Artificial intelligence (AI) in federal acquisition has moved beyond experimentation. Agencies are asking how to scale AI responsibly, effectively, and sustainably rather than questioning adoption.
By: Bridget Deary, Partner - Automation & Data Platforms, IBM
Insights from a recent panel featuring leaders from the General Services Administration (GSA), the Defense Logistics Agency (DLA), and academia highlight a clear message for federal agencies: success with AI depends less on the tools themselves and more on governance, data readiness, and workforce capability.
AI in Federal Procurement: From Pilot to Practice
AI is already finding its way into acquisition workflows. Agencies are using it to generate Federal Acquisition Regulation (FAR)-compliant documents, accelerate market research, support procurement modernization, and improve decision-making.
The conversation is shifting from automating individual tasks to rethinking how AI can reshape broader operating models. At DLA, for example, AI is moving into production environments and shaping supply chain decision-making. Use cases such as demand forecasting, identifying supply chain risks, and improving auditability demonstrate that AI is delivering tangible value today.
It is clear that technology alone will not make these use cases successful. AI depends on strong foundations: clean and accessible data, integrated systems, effective governance, and a federal workforce equipped to understand and effectively leverage the technology.
The takeaway for federal leaders is clear: AI creates the greatest value when it is embedded into mission workflows rather than isolated in pilot programs. For more on how the responsible use of AI can help to improve procurement, see the following publications Outcome-Based Contracting in U.S. Government, Government Procurement and Acquisition: Opportunities and Challenges Presented by Artificial Intelligence and Machine Learning and How Can Governments Use AI to Improve Procurement?
Governance Is Catching Up to Adoption
While AI adoption accelerates, governance frameworks continue to evolve. Federal policy on AI has shifted over time, alternating between periods of rapid innovation and stronger oversight. Recent federal guidance signals an evolving emphasis on accountability, risk management, and transparency.
Procurement presents particularly complex challenges. Existing frameworks governing organizational conflicts of interest (OCI), source selection, and procurement integrity were largely designed around human decision-making. They do not always clearly address scenarios in which AI helps draft solicitations, conduct market research, analyze requirements, or support proposal evaluation.
That does not mean agencies should wait for every policy question to be resolved before embarking on their AI journey. It means they should adopt with AI with appropriate guardrails. Agencies should treat AI as a decision-support tool rather than a decision-maker. Human judgment remains essential, particularly in high-stakes acquisition decisions. Strengthening oversight requires updated policy and a workforce that understands how AI systems influence outcomes.
GSA’s Role: Enabling Access and Reducing Friction
GSA plays a central role in scaling AI across government by lowering barriers to adoption. Rather than building a single solution, GSA focuses on enabling agencies to access, test, and evaluate multiple AI models in secure environments.
This approach centers on three priorities:
Equally important is GSA’s emphasis on maintaining choice and competition. Agencies are encouraged to avoid dependence on a single vendor and instead adopt a portfolio approach to provide greater flexibility, resilience and performance.
For federal leaders, this model offers a practical path forward: start with accessible tools, evaluate performance rigorously, and scale what works.
The Data and Workforce Challenge
For many agencies, the greatest barriers to AI adoption are not technical. They are organizational. Data fragmentation, siloed legacy systems, and limited AI literacy constrain scaled progress across agencies.
At DLA, the vision of an integrated “control tower” with end-to-end visibility illustrates what is possible when data is unified and accessible. Achieving that vision requires sustained investment in data infrastructure and governance to ensure quality data is integrated and available when needed.
At the same time, workforce readiness remains a critical factor. Acquisition professionals need more than access to AI tools. They need the skills to use them responsibly. This includes understanding model limitations, interpreting outputs, and applying appropriate oversight.
Agencies that invest in workforce training and communities of practice will scale AI more effectively than those that focus only on technology deployment.
High-Impact Use Cases Available Now
Federal agencies do not need to wait for the next breakthrough to begin realizing value from AI. Several high-impact applications are available today:
These applications share a common characteristic: they augment human work rather than replace it. Agencies that start with practical, mission-aligned use cases can build momentum while managing risk.
A Shift in Mindset: AI as an Operating Model
The overarching theme from federal leaders is that AI represents a shift in how agencies operate rather than simply adding a new technology layer. Scaling AI requires aligning policy, procurement, data, and workforce strategies.
Agencies that succeed will:
The question for federal leaders is no longer whether AI has a role in acquisition. It is whether agencies have the governance, data, workforce, and operating model required to turn promising pilots into sustained mission value. Those that focus on these foundations will be best positioned to move beyond experimentation and make AI a durable part of how government works.