Work Smarter, Not Harder: Leveraging Existing IT Infrastructure for AI Integration

As artificial intelligence (AI) continues to grow in ubiquity, it's clear that this technology may be one of the greatest disruptors the world has ever seen. AI is streamlining work and improving efficiency, making it a powerful tool for agencies of all sizes. This is especially true as many state and local governments face condensed budgets, turning AI into a vital asset in keeping projects on track.   

While state and local government leaders know they need to harness the power of this transformative technology, many have not been able to optimize their technology stack for AI. While graphics processing units (GPUs) are ideal for handling AI workloads, they need to be budgeted for and procured. On top of that, current data centers are not equipped to handle the GPUs' power and cooling requirements. 

Procurement also often struggles to keep pace with emerging technologies like AI, creating additional challenges in implementing new, purpose-built infrastructure, hardware, and software to support AI use cases. Existing contracts must be examined early in the process for alterations, especially where there may be gaps in procurement abilities regarding AI.

Once procurement challenges are addressed, agencies should begin identifying how they can leverage current technology investments, like cloud environments, to successfully deploy AI. Correctly combining new tools with existing solutions maximizes current investments while minimizing risks and optimizing performance. 

Let's explore the key factors agencies must consider to successfully integrate AI capabilities into their operations.

Getting creative: Putting existing tools to work for state and local government agencies

Before making any changes to an agency's technology environment, state and local government leaders must first ensure the desired outcomes from AI are clearly articulated and represent citizen and other stakeholder expectations. If the outcomes aren't aligned with stakeholder objectives, agencies can underestimate requirements from the IT systems and possibly risk wasting entire resources.   

Teams can then assess their current IT capabilities to optimize the use of existing investments for AI. This means evaluating their infrastructure's ability to support their defined use cases and goals, even if just in the short term or as a launchpad to start their AI journey. While this will likely include GPUs and higher bandwidth networking solutions in the future, organizations can get started with their existing central processing unit (CPU)-based infrastructure. Other aspects of IT that must be considered in this stage include data management tools, data flows, and APIs. These data sources must be integrated correctly and appropriately to optimize the accuracy of the AI. 

The agency's sustainability goals and short and long-term ROIs must also be considered when determining whether to reuse existing IT or invest in new infrastructure. AI infrastructure helps agencies do more with less, reducing their carbon footprint and increasing ROI through vastly improved performance. For state and local governments, this means unlocking better outcomes for constituents through avenues like AI-enabled treatment paths or more accurate and efficient eligibility determination.

Putting a plan in action: Adding AI tools to the mix

Once agency IT leaders have established goals and assessed all aspects of their existing technology landscape for AI compatibility, they must put their adoption plans in motion. The first step is risk-based contingency planning. Any existing environment envisioned to be utilized for AI should be evaluated to identify possible problems like system overload or integration difficulties, and plans should be developed to handle and/or recover from these issues. 

Having a robust lab environment can also help identify these areas of risk pre-deployment and allow teams to develop corresponding recovery plans. Stakeholders must be engaged early to determine the level of acceptable risk specific to the agency and which mitigation or avoidance strategies are warranted, such as contingency planning or required investments in new technology.

Agencies must ensure their teams have the skills they need to support the integration and maintenance of AI tools. Before deploying these solutions, IT leaders should seek out hands-on training, like labs and workshops, that enable teams to gain practical experience with the tech. 

When it's time to deploy AI models, thorough iterative and incremental integration testing, including User Acceptance Testing (UAT), is essential to confirm the AI model operates smoothly within an agency's existing system infrastructure. This is how specific problems like previously unidentified flaws, misconfigurations, or even cybersecurity vulnerabilities are identified.  

Post-integration, real-time monitoring, and feedback loops are important to manage the roll-out and quickly address issues. The main goals of these efforts are incremental improvements to the AI systems and supporting infrastructure. On the other hand, a failure to implement monitoring and feedback loops can lead to slow response times, performance issues, and outages, resulting in users losing faith in the system's ability to support their workloads.

One of the most important areas for preparation and ongoing monitoring is the security of AI systems, especially as threat actors continue to aggressively target sensitive citizen data. Integrating new tools into existing infrastructure inevitably creates additional vulnerabilities that can be exploited. To avoid attacks, agencies must develop comprehensive AI security programs that address multiple areas of concern, including vulnerability assessments, governance, and policy development, and offer regular security awareness training. These measures will help to ensure any gaps in an agency's existing security controls are identified so that AI deployments can be assured secure and compliant with organizational goals and regulatory requirements.

AI deployment is no longer a suggestion; it's now a requirement for agencies to manage workloads while working within imposed budget confines. State and local governments must employ a thoughtful, phased approach to integrating AI with existing IT systems. By doing so, agencies will maximize existing investments and accelerate citizen outcomes while maintaining ethical standards and compliance requirements.