🔁 Following up on my Part 1, I explained why many federal AI initiatives never progress beyond the pilot phase — usually due to misaligned goals, poor data planning, or overlooked change management.
Managing AI Like Mission-Critical Infrastructure
📍 But even when these issues are addressed, projects still struggle.
Too often, federal agencies manage AI like temporary tech experiments, rather than long-term infrastructure tied to the mission.
Here’s what needs to change.
🔧 AI Must Be Treated Like Mission-Critical Infrastructure
To scale and sustain AI across government, agencies need to:
- 🔹 Implement Lifecycle Governance
Oversight can’t stop at deployment.
Agencies need policies for model updates, data drift, auditability, and sunsetting AI systems. - 🔹 Address Risk Beyond Technology
AI introduces fairness, transparency, and privacy risks that can’t be solved with code alone, as much as the big cloud providers try.
Agencies must actively manage ethical and reputational risk, especially in public-facing programs. - 🔹 Establish Cross-Functional Ownership
Successful AI doesn’t live in the CIO’s office alone.
Legal, compliance, mission leadership, data officers, and IT all need defined roles from day one.
🧩 Use AI-Specific Frameworks, Not Generic Playbooks
Traditional IT PM frameworks fall short when applied to AI.
Federal programs benefit from adopting specialized approaches like the Cognitive Project Management for AI (CPMAI) framework, which includes:
- AI use case alignment
- Data readiness assessment
- Governance checkpoints
- Risk mitigation tailored to intelligent systems
📌 Coming Next in Part 3:
Why federal RFPs need to formally include a CPMAI-certified AI Project Manager as a required labor category — and how that simple shift can improve project outcomes across the board.
💬 How is your agency planning for long-term AI maintenance and governance?
Share your thoughts or challenges below.
Share your thoughts or challenges below.
