A growing disparity has emerged between the rapid adoption of artificial intelligence (AI) tools by federal contractors and the slower, more constrained pace at which U.S. government agencies can integrate AI into their own procurement and evaluation processes.
This divergence, termed the “automation mismatch,” presents complex challenges for digital modernization and effective governance, particularly within critical agencies like the U.S. Customs and Border Protection (CBP).
Farhan Bin Amjad, a Technical Analyst at Intellect Solutions LLC, is at the forefront of analyzing and addressing this critical issue. With a robust background spanning software engineering, project management, and government consulting, Mr. Amjad specializes in enterprise automation, AI integration, and federal digital modernization initiatives.
His work involves deg and delivering high-impact solutions, from large-scale SharePoint migrations and Salesforce automation to sophisticated iPath-based RPA workflows, which have transformed collaboration and compliance for numerous organizations.
As a published researcher and strategic contributor to Intellect’s innovation lab, Mr. Amjad’s recent paper, “Automation Mismatch: How Contractor AI Adoption Challenges Institutional Procurement Norms,” directly confronts this pressing challenge. His interdisciplinary expertise uniquely positions him to bridge the gap between cutting-edge technology and the complex realities of public-sector reform.
“My paper explores what I call the automation mismatch—a structural gap between how quickly federal contractors are adopting AI to streamline proposal development, and how slowly agencies like CBP can respond due to regulatory and cultural constraints,” Mr. Amjad explains.
He highlights the stark contrast: contractors are increasingly leveraging sophisticated AI tools, such as Vultron and Unanet ProposalAI, to generate compliant, tailored bids at an unprecedented scale and speed. These tools can rapidly analyze solicitations, build compliance matrices, and draft technical volumes in hours rather than weeks, dramatically accelerating the contractor side of the procurement lifecycle.
However, the government side operates under fundamentally different conditions. Procurement evaluators within agencies must adhere to strict regulatory frameworks like the Federal Acquisition Regulation (FAR) and agency-specific supplements such as the Homeland Security Acquisition Manual (HSAM) at CBP. These frameworks require detailed narrative justification and human-led scoring processes, making the integration of AI-driven evaluation tools difficult and complex.
“On the contractor side, AI is accelerating everything,” Mr. Amjad notes. “But on the government side, you’re still dealing with tight budgets, sensitive data (e.g., proprietary or classified information), and deeply rooted institutional practices.” He illustrates the challenge: even if an agency wishes to use AI to assist in proposal evaluation, doing so might expose protected vendor information or violate transparency requirements if the AI model cannot adequately explain its scoring logic.
According to Mr. Amjad’s research, this mismatch is not primarily a technological problem but one rooted in institutional theory. It stems from deeply embedded norms, established laws, and existing ability structures that govern public procurement. These factors create a significant productivity gap: contractors are equipped to handle an increasing volume of bids with AI-driven efficiency, while government evaluators remain constrained by manual processes and regulatory requirements designed for a pre-AI era.
The potential consequences of this disconnect are substantial. Agencies risk being overwhelmed by the sheer volume and complexity of AI-assisted proposals, potentially leading to slower processing, increased evaluator burden, and an inability to fully capture the benefits of contractor innovation. “If we don’t address this disconnect, agencies risk falling behind, overburdened by volume and unable to take advantage of innovation,” Mr. Amjad warns.
Cultural factors play a particularly significant role in limiting AI integration within federal procurement. The existing culture places a strong emphasis on fairness, meticulous documentation, and explicit human ability. Contracting officers are rigorously trained to follow strict evaluation procedures and provide written justifications for every decision and trade-off. This cautious approach is essential because procurement decisions are subject to intense scrutiny, including audits, bid protests, and public oversight.
“This culture is cautious—and for good reason: decisions must stand up to audits, protests, and public scrutiny,” Mr. Amjad states. However, this necessary caution also generates resistance to AI systems, particularly those perceived as “black boxes” that cannot clearly explain how they reached a conclusion or adapt dynamically during the evaluation process.
His research indicates that even when federal policies, such as Office of Management and Budget (OMB) memos, encourage AI adoption, many federal staff remain hesitant. This hesitation stems from fears of introducing errors, a lack of clear internal guidance on AI use, and a professional norm that historically equates automation with increased risk in high-stakes environments. “Until these cultural factors are addressed through training, policy updates, and institutional dialogue, AI integration will remain limited regardless of technical capability,” he emphasizes.
Addressing the skepticism towards AI among procurement officials is crucial for successful integration while simultaneously complying with AI governance standards. Mr. Amjad believes this skepticism, while natural and even necessary, can be constructively overcome.
He proposes several practical solutions grounded in his research:
- Explainable AI (XAI) Frameworks: Instead of relying on opaque algorithms, procurement officers need AI tools that can provide transparency. XAI allows s to understand and trace the logic behind AI outputs. This enables decisions to be aligned with solicitation criteria in human-readable , satisfying FAR requirements for justification and transparency.
- Robust Governance Infrastructure: Beyond technical tools, successful AI integration requires a comprehensive governance framework. This includes developing clear internal guidance, providing targeted training for procurement staff, and fostering cross-functional collaboration between legal, IT, and program personnel. This ensures that AI is adopted responsibly and compliantly.
- Low-Risk Pilot Programs: Implementing AI tools through pilot programs allows agencies to experiment and learn in a controlled environment. Testing AI tools in parallel with traditional human evaluation processes reduces risk and builds institutional confidence. This allows agencies to understand the capabilities and limitations of AI firsthand before full-scale deployment.
- Building Institutional Buy-in: AI adoption cannot be viewed as a mandate imposed externally. It requires a process of internal trust-building that respects and aligns with existing procurement values. By demonstrating how AI can enhance transparency, traceability, and fairness—rather than undermine them—agencies can foster acceptance among the workforce.
“That’s how skepticism becomes informed adoption,” Mr. Amjad concludes. By prioritizing these strategies, agencies can bridge the “automation mismatch,” enabling them to harness the benefits of AI-driven innovation while upholding the critical principles of fairness, transparency, and ability inherent in federal procurement.
Mr. Amjad’s work extends beyond theoretical analysis; he is the lead architect behind Intellect’s Vendor Intelligence Database and serves as the firm’s Salesforce and RPA Program Lead. These initiatives have directly contributed to significant contract wins and enterprise-wide efficiencies, demonstrating his ability to translate strategic insights into practical, impactful solutions for mission-critical environments.
With deep expertise at the intersection of cloud technology, compliance, and digital transformation, Farhan Bin Amjad is recognized as a trusted voice shaping the discourse at the frontier of federal digital transformation. His analysis highlights that successfully navigating the era of AI-driven procurement requires not just technological adoption, but a thoughtful, strategic approach to institutional reform, cultural adaptation, and robust governance. Bridging this critical “automation mismatch” is essential for the federal government to remain effective, efficient, and fair in an increasingly automated landscape.
