Why the New AI Buying Playbook Matters for Federal Government Contractors
Public buyers are moving quickly to acquire artificial intelligence, but the real disruption lies less in what they buy and more in how they buy it. In their November 2025 analysis for the Open Contracting Partnership, Kathrin Frauscher and Kaye Sklar synthesize interviews with more than fifty public-sector practitioners across the United States, Europe, and beyond to map the emerging architecture of AI procurement. For federal government contractors, this is not a distant policy conversation; it is a signal that the rules of the game for competing, delivering, and managing risk on AI-enabled contracts are changing in real time.
The first key finding is that off-the-shelf AI solutions are dominating current purchasing behavior. Government entities are prioritizing licenses embedded in existing cloud and productivity platforms over bespoke systems, particularly for “safe” use cases like writing assistance, document management, and data analysis. For contractors, this means that the near-term competition is less about landing a single large, custom development award and more about positioning standardized tools and services that can be rapidly adopted at scale. It also implies that value will increasingly be judged on integration, configuration, and change management rather than purely on novel code. Firms that can operate as “implementation partners” around enterprise platforms may experience more opportunity than those focused solely on building standalone tools.
A second structural shift is the consolidation of AI purchasing through central IT or digital transformation authorities. The report highlights how jurisdictions such as the United States are moving toward enterprise-wide AI acquisition models in which a single entity negotiates licenses and frameworks on behalf of all agencies. For federal contractors, this centralization reshapes the sales and capture strategy: success may depend on early engagement with central buying offices, shared service providers, or GSA-type vehicles rather than one-off agency-level pursuits. Once a central agreement is in place, the marginal cost for agencies to adopt additional modules or services decreases, and vendors already “inside the tent” are advantaged. Conversely, being locked out of these arrangements may effectively exclude firms from wide swaths of demand.
The report also surfaces the phenomenon of “shadow AI”: tools entering government not through formal procurement, but via free pilots, grants, academic partnerships, or features bundled in existing software. This pattern poses compliance and accountability challenges that federal contractors will recognize immediately. Uncontracted or lightly governed AI usage raises questions about data protection, cybersecurity obligations, intellectual property, and bias or transparency requirements. Vendors that treat pilots as governed, documented engagements—with clear terms on data use, security, and evaluation criteria—will be better positioned when agencies eventually seek to regularize these tools under formal contracts. In other words, treating informal experimentation as a precursor to rigorous procurement rather than a marketing afterthought becomes a strategic advantage.
Frauscher and Sklar argue that the true policy imperative is not simply to buy AI but to become “AI-ready.” They emphasize the need for an organization-wide AI strategy, explicit governance frameworks, and a roadmap for implementation and upskilling, illustrated by examples such as the City of Seattle’s comprehensive AI plan. For federal contractors, this translates into a shift in what agencies will demand during solicitations and negotiations. Contractors should anticipate requirements for model transparency, risk assessments, human-in-the-loop controls, auditability, and ongoing performance monitoring. Firms that can embed responsible AI practices—documentation, alignment with agency governance policies, and robust post-award support—will be more competitive in best-value tradeoffs and more resilient to evolving guidance from OMB, NIST, and agency-specific AI policies.
The report further underscores the need to build in-house government capacity to understand, evaluate, and manage AI. Current assessments suggest that many public bodies, particularly at the state level, are only at the earliest stages of this journey. For contractors, this capacity gap creates a delicate dual role: vendors are simultaneously solution providers and de facto educators. Contractors who can credibly support training, co-design, and knowledge transfer—without overstepping into conflicts of interest—are likely to be more trusted partners and may see increased demand for advisory or “AI implementation partner” scopes of work tied to technology delivery.
Finally, the authors call for procurement to become an engine of innovation rather than a bottleneck, citing examples such as Georgia’s use of a Request for Qualified Contractors to build a diverse pool of AI service providers, including smaller firms. This is particularly salient for federal government contractors: we can expect wider use of multi-award vehicles, qualification-based pools, and agile procurement methods that enable experimentation and iteration. Traditional contractors may need to adapt their pricing, teaming, and risk-sharing models to perform effectively under these structures, while newer entrants may find their first foothold in curated innovation pools rather than conventional full-and-open procurements.
Disclaimer: This blog post is a summary and interpretation of the cited Open Contracting Partnership report and related public information. It is provided for informational purposes only, does not constitute legal, technical, or procurement advice, and may not reflect subsequent policy or market developments.