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As more companies quickly begin using gen AI, it\u2019s important to avoid a big mistake that could impact its effectiveness: Proper onboarding. Companies spend time and money training new human workers to succeed, but when they use large language model (LLM) helpers, many treat them like simple tools that need no explanation. <\/p>\n
This isn't just a waste of resources; it's risky. Research shows that AI has advanced quickly from testing to actual use in 2024 to 2025, with almost a third of companies<\/b> reporting a sharp increase in usage and acceptance from the previous year.<\/p>\nProbabilistic systems need governance, not wishful thinking<\/b><\/h2>\n
Unlike traditional software, gen AI is probabilistic and adaptive. It learns from interaction, can drift as data or usage changes and operates in the gray zone between automation and agency. Treating it like static software ignores reality: Without monitoring and updates, models degrade and produce faulty outputs: A phenomenon widely known as model drift<\/b>. Gen AI also lacks built-in <\/b>organizational intelligence. A model trained on internet data may write a Shakespearean sonnet, but it won\u2019t know your escalation paths and compliance constraints unless you teach it. Regulators and standards bodies have begun pushing guidance precisely because these systems behave dynamically and can hallucinate, mislead or leak data if left unchecked.<\/p>\nThe real-world costs of skipping onboarding<\/b><\/h2>\n
When LLMs hallucinate, misinterpret tone, leak sensitive information or amplify bias, the costs are tangible.<\/p>\n
Misinformation and liability:<\/b> A Canadian tribunal held Air Canada liable after its website chatbot gave a passenger incorrect policy information. The ruling made it clear that companies remain responsible for their AI agents\u2019 statements.<\/p>\n<\/li>\n Embarrassing hallucinations:<\/b> In 2025, a syndicated \u201csummer reading list\u201d carried by the Chicago Sun-Times<\/i> and Philadelphia Inquirer<\/i> recommended books that didn\u2019t exist; the writer had used AI without adequate verification, prompting retractions and firings.<\/p>\n<\/li>\n Bias at scale:<\/b> The Equal Employment Opportunity Commission (EEOCs) first AI-discrimination settlement involved a recruiting algorithm that auto-rejected older applicants, underscoring how unmonitored systems can amplify bias and create legal risk.<\/p>\n<\/li>\n Data leakage:<\/b> After employees pasted sensitive code into ChatGPT, Samsung temporarily banned public gen AI tools on corporate devices \u2014 an avoidable misstep with better policy and training.<\/p>\n<\/li>\n<\/ul>\n The message is simple: Un-onboarded AI and un-governed usage create legal, security and reputational exposure.<\/p>\n Enterprises should onboard AI agents as deliberately as they onboard people \u2014 with job descriptions, training curricula, feedback loops and performance reviews. This is a cross-functional effort across data science, security, compliance, design, HR and the end users who will work with the system daily.<\/p>\n Role definition.<\/b> Spell out scope, inputs\/outputs, escalation paths and acceptable failure modes. A legal copilot, for instance, can summarize contracts and surface risky clauses, but should avoid final legal judgments and must escalate edge cases.<\/p>\n<\/li>\n Contextual training.<\/b> Fine-tuning has its place, but for many teams, retrieval-augmented generation (RAG) and tool adapters are safer, cheaper and more auditable. RAG keeps models grounded in your latest, vetted knowledge (docs, policies, knowledge bases), reducing hallucinations and improving traceability. Emerging Model Context Protocol (MCP) integrations make it easier to connect copilots to enterprise systems in a controlled way \u2014 bridging models with tools and data while preserving separation of concerns. Salesforce\u2019s Einstein Trust Layer illustrates how vendors are formalizing secure grounding, masking, and audit controls for enterprise AI.<\/p>\n<\/li>\n Simulation before production.<\/b> Don\u2019t let your AI\u2019s first \u201ctraining\u201d be with real customers. Build high-fidelity sandboxes and stress-test tone, reasoning and edge cases \u2014 then evaluate with human graders. Morgan Stanley built an evaluation regimen for its GPT-4 assistant, having advisors and prompt engineers grade answers and refine prompts before broad rollout. The result: >98% adoption<\/b> among advisor teams once quality thresholds were met. Vendors are also moving to simulation: Salesforce recently highlighted digital-twin testing to rehearse agents safely against realistic scenarios. <\/p>\n<\/li>\n 4) Cross-functional mentorship.