As more companies rapidly start using Gen AI, it’s important to avoid a big mistake that can impact its effectiveness: proper onboarding. Companies spend time and money training new employees to succeed, but when using large-scale language model (LLM) helpers, many companies treat them like a simple tool that needs no introduction.
This is more than just a waste of resources. That’s dangerous. According to the study, AI progressed rapidly from testing to real-world use from 2024 to 2025. Almost one-third of companies It has been reported that usage and acceptance numbers have increased sharply compared to the previous year.
Stochastic systems require governance, not wishful thinking
Unlike traditional software, generative AI is probabilistic and adaptive. It learns from interactions, can drift as data and usage changes, and operates in the gray area between automation and agency. Treating it like static software ignores reality. Without monitoring and updating, models degrade and produce incorrect outputs. This is a widely known phenomenon. model drift. Gen AI also lacks built-in features Organizational intelligence. A model trained on internet data may write Shakespearean sonnets, but it won’t know the escalation path or compliance constraints unless you tell it. Regulators and standards bodies are beginning to push for guidance because these systems operate dynamically and can hallucinate, mislead, or leak data if left unchecked.
The real cost of skipping onboarding
When LLMs hallucinate, misinterpret tone, divulge sensitive information, or amplify bias, the cost is tangible.
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Misinformation and liability: A Canadian court has held Air Canada liable after a chatbot on its website provided incorrect policy information to passengers. The ruling made clear that companies remain liable for what their AI agents say.
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Embarrassing hallucination: In 2025, the syndicated “Summer Reading List” Chicago Sun-Times and philadelphia inquirer I recommended a book that doesn’t exist. The author used AI without proper verification, prompting retraction and termination.
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Massive bias: The first AI discrimination settlement by the Equal Employment Opportunity Commission (EEOC) involved a hiring algorithm that automatically rejected older applicants, highlighting how unmonitored systems can amplify bias and create legal risks.
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Data breach: After an employee pasted confidential code into ChatGPT, Samsung temporarily banned the use of public AI tools on corporate devices, a mistake that could be avoided with better policies and training.
The message is simple. Unonboarded AI and unmanaged use exposes you to legal, security, and reputational risks.
Treat AI agents like new employees
Companies need to onboard AI agents as carefully as they would onboard humans, using job descriptions, training curricula, feedback loops, performance reviews, and more. This is a cross-functional effort that spans data science, security, compliance, design, human resources, and the end users who use the system on a daily basis.
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Role definition. It details scopes, inputs/outputs, escalation paths, and acceptable failure modes. For example, a legal co-pilot can summarize contracts and surface dangerous clauses, but must avoid final legal judgments and escalate edge cases.
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Training according to the situation. Although it requires some fine-tuning, search extension generation (RAG) and tool adapters are more secure, cheaper, and auditable for many teams. RAG maintains a model based on the latest vetted knowledge (documents, policies, knowledge bases) to reduce illusions and improve traceability. The new Model Context Protocol (MCP) integration makes it easy to connect CoPilot to enterprise systems in a controlled way, bridging models with tools and data while maintaining separation of concerns. Salesforce’s Einstein Trust Layer shows how vendors are formalizing secure grounding, masking, and audit controls for enterprise AI.
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Simulation before production. Don’t do the initial “training” of your AI on real customers. Build a high-fidelity sandbox, stress test tone, inference, and edge cases, and evaluate with human graders. Morgan Stanley built an evaluation plan for the GPT-4 Assistant, with advisors and prompting engineers evaluating responses and refining prompts before widespread deployment. result: >98% adoption between the advisor team after meeting quality standards. Vendors are also moving toward simulation, with Salesforce recently emphasizing digital twin testing to safely rehearse agents for realistic scenarios.
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4) Cross-functional mentorship. Treat early usage as follows: Bidirectional learning loop: Domain experts and front-line users provide feedback on tone, accuracy, and usefulness. Security and compliance teams enforce perimeters and redlines. Designers shape frictionless UIs that encourage appropriate usage.
Feedback loops and performance reviews forever
Onboarding doesn’t end with go live. The most meaningful learning begins rear Expand.
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Monitoring and observability: Log output, track KPIs (accuracy, satisfaction, escalation rate), and monitor degradation. Cloud providers now offer observability/evaluation tools that allow teams to detect drift and regression in production, especially in RAG systems where knowledge changes over time.
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User feedback channels. Provides in-product flagging and structured review queues to allow humans to coach models. Then input these signals into a prompt, RAG source, or tweak set to close the loop.
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Regular audits. Schedule coordination checks, fact audits, and safety assessments. For example, Microsoft’s Responsible AI Playbook for Enterprise emphasizes governance and gradual deployment with executive visibility and clear guardrails.
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Model succession planning. As laws, products, and models evolve, plan for upgrades and retirements the same way you plan for people’s transitions. Run redundant tests and port your organizational knowledge (prompts, assessment sets, search sources).
Why is this urgent now?
Gen AI is no longer an “innovation shelf” project, but is embedded in CRMs, support desks, analytical pipelines, and executive workflows. Banks like Morgan Stanley and Bank of America are focusing AI on internal co-pilot use cases to improve employee efficiency while limiting customer-facing risks, and their approach relies on structured onboarding and careful scoping. Meanwhile, security leaders say the AI generation is ubiquitous but still One-third of adopters do not have basic risk mitigation in placegaps that lead to shadow AI and data exposure.
AI-native employees expect more: transparency, traceability, and the ability to shape the tools they use. Organizations that deliver this through training, clear UX affordances, and responsive product teams achieve faster adoption and fewer workarounds. When the user trusts the co-pilot, use it; if not, bypass it.
As onboarding matures, we expect the following AI enablement manager and PromptOps Specialist Create more org charts, curate prompts, manage acquisition sources, run assessment suites, coordinate updates across departments, and more. Microsoft’s internal Copilot deployment demonstrates operational discipline: centers of excellence, governance templates, and executive deployment playbooks. These practitioners are the “teachers” who keep AI aligned with rapidly changing business objectives.
Practical onboarding checklist
If you’re looking to deploy (or rescue) an Enterprise co-pilot, start here.
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Write the job description. Scope, input/output, tone, redline, escalation rules.
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Ground the model. Implement a RAG (and/or MCP-style adapter) to connect to privileged access-controlled sources. Prefer dynamic grounding over extensive tweaking when possible.
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Build a simulator. Create scripted and seeded scenarios. Measure accuracy, coverage, tone, and safety. Human approval is required to exit the stage.
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A ship with guardrails. DLP, data masking, content filters, and audit trails (see vendor trust layers and responsible AI standards).
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Instrument feedback. In-product flagging, analytics, and dashboards. Schedule weekly triage.
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Review and retrain. Monthly reconciliation checks, quarterly fact audits, and planned model upgrades – side-by-side A/B to prevent setbacks.
In a future where every employee has an AI teammate, organizations that take onboarding seriously will move faster, more securely, and with greater purpose. Gen AI requires more than just data and computing. You need guidance, goals, and a growth plan. Treating AI systems as teachable, improvable, and responsible team members turns hype into habitual value.
Dhyey Mavani accelerates generative AI at LinkedIn.
