digital Foundation and Governance
Core components of digital transformation—robust security architecture, data exchange capabilities, and a foundational data model—must be established to support generative AI. Since generative AI relies on multiple data sources, strict data governance is essential for data access, beginning with a strong API architecture and updated applications portfolio.
“We conduct workshops to evaluate AI suitability for your agency,” shares Johnson. “We start with prescriptive questions: Have you completed the essential digital transformation steps? Is your security infrastructure solid? Do you have a strong cloud foundation?” To realize generative AI as a valuable enterprise asset, a solid digital framework and trained personnel are necessary.
Establish Cost Controls Early On
Before rolling out generative AI, agencies should grasp the cost structure, which resembles cloud computing pricing. “With generative AI, you’re charged per cluster of tokens,” explains Thompson. “Having a system to monitor token consumption is vital to track your AI expenses, which can fluctuate significantly based on user count or implementation complexity.”
Agencies should consider scheduling an AI readiness workshop to learn how to create a cost-optimized AI architecture. XYZ Solutions’ Elevate AI labs, for instance, offer consultations on establishing cost controls, assessing technological readiness for AI, and determining the suitability of the current environment for desired AI outcomes. These workshops also provide opportunities to experiment with proofs of value for upcoming AI projects.
Harness the Power of Generative AI Effectively
Given its complexity, generative AI cannot simply be purchased and integrated into existing infrastructures. Thus, selecting the right vendor is crucial for the success of generative AI initiatives. Here are some factors to consider:
- Digital transformation serves as the foundation for generative AI, so your vendor must have a proven track record in guiding and executing such projects.
- The ideal vendor should deeply understand your agency’s mission and the outcomes you aim to achieve, alongside expertise in the relevant technologies.
- Look for a vendor experienced in addressing the unique challenges of high-compliance public sector environments, capable of operating at the scale of your agency’s data and mission.
- Lastly, as the generative AI vendor relationship should be consultative, your vendor must engage in a collaborative dialogue aimed at finding the best solutions for your agency’s mission.
For further insights on generative AI, consider reading another blog post linked here.
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