A growing consensus among experts in government technology circles is that procurement processes will be a key factor in determining whether artificial intelligence succeeds or fails in public sector use. While AI holds promise for improving efficiency, decision‑making, and public services, poorly structured purchasing practices could stall progress or lead to wasted resources.
One major challenge is that traditional government procurement systems were not designed for fast‑moving technologies like AI. They tend to be slow, rigid, and focused on compliance rather than innovation, making it difficult for agencies to acquire cutting‑edge AI tools in a timely manner. This can result in outdated solutions, missed opportunities, and higher overall costs, undermining the potential benefits of AI deployments.
Experts argue that governments need to evolve procurement frameworks to better accommodate AI’s unique characteristics, such as iterative development, data‑intensive models, and frequent updates. This may involve establishing more flexible acquisition pathways, adopting modular contracting approaches, and fostering partnerships with vendors that allow for experimentation and scaling. Without such reforms, agencies risk procuring systems that don’t deliver value or that become obsolete before they’ve even been fully implemented.
Effective procurement also requires strong internal capabilities, including technical expertise to evaluate AI solutions, clear governance around ethical and secure use, and mechanisms for ongoing performance measurement. When procurement is aligned with a strategic vision for AI that includes accountability, risk management, and user needs, governments are far more likely to achieve meaningful outcomes and public trust. Conversely, missteps in buying AI — such as overlooking data quality issues, not planning for integration, or failing to monitor impact — could slow adoption and erode confidence in the technology.
