Image by Wolfgang Rottmann on Unsplash
If I told you your company’s newest employee could leak private data, recommend fake books, or reject job applicants just because they’re older—without ever knowing it did anything wrong—you’d probably panic.
But that’s exactly what’s happening when organizations roll out generative AI tools without proper onboarding.
I’ve been watching this shift closely, and here’s the truth: Generative AI systems like large language models (LLMs) aren’t just “smart tools.” They’re dynamic, unpredictable, and—if left unsupervised—capable of messing things up in ways traditional software never could.
And yet, many companies are putting these systems to work without ever training them on company policies, legal boundaries, or even the right tone of voice for customer service. That’s not just lazy—it’s dangerous.
AI is everywhere now. But onboarding? Not so much.
In just a year, nearly a third of businesses have dramatically ramped up their use of generative AI. It’s not just another tech experiment anymore. We’re talking real deployments inside CRMs, customer support centers, and core business analytics.
But here’s the scary part: about 1 in 3 companies haven’t added any meaningful AI risk controls.
Think about that. We’re unleashing probabilistic systems—tools that can guess, drift, and, yes, hallucinate—with no safety net. Unlike hard-coded apps, generative AI adapts on the fly. That’s super powerful, but it also means things can go off the rails fast if you’re not watching.
And plenty of things already have.
Real-world facepalms: When AI goes wrong
Image by Edi Kurniawan on Unsplash
Let’s break down a few cautionary tales:
- Air Canada got slapped with a tribunal ruling after its website chatbot gave a passenger wrong refund info. The court said: yes, the chatbot gave the bad advice. And yes, the company is still 100% liable.
- A syndicated article in the Chicago Sun-Times and Philadelphia Inquirer shared a summer reading list generated by AI. The only problem? Some of the books didn’t exist. Public embarrassment, retractions, and firings followed.
- One company’s recruiting AI automatically rejected older applicants. That cost them a settlement with the Equal Employment Opportunity Commission.
- Employees at Samsung pasted sensitive code into ChatGPT—and boom, instant data leak. The company had to ban public AI tools on internal devices to recover.
None of these failures were technical glitches. They were human oversights. Caused by skipping AI onboarding.
Treat AI like a new hire—not a magic button
Here’s the mindset shift we need: Stop treating AI as a plug-and-play tool. Start treating it like a teammate who needs training, guardrails, and feedback.
That means:
- Defining a job role. What’s this AI actually doing? What are the inputs, outputs, limits, and escalation paths? A legal AI copilot, for example, shouldn’t give legal advice—it should flag contract concerns and escalate the tricky stuff.
- Providing context. Don’t expect the AI to know your company policies or past decisions. Use retrieval-augmented generation (RAG) instead of just fine-tuning. It’s cheaper, more transparent, and feeds AI with real, vetted knowledge from your own systems.
- Running simulations. Before deploying to real users, test outputs in high-fidelity sandboxes. Stress-test reasoning, tone, and edge cases. Morgan Stanley did this with its GPT-4 assistant and hit 98% advisor adoption by the time it went live.
- Cross-functional mentoring. Set up feedback loops. Security teams enforce red lines. End-users flag tone issues. Designers fix unclear prompts. Everyone’s a teacher.
The learning never stops: Feedback, audits, and updates
Once the AI is live, that’s just the beginning. Continuous improvement is essential.
- Monitor everything. Track accuracy, user satisfaction, escalation rates. Watch for model drift—AI gets outdated when your docs and rules change.
- Close feedback loops. Let users flag AI mistakes, correct behavior, and feed those signals back into the model’s prompts or knowledge base.
- Audit regularly. Schedule safety and factual accuracy reviews. Think of them as performance evaluations for your AI.
- Plan ahead. Update or replace old models just like you manage people transitions. Archive lessons, prompts, and knowledge sources for reuse.
The rise of AI enablement managers and PromptOps
Because of all this complexity, new roles are popping up—like AI enablement leaders and PromptOps specialists. These folks curate prompts, manage AI connections to business systems, and help maintain standards across teams. They’re the teachers who keep AI systems smart, safe, and aligned.
Companies like Microsoft are building entire playbooks around this, with governance templates and internal “centers of excellence” to manage Copilot systems.
Want AI that actually helps? Train it like you would a person.
Here’s a simple checklist for anyone rolling out—or rescuing—a generative AI system:
- Write the job description. Define outputs, tone, and what happens when things get weird.
- Ground the model in your data. Use RAG or tools like Model Context Protocol, not whatever’s on the internet.
- Build a simulator. Test everything first: accuracy, tone, weird edge cases.
- Add safety tools. Use audit trails, data protections, and content filters.
- Gather feedback in-product. Don’t wait for disaster—get flagged issues in real time.
- Review and retrain over time. Do monthly checks and quarterly audits to keep things on track.
Final thought
In the near future, most knowledge workers will have an AI sidekick. The companies that train those sidekicks well—from day one—will move faster, avoid costly failures, and earn their employees’ trust.
Generative AI doesn’t just need good data. It needs a job, a coach, and a clear understanding of its purpose.
Talk to it like a teammate. Because in many ways, that’s exactly what it is.
Keywords: AI onboarding, generative AI, AI risk controls, AI deployment, AI training, AI enablement
