Prioritize what matters: A smarter path to AI adoption

With tightening budgets and evolving mission needs, federal agencies are under increasing pressure to adopt and scale AI solutions that can deliver value with fewer resources. That’s why smart prioritization is no longer optional for agencies—it’s a critical success factor.

In the private sector, prioritization fuels AI innovation. Startup leaders continuously weigh limited time and resources against impact and risk. Applying that same discipline can help federal agencies focus their efforts—avoiding AI for AI’s sake and investing in solutions that deliver real results.

Outcome-driven AI development starts with focus

Agencies that want to succeed with their AI initiatives can’t scale everything at once. They must zero in on high-impact outcomes, make strategic tradeoffs, and deploy proven frameworks to align efforts with mission goals.

Prioritization enables:

• Cost savings by avoiding low-impact investments
• Faster delivery of what matters most
• Increased efficiency across teams and programs
• Better decision-making using data and automation

How to define mission objectives and key returns

One of the biggest barriers to prioritization in government—regarding AI or anything else—is a lack of clarity. Federal missions are often broad and ambitious, and performance metrics can be vague, disconnected, or difficult to measure in real time. That makes it hard to link individual investments to real outcomes, especially when budget and urgency force difficult tradeoffs.

To prioritize effectively, agencies need a shared framework that connects day-to-day decisions to the broader mission. That’s where Objectives and Key Results (OKRs) come in.

Why OKRs work

OKRs are a structured goal-setting method that helps teams align around what matters most. At their core, they force clarity on two essential questions:

• What are we trying to achieve? (Objective)
• How will we measure progress? (Key Results)

This model gives product teams, programs, and executive sponsors a common language for impact and a consistent way to measure whether work is advancing mission goals. When used well, OKRs ensure that modernization efforts stay focused, efficient, and aligned with strategic priorities.

Practical frameworks for smarter decisions

You don’t have to start from scratch. These proven frameworks can help federal teams make faster, smarter, and more defensible decisions:

• MoSCoW Method: Classifies features as Must, Should, Could, or Won’t. Ideal for distinguishing essentials from extras.
• Eisenhower Matrix: Prioritizes work by urgency and importance. Perfect for overloaded backlogs.
• Kano Model: Identifies what users expect, what they value, and what will truly delight. Useful for customer-facing tools.
• RICE and ICE Scoring: Quantifies effort vs. impact for feature roadmaps.
• Value vs. Complexity Matrix: Highlights quick wins by mapping value against implementation difficulty.

There’s no single “right” framework but these tools give structure to what can otherwise be subjective decisions—especially in multi-stakeholder environments.

Prioritization is the key to scaling AI

Smart prioritization is a crucial asset when agencies must make decisions about which AI solutions they should scale.

Agencies can’t afford to scale every viable idea that comes out of the rapid prototyping process. They must measure AI apps not only against agency mission requirements but also one another to answer the question: which solution gives them the most bang for their buck?

We’re currently working with a federal agency to develop an Impact Dashboard that can inform these critical decisions. The product relies on two key metrics: a RICE score (mentioned above) and a GenAI Fit score. The latter metric considers each solution’s OKRs and the agency’s overall business objectives for funding AI. If there’s a high degree of alignment, the GenAI Fit score is higher. With that information in hand, the agency can advance the best AI prototypes to scale with confidence they’ll deliver a meaningful ROI.

Prioritize for progress—not just activity

Federal agencies may never operate exactly like startups, but they can apply the same discipline to prioritize smarter, modernize faster, and stretch every dollar further.

By clearly defining objectives and applying the right frameworks—particularly when developing AI solutions—agencies can focus resources where they matter most and deliver better outcomes, faster.