You’re in the cockpit of a small plane, cruising a few thousand feet in the air. Then, out of nowhere, the airspeed dips and an alarm rings out. The nose drops, and you're in a full-out stall by the time instinct kicks in. You pull back on the yoke, trying to steady the plane, stop the descent and patch things up midair. But that’s exactly the move that seals your fate, sending you into a deeper spiral.
Without the right training or information, even a split-second decision can go sideways. That’s the danger, not just in flight, but also in artificial intelligence (AI). When agencies deploy without meticulously ensuring their data is complete, accurate and properly governed, they’re pulling back on the yoke without realizing they’re already in a stall. Systems can misfire, causing detrimental blows to reputation and trust. Worst of all, once you’re off the ground, small mistakes become harder to correct.
AI systems that operate in the real world rely on structured, usable inputs. Without a solid foundation—or what we might call completeness, correctness and conditions—models are prone to failure. Just like flying an aircraft, deploying AI requires a methodical, pre-flight approach. Pilots are trained to rely on their checklists, not their instincts and AI systems need that same discipline before they ever go into production.
Completeness: Having the Whole Picture
Before a plane ever lifts off, the pilot walks the tarmac to inspect the fuel, the flaps, the weather and the weight distribution. It’s not only about how much information they have, but whether they have everything they need to fly safely. AI needs the same assurance. Data inputs should cover all relevant factors, especially those that are easy to overlook, meaning pulling in seasonal patterns, edge cases or regional differences. Program leads and frontline staff often know where the gaps are and can answer the question: “Is this enough to make a good decision?”
Correctness: Trusting Your Information
A pilot trusts their instruments—airspeed, altitude, fuel levels—to make decisions in-flight. But if just one reading is wrong, everything can spiral. The same applies to AI. Outdated or inaccurate data might look clean on the surface, but it can quietly distort decisions downstream. Organizations must set clear standards for how often data is refreshed and validated. “Clean” doesn’t always mean “current.” If your system is acting on real-time information, that information needs to be accurate, not just plausible.
Conditions: Defining Usage and Boundaries
Conditions ensure the system knows how data is supposed to behave. Before takeoff, every control input must respond as expected. Pilots check not only if something works, like the throttle and trim, but how and under what limits. For AI systems, that means enforcing governance and access control. Metadata, traceability and usage rules must accompany the data so agencies can see its origin, who accessed it, and whether it’s authorized for the task at hand. Without this, even the best models can violate privacy, pull from restricted sources or make decisions they weren’t cleared to make.
Ensuring AI Readiness: The Checklist Approach
A checklist-based mindset creates structure where instinct might fail. Pilots don’t run through their list because they’ve forgotten how to fly—they use it because even the best can miss something under pressure. A pre-deployment AI checklist does the same by reducing human error, increasing consistency and giving teams a repeatable framework to work from. Most importantly, it ensures that completeness, correctness and conditions aren’t afterthoughts—they’re built into the deployment process from the start.
As AI becomes more embedded in government services, its reliability depends on data readiness. A structured checklist improves outcomes, protects trust and mitigates risks before they escalate. AI may be evolving quickly, but the fundamentals still matter. Every successful takeoff starts on the ground—with the right checks in place.
Before moving forward, review the pre-flight AI checklist below and ask yourself: is your agency truly ready for takeoff?
“Pre-flight Data Checklist”
DO WE HAVE THE WHOLE PICTURE? (Completeness)
Are we including all the information that matters for the task or decision?
Have we accounted for unusual cases, special populations or edge situations?
Does this data reflect what’s actually happening—on the ground, across regions, over time?
Are we confident that we’re pulling in data from the right systems?
CAN WE TRUST THE INFORMATION? (Correctness)
Has the data been checked for accuracy?
Is it current—not from last year or last quarter?
Are we testing the AI system using today’s conditions, not just past patterns?
Have we reviewed key details—like dates, names, eligibility—for possible errors?
DO WE KNOW WHERE THE DATA CAME FROM AND WHO CAN USE IT? (Conditions)
Can we trace where each piece of data came from?
Do we know who has access to it—and who shouldn’t?
Are there clear rules about how this information can be used?
Are those rules being followed automatically, not just written in a policy document?
IS THERE A SAFETY PLAN IN PLACE? (Operational Readiness)
Will a person review high-impact decisions before they go into effect?
Are there clear backup plans if the system makes a mistake?
Does everyone involved understand what the AI can and cannot do?
Do we have a plan for checking how it’s performing once it goes live?
WILL WE KEEP IMPROVING OVER TIME? (Feedback and Learning)
Are we reviewing results to see if the system is doing what we expect?
Do we have a way to fix things if it starts to go off track?
Is someone clearly responsible for watching how the system learns and changes?
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