AI agents are no longer experimental tools tucked inside innovation labs. They are drafting contracts, recommending prices, screening candidates and reshaping how decisions are made across companies. As adoption accelerates, however, many organizations are discovering a sobering truth: Knowing how to use AI is not the same as knowing when not to.
Members of the Senior Executive AI Think Tank—a curated group of technologists, executives and strategists shaping the future of applied AI—agree that the next frontier of AI maturity is literacy rooted in judgment. Training programs must now prepare employees not just to operate AI agents, but to question them, override them and escalate concerns when outputs conflict with human values, domain expertise or organizational risk.
That concern is well founded: Organizations relying on unchecked automation face higher reputational and compliance risk, even when systems appear highly accurate. Similarly, confident but incorrect AI outputs—often called “hallucinations”—are becoming one of the biggest enterprise risks as generative AI scales.
Against that backdrop, Senior Executive AI Think Tank members outline what effective AI literacy training must look like in practice—and why leaders must act now.
“To build real AI literacy, companies need to make sure it is OK—and even good—to pause and ask, ‘Does this make sense?’”
Making It Safe to Pause and Question
One of the most overlooked barriers to AI literacy is not technical—it’s cultural. As AI agents accelerate workflows, employees often feel subtle pressure to accept outputs quickly rather than question them. Daria Rudnik, Team Architect and Executive Leadership Coach at Daria Rudnik Coaching & Consulting, argues that effective AI training must explicitly legitimize hesitation.
“To build real AI literacy, companies need to make sure it is OK—and even good—to pause and ask, ‘Does this make sense?’” Rudnik says. Without that permission, even well-trained employees may override their own judgment in favor of speed.
Rudnik emphasizes that training should help teams recognize when AI outputs conflict with domain expertise, feel incomplete or raise concerns about rights or well-being. Just as critical, she notes, are clearly defined escalation pathways.
“When people are taught not just to use AI but to challenge it—and know exactly where to take their concerns—that’s when the organization becomes truly AI-ready,” she says.
Teaching Skepticism as a Core Skill
After years of evangelizing AI adoption, organizations are entering a more sober phase—one defined by discernment rather than enthusiasm. According to Pradeep Kumar Muthukamatchi, Principal Cloud Architect at Microsoft, the biggest gap in most training programs is not capability, but skepticism.
“We have moved beyond the initial excitement with AI,” Muthukamatchi says. “The main challenge now is to develop skepticism, not just encourage adoption.” The danger, he explains, lies in AI’s ability to generate outputs that appear authoritative while quietly containing errors.
To counter that risk, Muthukamatchi advocates for adversarial training methods, including role-playing scenarios where AI produces plausible but incorrect answers. These exercises force employees to practice verification rather than assumption.
“The aim is for employees to treat every AI output as a suggestion that must be verified by a human,” he says—a principle that defines true AI maturity.
Turning Users Into Auditors
For many organizations, AI training still treats employees as operators rather than overseers. Sathish Anumula, Sr. Customer Success Manager and Architect for IBM Corporation, believes that approach is fundamentally misaligned with how AI actually works.
“Training shouldn’t just teach people how to use LLMs,” Anumula says. “It should explain that they are probabilistic predictors, not fact engines.” That distinction, he notes, is essential for helping employees recognize hallucinations and subtle inaccuracies.
Anumula supports instituting a formal “trust but verify” rule, particularly for high-stakes decisions. Equally important is empowering employees with stop-the-line authority—clear permission to halt AI-driven processes they believe are unsafe. In his view, confidence to challenge AI matters far more than speed of adoption.
Designing Role-Based AI Literacy
As AI spreads across departments, the risks it introduces vary widely by role. Egbert von Frankenberg, CEO of Knightfox App Design Ltd., says organizations must abandon generic training in favor of role-based literacy programs.
“Organizations should approach AI literacy as a continuous, role-based program that teaches not only how to use AI, but when to question it,” von Frankenberg says. At a minimum, he argues, every employee should understand fundamentals like hallucinations, bias and privacy. Beyond that, managers, technical teams and compliance leaders need deeper training tailored to oversight and incident response.
Scenario simulations, he adds, are particularly effective. By exposing teams to real-world failure modes in a controlled environment, organizations help employees build muscle memory for escalation—before real consequences are at stake.
Building Productive Skepticism at Scale
While many leaders tell employees to “use judgment” with AI, Jim Liddle, serial entrepreneur and enterprise AI strategist, warns that such guidance collapses at scale: “AI literacy isn’t about mastering the tool—it’s about knowing where the tool ends and your own judgment begins.”
He advocates for what he calls “productive skepticism,” a calibrated approach that avoids both blind trust and reflexive doubt. To achieve that balance, organizations must define clear escalation frameworks and deliberately train on examples where AI fails plausibly—outputs that look right until examined closely. These examples, Liddle says, are what teach pattern recognition in real-world use.
Defining Human Checkpoints in High-Stakes Work
In operational environments like retail and supply chain, unchecked automation can quickly erode trust. Uttam Kumar, Engineering Manager at American Eagle Outfitters, says AI literacy must be designed around “critical evaluation breakpoints” to “help employees spot bias, errors or ethical issues rather than trust automation blindly.”
Training, Kumar argues, should clearly specify which AI outputs require manual review—pricing changes, sensitive personalization or supply chain decisions among them. These checkpoints reinforce accountability and protect brand integrity while reminding employees that AI is a co-pilot, not a decision-maker.
