As Artificial Intelligence (AI) rapidly reshapes industries globally, its integration into government operations presents both immense potential and significant challenges. At the forefront of navigating this complex landscape within the U.S. federal sector is Farhan Bin Amjad, a Technical Analyst at Intellect Solutions LLC. Amjad, who specializes in enterprise automation, AI integration, and federal digital modernization initiatives, offers a critical perspective on the current state of AI adoption in government, particularly focusing on what he terms the “automation mismatch” in federal contracting.
With a robust foundation spanning software engineering, project management, and government consulting, Amjad has a unique vantage point from which to observe and influence the intersection of technology and institutional reform. His work involves designing secure architectures and business process automation strategies for mission-critical environments, leveraging cloud technology, compliance frameworks, and digital transformation principles. As the lead architect behind Intellect’s Vendor Intelligence Database, the firm’s Salesforce Administrator, and its RPA Program Lead, his initiatives have directly contributed to tangible results like contract wins and enterprise-wide efficiencies.
Amjad’s recent published research, titled “Automation Mismatch: How Contractor AI Adoption Challenges Institutional Procurement Norms,” delves into the intricacies of how federal procurement systems are struggling to keep pace with the rapid advancement and adoption of AI technologies by contractors.
When asked about the role of AI in his own work, Amjad emphasizes its pervasive nature. “AI is no longer confined to a single toolset—it’s embedded across everything we do, from automation to analytics to content generation,” he states. He highlights hands-on engagement with automation platforms like UiPath for intelligent workflow automation and prototyping AI-powered triage engines using tools like H2O.ai to glean strategic insights from procurement data. His work also includes experimenting with scalable AI deployments within AWS cloud environments, always ensuring alignment with stringent federal security standards.
However, his engagement with AI extends beyond technical implementation. “At the same time, I approach AI not just as a developer, but as a researcher,” Amjad explains. His research focuses on the responsible integration of generative AI and machine learning into public sector processes, particularly where considerations of regulation, transparency, and fairness are paramount. He points to examples like RPA bots handling onboarding tasks, language models drafting intake reports, and predictive models identifying high-value solicitations as evidence of AI’s presence across various functions.
This widespread presence, he notes, underscores the critical importance of how AI is deployed and governed. His role involves designing, testing, and evaluating AI implementations through both a technical lens, ensuring efficiency, and an ethical lens, ensuring institutional appropriateness and public accountability.
Delving into the broader picture of AI in federal contracting, Amjad paints a picture of significant asymmetry. “We’re at a moment of asymmetry,” he observes. “On the contractor side, AI tools like Vultron and Unanet ProposalAI are actively reshaping proposal development—enabling vendors to submit more bids, faster, and often with greater compliance accuracy.” This has led to a surge in contractor capabilities and efficiency.
In stark contrast, the government side, represented by agencies such as the U.S. Customs and Border Protection, remains largely tethered to human-centered, regulation-bound evaluation systems. Amjad’s research reveals that despite policy encouragement from the Office of Management and Budget (OMB) and The White House, most federal evaluators encounter substantial cultural, regulatory, and operational hurdles in adopting AI, especially within the procurement lifecycle.
He points to legal mandates like Federal Acquisition Regulation (FAR) provisions 15.303 and 15.304, which necessitate documented human justification for evaluation criteria. Such requirements make deploying many machine learning models, particularly those with dynamic feature weighting that might lack clear, human-readable justifications for specific decisions, inherently difficult within the current framework. “So, while contractors are accelerating with AI, agencies are still trying to reconcile innovation with fairness, transparency, and oversight,” Amjad explains.
The current state of the field is, in his words, “technically advanced, institutionally hesitant.”
Discussing how AI is currently contributing to government efficiency, Amjad acknowledges pockets of success, primarily through Robotic Process Automation (RPA) and advanced document processing. Agencies like the IRS and GSA have utilized AI to streamline tasks such as processing Freedom of Information Act (FOIA) requests, auditing logs, and reviewing procurement files. However, these applications are typically confined to specific, narrow use cases where the potential risks of bias or lack of transparency are deemed low.
