The recent executive order, Advancing Advanced Artificial Intelligence Innovation and Security, directed to federal agencies to strengthen their defenses against artificial intelligence-based threats, is the latest in a continued push for agencies to assess their AI readiness. In practice, however, these demands can come across as counterintuitive, leading to confusion between enthusiasm for AI and true readiness.
Enthusiasm reflects a desire to explore the technology, while readiness represents the framework and capabilities for deploying 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 AI applications: defining the mission that the AI will support.. Once clear goals and outcomes are set, technical and operational readiness efforts can work together to drive the outcomes the agency is trying to achieve.
The first step to AI readiness: Set a mission
One of the biggest mistakes organizations make is treating AI adoption as a mission, rather than viewing AI applications as strategic tools to support mission accomplishment. Therefore, setting clear outcomes and goals for what AI can help your agency accomplish is the first step before making technology decisions.
However, with the pressure on today’s government agencies to quickly prove AI fluency, this step is often overlooked. Conversations quickly move toward discussing AI platforms, models, implementation plans, and readiness assessments. Over time, agencies begin to make technology decisions before they have a clear understanding of the desired outcomes.
My background includes time spent in the military, and looking back at my mission workflow, planning didn’t start with a discussion of equipment, but with identifying objectives. We then moved on to discuss strategy and tactics, including identifying the people, processes, and technology needed to support mission outcomes. Federal agencies should similarly embrace AI.
Before evaluating platforms, models, and implementation strategies, leaders need to be able to answer three questions:
- What problem are we trying to solve?
- What results are we trying to achieve?
- What are the measures of success?
These questions extend beyond planning and establish a baseline against which readiness can actually be measured.
Without clear answers to these questions, agencies risk investing in capabilities that represent innovation but offer limited operational value. However, answering these questions provides mission clarity, which is not just part of readiness, but the foundation that makes it possible.
5 questions to assess the value of your AI investment
Even in today’s environment of AI-driven approaches, AI is not always the right approach to solving operational challenges and achieving every federal agency’s mission. Therefore, it is also important to determine whether AI is the right approach to achieve that.
Here are five questions to help government agencies evaluate AI approaches.
- What results are we trying to achieve? Tangible outcomes such as improving citizen services, reducing administrative burden, accelerating analysis, and enhancing decision-making are all worthy goals, but they require different approaches and different measures of success. On the other hand, a goal like “implement AI” is too broad to be useful.
- What constraints do we need to operate within? Advances in AI technology are outpacing AI regulations, so setting guardrails such as security requirements, compliance obligations, governance standards, and budget realism should be at the top of your list when making decisions, not after a solution has been selected.
- What is our timeline? Some challenges require immediate attention, while others require long-term modernization efforts, so understanding timelines can help agencies distinguish between immediate needs and future opportunities.
- What resources can you realistically commit? Because technology alone rarely determines success, agencies also need the expertise, talent, and organizational support necessary to sustain efforts beyond the pilot stage.
- What is realistically achievable today? AI capabilities are rapidly evolving, but not all are mature enough for mission-critical deployments. Therefore, instead of chasing the latest headlines and industry trends, leaders should focus on starting with practical applications that can deliver measurable value.
By addressing these questions, leaders go beyond surface-level technical assessments to uncover meaningful insights into whether approaches align with mission requirements to deliver meaningful outcomes.
AI readiness starts with clear business outcomes
With clearly defined goals and outcomes, agencies can meaningfully assess their AI readiness. Infrastructure, data quality, governance, security, and observability are important considerations at this stage.
The technology sector is increasingly extending AI capabilities to devices and edge environments, reflecting broader changes in how AI workloads are distributed beyond the data center. As AI workloads move closer to endpoints and mission environments, leading technology providers like Dell are also joining the trend. For many federal agencies, the future of AI will extend beyond centralized infrastructure to secure, locally deployed applications.
Each of these components plays a critical role in a successful AI implementation, but their value can only be assessed based on clearly defined objectives.
This may limit the value of readiness assessments. If implemented too early, agencies risk evaluating capabilities without first defining what they need to accomplish. I’ve seen organizations spend months evaluating tools and architectures, only to spend far less time defining the operational problems they’re trying to solve, ultimately slowing progress.
Government agencies that are making meaningful progress with AI are not necessarily the fastest moving, but they are the most intentional. First define your mission and outcomes, then build the architecture needed to support them.
Execution requires a destination
We expect to see more AI-related executive orders in the future, and the pressure on federal agencies to respond quickly to AI will continue. For agencies to set their teams for long-term success, decision makers need to establish what AI can help them accomplish. Mission definition occurs first, establishing the basic operational framework for everything that follows.
Only then can you achieve technical and operational readiness in a meaningful way that achieves your end goals by delivering measurable outcomes. Yes, enthusiasm for AI may be a conversation starter, but it’s practicality and mission clarity that turns that enthusiasm into effective preparation and execution.
Justin Kuiper is a military veteran and director of architecture and engineering. Future Tech Enterprise Co., Ltd.
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