A recent Massachusetts Institute of Technology (MIT) study revealed that 95% of artificial intelligence (AI) projects fail before they even get started. For Federal agencies managing citizen data, classified information and critical infrastructure, this is not just a learning curve; it is a fundamental breakdown of how AI initiatives are conceived and executed. The disconnect between AI proliferation and AI success stems from a common pattern—agencies prioritizing tools over outcomes, launching disconnected pilots without enterprise alignment and lacking the governance structures to ensure accountability. The path forward requires a deliberate shift. Starting with mission-driven use cases, building on clean and governed agency data and ensuring sustainable adoption through people-centered strategies.
Mission-Driven Use Case Development First, Technology Second
The fastest way to stall an AI initiative is to start with the technology instead of the mission. Too often, agencies approach AI adoption by asking “what can we do with generative AI (GenAI)?” rather than “what operational problem needs solving?” This approach yields pilots that work in limited scenarios but often fail to scale because the model, data and governance do not translate to enterprise-level needs. Strong AI use cases are not discovered after implementation; they are designed deliberately around mission outcomes and real operational constraints. Agencies should begin by defining a specific challenge or opportunity, whether it is a slow and manual process, resource-intensive workflows or error-prone operations. The critical test is simple: if success would not fundamentally change how the mission operates, it is not the right use case to prioritize.
Identifying stakeholders early is equally essential. Program owners, analysts, operators and leadership must validate whether AI will genuinely help or simply add noise to an already complex technology landscape. Agencies must also be explicit about outcomes—faster decisions, fewer errors, reduced backlogs, better procurement insights or reclaimed staff time. Without clearly articulated outcomes, measuring success or defining return on investment becomes impossible. A practical prioritization matrix can guide agencies in filtering use cases into four categories:
- high-impact, high-effort investments for enterprise transformation
- high-impact, low-effort quick wins ideal for pilots
- low-impact distractions to avoid entirely
- interesting but non-urgent projects to defer
By focusing on tightly scoped problems with clear ownership and contained risk, agencies can deliver meaningful pilots that demonstrate real value and build momentum for broader adoption.
Data Foundation and Governance as the Critical Success Factor
Most AI models in use today are generalized Large Language Models (LLMs) trained on public internet data. These models are faster to deploy and have lower upfront costs, making them attractive for proofs of concept. However, they lack understanding of an agency’s unique mission, culture and decision-making context. For lasting, mission-critical AI, agencies should consider Small Language Models (SLMs) trained on agency-specific data. These models are more energy efficient, operationally reliable and context-aware with fewer mistakes. The challenge lies in fragmented data environments where records are spread across systems, formats and classification levels. This is where records management and data governance professionals become invaluable, helping to locate data and establish controls that transform data from a liability into a strategic asset.
AI learns directly from the data it was trained on and from how humans categorized it through reinforcement learning from human feedback. If the underlying information is disorganized, untagged or incomplete, the model will reproduce those flaws at scale. Properly governed, annotated and categorized data produces outputs that are accurate, explainable and trustworthy. Unstructured data—emails, PDFs, chat logs, memos, case files—represents roughly 80% of all agency information and contains the real story of mission operations. Yet most tools focus on structured data like databases and spreadsheets, missing the valuable context hidden in human-generated content. In-place data management addresses cost and security concerns by training and running models where data already lives, minimizing movement and preserving security boundaries. When Chief Data Officers (CDOs) and Chief AI Officers (CAIOs) collaborate under a shared governance model that includes Chief Information Security Officers (CISOs), Chief Information Officers (CIOs), legal teams and records leaders, innovation becomes both safer and faster because trust and accountability are built from the start.
The AI Failure Crisis and Its Root Causes
Federal AI adoption has accelerated faster than almost any other technology in Government history, yet this growth comes with significant risk. Currently, there is no Federal statute enacted by Congress to regulate AI across sectors, leaving agencies to rely on self-assessments and voluntary guidelines. The Office of Management and Budget (OMB) M-24-10 requires agencies to apply risk management and governance controls to high-impact AI systems, but without uniform standards for measuring impact or frameworks for compliance, agencies struggle to implement meaningful safeguards.
Many AI projects begin in isolation, driven by excitement about new tools or pressure to deliver results quickly, without engaging CIOs, CDOs or records management teams. Solutions may work adequately for limited use cases but lack the foundation to scale because governance, data quality and stakeholder alignment were afterthoughts rather than prerequisites. This pattern creates an explosion of activity with limited longevity, the very definition of a bubble. Experts report that Government is a generation behind industry in AI governance, a concerning gap given the sensitive citizen data, classified information and critical infrastructure at stake. If agencies rush to deploy AI without proper governance, they multiply the surface area for data errors, bias and compliance breakdowns. Expansion without oversight increases exposure rather than capability.
Sustainable Adoption Through People and Partnership
Even well-designed AI initiatives fail without sustained human engagement and vendor commitment. Vendors must remain engaged beyond initial implementation, continuing to train systems, monitor performance, incorporate feedback and deliver updates. If a vendor disappears after the sale, agencies are left without the support needed to refine and sustain their AI investments. This reinforces why starting with genuine use cases matters: when AI addresses tangible operational pain points, users are motivated to engage with and trust the technology.
Training cannot be a one-time orientation. Structured, continuous learning programs ensure that users understand not just the technology, but the workflows and data that feed it. Agencies should design AI for growth from the outset, building in governance controls, planning for scalability and considering reuse potential beyond the initial deployment. This “build once, reuse often” approach delivers efficiency gains and cost savings while making funding approval easier.
In an era where understanding how to learn has become the most essential skill, professionals must remain elastic and curious about topics that may fall outside traditional scopes, whether data governance for operational staff or technical architecture for mission leaders. By prioritizing mission-driven use cases, establishing robust data foundations, implementing governance as an enabler rather than a barrier and investing in people alongside technology, Federal agencies can move beyond experimental pilots to deliver AI that creates lasting, measurable impact.
To explore proven strategies for building mission-driven AI that lasts, watch ZL Technologies’ webinar, “From Noise to Impact: Building Mission-Driven AI in the Agency.”
