The recent executive order, “Promoting Advanced Artificial Intelligence Innovation and Security,” directed at federal agencies to bolster their defenses against artificial intelligence-powered threats is the latest in an ongoing push for agencies to assess their AI readiness. However, in practice, these requests can come across as counterintuitive, creating confusion between enthusiasm for AI and true readiness.
While enthusiasm reflects a desire to explore technology, readiness represents the framework and capability to deploy sustainable AI systems, including infrastructure, governance, security and data maturity.
But understanding the difference between enthusiasm and readiness overlooks a critical first step in developing purposeful applications of AI, which is defining the mission AI is meant to support. Once clear goals and outcomes are set, technical and operational readiness work can align to drive the outcomes agencies are trying to achieve.
The first step in AI readiness: Setting a mission
One of the biggest mistakes organizations make is treating AI adoption as the mission rather than viewing AI applications as strategic tools that support accomplishing the mission. As such, setting clear outcomes and goals for what AI will help agencies achieve is the first step before making technology decisions.
]]>
However, pressure for today’s agencies to quickly demonstrate AI fluency makes this step easy to overlook. The conversation rapidly shifts towards discussing AI platforms, models, implementation plans and readiness assessments. Before long, agencies are deciding on technology before clearly understanding the outcomes they are aiming for.
My background includes time spent in military service, and when reflecting on our mission workflows, planning never began with a discussion about equipment but instead with identifying the objective. Then we moved onto discussing strategies and tactics like identifying the people, processes and technologies required to support the mission outcome. Federal agencies should approach leveraging AI the same way.
Before evaluating platforms, models or implementation strategies, leadership should be able to answer three questions:
What problem are we trying to solve?
What outcome are we trying to achieve?
What are our measures of success?
These questions extend beyond planning and establish the criteria against which readiness can actually be measured.
Without clear answers to these questions, agencies risk investing in capabilities that imply innovation but deliver limited operational value. However, answering these questions delivers mission clarity, which is not simply part of readiness, but the foundation that makes readiness possible.
Five questions to assess the value of your AI investments
Even in today’s environment of AI-forward approaches, AI is not always the right approach for solving the operational challenges and achieving the mission of every federal agency, so it is also crucial to determine if AI is the right approach for accomplishing it.
Here are five questions to help guide agencies through evaluating AI approaches.
]]>
What outcome are we trying to achieve? Tangible outcomes like improving citizen services, reducing administrative burden, accelerating analysis and strengthening decision-making are all worthwhile objectives, but they require different approaches and different measures of success. Meanwhile, a goal such as “implement AI” is too broad to be useful.
What constraints do we need to operate within? The advancement of AI technology is outpacing AI regulation, so setting guardrails like security requirements, compliance obligations, governance standards and budget realities should be at the top of the list when shaping decisions, not after a solution has already been selected.
What is our timeline? While some challenges require immediate action, others require longer-term modernization efforts, so understanding the timeline helps agencies separate urgent needs from future opportunities.
What resources can we realistically commit? Since technology alone rarely determines success, agencies also need the expertise, personnel, and organizational support needed to sustain an initiative beyond the pilot phase.
What is realistically achievable today? AI capabilities are rapidly evolving, and not all are mature enough for mission-critical deployment. As such, leaders should focus on starting with practical applications that can deliver measurable value rather than chasing the latest headlines or industry trends.
By addressing these questions, leaders move beyond surface-level technology assessments and uncover meaningful insights about whether approaches are aligned with the mission requirements to deliver meaningful outcomes.
AI readiness starts with clear business outcomes
With clearly defined goals and outcomes, agencies can meaningfully assess AI readiness. At this stage, infrastructure, data quality, governance, security and observability become critical considerations.
The technology sector is increasingly extending AI capabilities to devices and edge environments, reflecting a broader shift in how AI workloads are being distributed beyond the data center. Major technology providers, including Dell, are part of this trend, as AI workloads move closer to endpoints and mission environments. For many federal agencies, the future of AI will extend beyond centralized infrastructure to include secure, locally deployed applications.
While each of these components plays an important role in successful AI deployment, their value can only be assessed in the context of clearly defined objectives.
This is why the perceived value of readiness assessments can sometimes be limited. When conducted too early, agencies risk evaluating capabilities without first defining what those capabilities need to achieve. I have witnessed organizations spend months assessing tools and architectures while dedicating far less time to defining the operational problem they are trying to solve, ultimately slowing progress.
The agencies making meaningful progress with AI are not necessarily moving the fastest, but the most deliberately. They define missions and outcomes first, then build the architecture required to support them.
Execution requires a destination
I expect to see more AI-related executive orders in the future, and the pressure for federal agencies to move quickly on AI will continue. For agencies to set their teams up for long-term success, decision-makers need to establish what AI will help them accomplish. Defining the mission comes first and establishes the foundational operational framework for everything that follows.
Only then can technical and operational readiness be developed in a meaningful way that targets end goals by delivering measurable outcomes. It’s true that AI enthusiasm may start the conversation, but practicality and mission clarity are what turn that enthusiasm into effective readiness and execution.
Justin Kuiper is a military veteran and the director of architecture and engineering at Future Tech Enterprise, Inc.
]]>
Copyright
© 2026 Federal News Network. All rights reserved. This website is not intended for users located within the European Economic Area.