Fall is approaching, kids are heading back to school, and football is starting. In my house, this is also the time of year when we go through the kids’ clothes. We figure out what still fits, what has been outgrown, what is worn out and what we need before cooler weather arrives. Some things stay. Some get replaced. Others are no longer needed at all.
AI adoption is moving quickly, and organizations are continually identifying new opportunities to apply AI to business and mission challenges. But as the number of AI pilots, agents, automations, and use cases grows, it’s important to periodically take stock of what they already have.
What still fits? What is delivering value? What has become obsolete? Where are there gaps? And where have changing mission requirements created entirely new opportunities?
An AI inventory can help answer those questions. Making that inventory a recurring exercise can transform AI from a collection of individual technology initiatives into a managed portfolio of capabilities that continually evolve with the mission.
Start by Taking Stock of What Exists
The first step is to understand what you have.
Organizations should maintain an inventory of their AI use cases. If one does not exist, now is a good time to build it. And if one already exists, don’t assume it is complete.
AI adoption frequently happens across organizational boundaries. A business unit may have developed a generative AI assistant. An operations team may be experimenting with intelligent automation. A program office may have implemented predictive analytics.
This makes stakeholder engagement essential. Talk with business owners, program leaders, technology teams, and security and governance personnel to uncover AI implementations that may not have made it into the official inventory.
The inventory should contain enough information to support decisions. For each use case, consider documenting the business or mission owner, problem being addressed, expected outcomes, users, data sources, technology or model being used, operational status, costs, dependencies, security and privacy considerations, risks, and ongoing maintenance requirements.
Evaluate the Value Being Delivered
Once you know what exists, the next question is if it’s working?
Every AI use case should be connected to an intended outcome. Perhaps the objective was reducing processing time, increasing workforce productivity, reducing errors, accelerating analysis or improving mission responsiveness. You should compare actual performance against those expectations.
If the expected outcomes were never clearly identified, establish them now. A use case without a measurable outcome can easily become technology in search of a purpose.
The inventory process creates an opportunity to ask whether AI is actually the right technology for each problem. There is an old saying that if all you have is a hammer, every problem looks like a nail. AI can quickly become a hammer.
Not every process needs generative AI, machine learning or an autonomous agent. In some situations, workflow automation, robotic process automation, business rules, traditional analytics or simply redesigning the process may produce better results with less complexity.
The objective should be to maximize mission value.
Every AI Use Case Has a Lifecycle
One factor that is often underestimated during AI adoption is maintenance.
Every AI capability creates some degree of operational debt. Models change. Data changes. Business processes change. Interfaces change. Security requirements evolve. Vendors release new capabilities. Regulations and policies are updated. And the mission itself continues to move.
For more advanced AI applications, performance can also degrade as the environment changes. Outputs need to be monitored, data pipelines maintained, integrations updated, risks reassessed, and controls tested. Deploying an AI capability is not the finish line.
An AI solution delivering modest value but requiring significant maintenance may no longer make sense. Conversely, a relatively simple capability that consistently saves thousands of staff hours may deserve additional investment and expansion.
Taking inventory creates the opportunity to continue, improve, consolidate, replace, or retire AI capabilities based on evidence rather than momentum.
Assess Where AI Can Add New Value
Once existing AI capabilities have been evaluated, examine current business and mission processes through a fresh lens as these may represent opportunities for AI.
- Where are employees spending significant time performing repetitive knowledge work?
- Where are decisions delayed because people must gather information from multiple systems?
- Where are large amounts of unstructured information being manually reviewed?
- Where are bottlenecks limiting mission speed?
At the same time, consider what has changed since the last inventory. New mission requirements may have emerged. Customer expectations may have shifted. New data may be available. Technology capabilities that were immature six months ago may now be viable.
This is one reason an AI inventory should never be viewed as a one-time compliance exercise. It is part of an iterative process for continuously aligning technology with mission requirements.
A good question is, “Where can AI materially improve the outcome?” This starts with the mission problem and works backward to the technology rather than beginning with the technology and searching for somewhere to deploy it.
Look Beyond Today’s Requirements
The most mature AI inventories also connect to strategy and forward planning.
Organizations typically spend significant effort determining what they need to accomplish over the next year, three years or five years. AI should be incorporated into those discussions as one of the capabilities available to achieve broader organizational objectives.
Consider what future requirements are already visible on the horizon. This shifts the conversation from AI adoption toward AI-enabled mission design.
Make AI Inventory a Recurring Habit
The value of taking inventory does not come from doing it once.
AI capabilities, business processes, technologies, risks and mission requirements are changing rapidly. Organizations need an iterative process for continuously assessing their portfolio of AI use cases. A recurring AI inventory strengthens governance because leaders gain visibility into what is being used and why.
Within RELI, our AI governance process includes this recurring review of the AI inventory. It helps prevent AI sprawl by identifying redundant or disconnected solutions. It improves investment decisions by connecting AI spending to measurable outcomes. It identifies emerging risks and maintenance requirements. And it creates a mechanism for continuously discovering new opportunities.
It acknowledges a simple reality: what fits the mission today may not fit tomorrow.
Just as we periodically look through the closet and discover that last year’s clothes no longer fit, organizations will inevitably find AI use cases that have been outgrown, capabilities that need to be replaced, and new requirements that demand something different.