Amjad is careful to qualify the notion that AI inherently equates to greater efficiency in government. “What’s important to recognize is that AI doesn’t inherently make the government more efficient. It makes it conditionally more efficient—when embedded in processes designed for traceability, fairness, and legal defensibility,” he explains. This dependency on underlying process design is why current adoption often remains narrow and is frequently driven by cost-saving motivations rather than broader mission innovation.
While efficiency gains are real, they are, for now, scattered and localized. Amjad argues that achieving truly sustainable efficiency requires institutional change to occur in parallel with technical deployment. Without this fundamental shift, AI risks remaining a supplementary feature rather than a truly transformative force within governmental structures.
This brings Amjad to what he identifies as the most urgent challenge: the “automation mismatch.” This structural divide, he posits, exists between the sophisticated, AI-enhanced capabilities now common among contractors and the manual evaluation systems that still dominate federal agencies. This disparity creates significant operational strain on the government side and, critically, can undermine the fairness of procurement processes.
Contractors, armed with AI, can submit a higher volume of proposals at greater speed and potentially accuracy, while contracting officers are still required to provide detailed, narrative justifications for every evaluation decision, adhering to rigid, human-readable criteria. This imbalance slows down evaluations, contributes to backlogs, and potentially disadvantages innovative approaches from contractors if their AI-assisted proposals don’t fit neatly into legacy evaluation paradigms.
Bridging this “automation mismatch” requires more than simply deploying more advanced technology within government agencies, Amjad stresses. It demands fundamental institutional alignment. “My thesis shows that regulatory frameworks like the FAR and agency-specific manuals like HSAM need to evolve in parallel with AI tooling,” he said.
Addressing this gap necessitates comprehensive training for government evaluators on interacting with and understanding AI outputs, developing clear policy guidance on explainable AI (XAI), and fostering robust cross-functional collaboration between crucial teams like IT, legal, and procurement. Without proactively addressing these deep-seated institutional conditions, Amjad warns, the AI gap will continue to widen, irrespective of how quickly the underlying technology advances.
Looking towards the future, Amjad is optimistic about the potential for AI to better serve the federal government, provided the necessary institutional groundwork is laid. He anticipates that future AI systems will become more context-aware, transparent, and integrable – qualities he deems essential for effective and trustworthy government use. Progress is already being made with explainable AI (XAI) models, which offer the potential for procurement officers to understand the reasoning behind system recommendations. Tools capable of mapping AI-driven decisions back to specific solicitation criteria in a clear, human-readable format will be particularly valuable.
Additionally, the proliferation of low-code AI platforms has the potential to democratize access to AI development and deployment within agencies, enabling more teams—beyond just dedicated data scientists—to experiment with and implement intelligent automation solutions. As AI models become more sophisticated and regulatory bodies issue clearer guidance on their ethical use and deployment in government, Amjad foresees the development of more mission-aligned AI applications, customized to the specific needs of individual agencies rather than relying solely on generic enterprise tools.
Ultimately, however, Amjad circles back to the fundamental prerequisite for realizing this future potential: institutional readiness. “Institutional readiness will determine whether these improvements have real impact,” he said.
His research consistently emphasizes the necessity of building organizational capacity—including training, policy updates, and cultural shifts—in tandem with the adoption of advanced technology. In his view, the most promising future scenario is one where AI extends far beyond merely supporting procurement processes to fundamentally reshaping how agencies deliver services, allocate vital resources, and inform policy planning – a transformation contingent on successfully bridging the ‘automation mismatch’ and aligning institutional capabilities with technological possibilities.
Farhan Bin Amjad remains a trusted voice at the frontier of federal digital transformation, offering expert analysis on the critical challenges and strategic pathways for integrating AI responsibly and effectively into the heart of government operations. His work underscores that the future of AI in the public sector is not just a technical question, but a profound institutional one.
