Most AI training begins with the prompt. Employees learn how to ask better questions, add context, refine instructions, and turn an initial response into something useful. These skills matter, but they address only one side of working with AI: how to get an answer.
They do not necessarily teach people how to decide whether that answer is worth using.
In this episode of the #shifthappens podcast, Chanel Turner, CEO of The Turner Point, draws a line between AI awareness and AI literacy. Knowing that AI exists, opening a tool, or entering a query does not make someone fully AI literate. Literacy means understanding what AI can and cannot do, where it fits into everyday work, and, importantly, when to trust it and when to push back.
That judgment is what turns access to AI into literacy.
Move Beyond the Query Stage
Most employees no longer need to be convinced that AI exists. Awareness is widespread, but meaningful use is a different challenge.
“People know that AI exists. They have that awareness, but the issue is actually using it to change behaviors,” Chanel shares.
An isolated query does not necessarily change how work gets done. Someone can ask AI a question, summarize a document, or generate an image without learning how to integrate the technology into their work.
Chanel sees a gap between querying AI and becoming fully AI literate. Moving beyond that first stage requires more than increasingly sophisticated prompts. Employees must understand what context AI needs, evaluate whether its response is appropriate, and recognize when the system has missed something they know to be true.
That changes the objective of AI literacy. The goal cannot simply be getting employees to use the technology more often. It should be developing users who can assess what comes back.
Sometimes that means accepting the answer, refining it, or even disagreeing with AI altogether.
Stay the Subject Matter Expert
Chanel illustrates that distinction through an interaction she had with Microsoft Copilot while creating training materials for Microsoft Planner. She wanted a visual involving Planner, but Copilot repeatedly returned an outdated Planner icon.
Instead of accepting the output, she kept challenging it. Eventually, she showed Copilot the correct icon herself.
“I said, ‘No, you are incorrect.’ And I took a screenshot, uploaded it. They’re like, ‘You’re 100% right.’ And then it came out within 30 seconds, after this 30-minute interaction. After 30 seconds, it gave me exactly what I needed.”
The story demonstrates an important difference between using AI and deferring to it. Copilot could help produce the asset, but Chanel had the product knowledge required to recognize that the result was wrong.
AI does not remove the user's responsibility to review what it produces. The user still brings the organizational knowledge, professional expertise, and task-specific context needed to determine whether the response makes sense.
Make Good AI Judgment a Daily Practice
Knowing when to challenge an answer becomes useful when employees have opportunities to practice that judgment in real work. Chanel’s advice points to several behaviors leaders and teams can reinforce.
Read the Output Before You Run with It
Treat every AI response as a draft to evaluate, not an answer to copy, so subject matter expertise stays in the loop.
Chanel’s Copilot experience shows why. The tool produced an answer, but she knew enough about the subject to identify what was wrong. Employees need that same willingness to stop, review, and correct before an AI-generated output moves further into the workflow.
Lead AI Adoption by Using It Out Loud
Show your team how you work with AI on real tasks, including the parts where the output was wrong, and you fixed it.
Leadership visibility helps normalize the less polished side of AI use. Employees see that effective use does not mean getting the right result immediately. It can involve reviewing an answer, identifying missing context, correcting it, and trying again.
Being open about that process can also make experimentation feel more approachable. Instead of positioning AI proficiency as instant expertise, leaders can show that they are learning how and where the technology fits into their own work.
Build Confidence Through Friction
Push back, correct, and re-prompt when the output misses the mark. That practice helps employees understand what context AI needs, where its limitations appear, and where their expertise matters most.
That kind of friction can be productive. Each correction gives the user another opportunity to understand how to work with the technology rather than simply receive information from it. Over time, those experiences can help move AI from something employees experiment with occasionally to something they use more deliberately in their daily work.
Reward Curiosity Over Speed
Give teams room to test, fail, and ask questions so that an AI rollout produces people who know how to work with the technology, rather than nervous spectators waiting for someone else to figure it out.
This also reframes correction. Finding a bad answer is not necessarily evidence that AI adoption has failed. Recognizing the problem, understanding why it happened, and knowing what to do next can be evidence that employees are becoming more capable users.
Creating that environment makes curiosity more valuable than appearing to have AI figured out from day one.
Know When the Answer Is Not Good Enough
Organizations do not need employees to argue with AI for the sake of arguing. They need employees who can recognize when an answer does not fit the facts, the task, or the context.
That requires a broader definition of AI competence. A capable user knows how to write a prompt and how to inspect the response. They know when another iteration can improve it, when information needs verification, and when their own expertise should override the system's output.
The next stage of AI literacy cannot be measured only by how many people have access to AI or how frequently they use it. What matters is what happens after the answer appears. Prompting starts the interaction. Knowing when to trust, correct, or disagree with the response is what makes that interaction useful.
Soundtrack of Shift
Chanel’s Soundtrack of Shift is “Perfect Peace” by Andraé Crouch. She chose the song for both its distinctive sound and its enduring message. For Chanel, it reflects a lesson she carries into digital transformation and AI adoption: leadership during change is not about having all the answers, but maintaining clarity and focus when everything around you feels uncertain.
Explore more soundtracks shaping how leaders approach change and transformation today.
Episode Resources
#shifthappens Research: 2026 State of AI
#shifthappens Insights:
#shifthappens Podcasts:
Why Most Enterprise AI Training Fails — and How Learning Design Turns Use Into Real Adoption
AI Readiness Starts Before AI: Identity, Endpoints, and Security First
AI Won’t Fix What’s Already Broken: Rethinking the Operating Model for Scale
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Chanel Turner on LinkedIn