Choosing Clarity Over Capability
As AI agents accelerate enterprise systems, Dileep Rai, Manager of Oracle Cloud Technology at Hachette Book Group (HBG), warns that speed without understanding creates fragility. “AI literacy must center on one principle: clarity over capability,” Rai says.
In complex environments such as ERP, finance and supply chain operations, AI may be fast but context-blind. Rai believes training should help teams identify friction points and slow down when decisions carry ethical or operational weight. Only then does AI function as a true collaborator rather than an unquestioned authority.
“Teams should be trained to recognize when an AI system may be wrong, biased or outside its domain.”
Pairing Fluency With Oversight
Hands-on skill matters—but it is only one component of AI literacy, according to Sinan Ozdemir, AI thought leader and Founder of Crucible. Ozdemir recommends pairing technical fluency with oversight and continuous learning.
“Teams should be trained to recognize when an AI system may be wrong, biased or outside its domain,” he says, which helps keep humans firmly in control when it matters most.
Regular refreshers, shared case studies and internal demo days also help organizations keep pace with rapidly evolving models while reinforcing safe use.
Training the Supervisor Mindset
Bhubalan Mani, Lead for Supply Chain Technology and Analytics at GARMIN, believes the most important shift in AI literacy is psychological.
“True AI literacy isn’t about prompt engineering,” Mani says. “It’s about shifting from operator to auditor.”
He suggests designing training around the “Supervisor Mindset”: “Just as managers are accountable for staff, employees must learn to audit an agent’s chain of thought, not just its output.”
He recommends red-team simulations that deliberately inject logic errors or hallucinations into AI workflows. These exercises build what Mani calls the “override muscle,” preparing employees to catch subtle mistakes before they propagate into operational risk.
Embedding Accountability and Psychological Safety
Even the best escalation frameworks fail if employees fear consequences for speaking up. Mohan Krishna Mannava, Data Analytics Leader at Texas Health, says AI literacy must be anchored in accountability and psychological safety.
Training programs, he argues, should emphasize verification checkpoints and reward employees who challenge flawed outputs. “That’s how you transform people into human-in-the-loop auditors,” Mannava says—not by compliance, but by recognition.
Balancing Technical Skills With Ethical Judgment
Roman Vinogradov, VP of Product at Improvado, stresses that AI literacy must balance experimentation with ethical awareness.
“Start by developing a comprehensive training program that balances technical skills with critical thinking,” he says. Effective programs, he continues, combine AI fundamentals workshops with real-world scenarios that force employees to question outputs and discuss bias, limitations and trade-offs.
Equally important is maintaining open feedback loops, where employees can share AI-related concerns and insights across teams. That transparency, Vinogradov notes, fuels both innovation and accountability.
Building AI Champions Across the Organization
“Effective AI literacy training requires a multi-layered curriculum,” says Chandrakanth Lekkala, Principal Data Engineer at Narwal.ai. He recommends hands-on workshops, critical thinking exercises, clear escalation protocols and role-specific training. But for him, sustainable AI literacy depends on internal ownership that reinforces AI champions embedded across departments.
These champions create feedback loops, surface issues early and reinforce the principle that AI augments—rather than replaces—human expertise. Questioning AI, Lekkala says, must be treated as a professional responsibility, not a disruption.
“The deeper skill is recognizing when an agent’s confident answer is built on shaky ground.”
Teaching Judgment, Not Compliance
Ultimately, AI literacy is a leadership issue. Aditya Vikram Kashyap, Vice President of Firmwide Innovation at Morgan Stanley, believes organizations gain advantage not from faster adoption, but from better judgment.
“AI literacy must evolve from tool training to judgment training,” Kashyap says. “Companies should teach the mechanics of prompting and workflows, but the deeper skill is recognizing when an agent’s confident answer is built on shaky ground.”
The most capable teams know when to accelerate with AI—and when to tap the brakes—protecting accuracy, accountability and human intent at every step.
Quick Tips for Effective AI Literacy
- Normalize pausing and questioning. Make skepticism a cultural expectation, not a personal risk.
- Teach AI failure modes explicitly. Show employees how AI fails convincingly, not just obviously.
- Turn users into auditors. Require verification for high-stakes outputs.
- Create role-based AI training. Tailor depth and oversight to responsibility.
- Define escalation frameworks. Vague judgment calls don’t scale.
- Build human checkpoints. Specify when automation must stop.
- Choose clarity over speed. Slow down where stakes are high.
- Pair fluency with oversight. Skills without governance create risk.
- Train the supervisor mindset. Employees must audit, not defer.
- Protect psychological safety. Reward responsible escalation.
- Balance ethics with experimentation. Curiosity and caution can coexist.
- Establish AI champions. Create feedback loops across teams.
- Teach independent thinking. Judgment—not compliance—is the goal.
Letting Humans Take the Lead
As AI agents move from assistants to collaborators, organizations must train employees not to follow machines—but to think alongside them. True AI maturity emerges when people are trained—and culturally supported—to question outputs, override automation and take accountability for outcomes.
Empowering employees to take control of AI in this way builds more resilient and more trusting teams. And in doing so, leaders send a powerful message: AI may accelerate the work, but humans remain responsible for the decisions. And in an era of increasingly capable agents, that distinction makes all the difference.