<\/b> Treat early usage as a two-way learning loop<\/i>: Domain experts and front-line users give feedback on tone, correctness and usefulness; security and compliance teams enforce boundaries and red lines; designers shape frictionless UIs that encourage proper use.<\/p>\n<\/li>\n<\/ol>\n Onboarding doesn\u2019t end at go-live. The most meaningful learning begins after<\/b> deployment.<\/p>\n Monitoring and observability: <\/b>Log outputs, track KPIs (accuracy, satisfaction, escalation rates) and watch for degradation. Cloud providers now ship observability\/evaluation tooling to help teams detect drift and regressions in production, especially for RAG systems whose knowledge changes over time.<\/p>\n<\/li>\n User feedback channels.<\/b> Provide in-product flagging and structured review queues so humans can coach the model \u2014 then close the loop by feeding these signals into prompts, RAG sources or fine-tuning sets.<\/p>\n<\/li>\n Regular audits.<\/b> Schedule alignment checks, factual audits and safety evaluations. Microsoft\u2019s enterprise responsible-AI playbooks, for instance, emphasize governance and staged rollouts with executive visibility and clear guardrails.<\/p>\n<\/li>\n Succession planning for models.<\/b> As laws, products and models evolve, plan upgrades and retirement the way you would plan people transitions \u2014 run overlap tests and port institutional knowledge (prompts, eval sets, retrieval sources).<\/p>\n<\/li>\n<\/ul>\n Gen AI is no longer an \u201cinnovation shelf\u201d project \u2014 it\u2019s embedded in CRMs, support desks, analytics pipelines and executive workflows. Banks like Morgan Stanley and Bank of America are focusing AI on internal copilot use cases to boost employee efficiency while constraining customer-facing risk, an approach that hinges on structured onboarding and careful scoping. Meanwhile, security leaders say gen AI is everywhere, yet one-third of adopters haven\u2019t implemented basic risk mitigations<\/b>, a gap that invites shadow AI and data exposure.<\/p>\n The AI-native workforce also expects better: Transparency, traceability, and the ability to shape the tools they use. Organizations that provide this \u2014 through training, clear UX affordances and responsive product teams \u2014 see faster adoption and fewer workarounds. When users trust a copilot, they use<\/i> it; when they don\u2019t, they bypass it.<\/p>\n As onboarding matures, expect to see AI enablement managers<\/b> and PromptOps specialists<\/b> in more org charts, curating prompts, managing retrieval sources, running eval suites and coordinating cross-functional updates. Microsoft\u2019s internal Copilot rollout points to this operational discipline: Centers of excellence, governance templates and executive-ready deployment playbooks. These practitioners are the \u201cteachers\u201d who keep AI aligned with fast-moving business goals.<\/p>\n If you\u2019re introducing (or rescuing) an enterprise copilot, start here:<\/p>\n Write the job description.<\/b> Scope, inputs\/outputs, tone, red lines, escalation rules.<\/p>\n<\/li>\n Ground the model.<\/b> Implement RAG (and\/or MCP-style adapters) to connect to authoritative, access-controlled sources; prefer dynamic grounding over broad fine-tuning where possible.<\/p>\n<\/li>\n Build the simulator.<\/b> Create scripted and seeded scenarios; measure accuracy, coverage, tone, safety; require human sign-offs to graduate stages.<\/p>\n<\/li>\n Ship with guardrails.<\/b> DLP, data masking, content filters and audit trails (see vendor trust layers and responsible-AI standards).<\/p>\n<\/li>\n Instrument feedback.<\/b> In-product flagging, analytics and dashboards; schedule weekly triage.<\/p>\n<\/li>\n Review and retrain.<\/b> Monthly alignment checks, quarterly factual audits and planned model upgrades \u2014 with side-by-side A\/Bs to prevent regressions.<\/p>\n<\/li>\n<\/ol>\n In a future where every employee has an AI teammate, the organizations that take onboarding seriously will move faster, safer and with greater purpose. Gen AI doesn\u2019t just need data or compute; it needs guidance, goals, and growth plans. Treating AI systems as teachable, improvable and accountable team members turns hype into habitual value.<\/p>\n Dhyey Mavani is accelerating generative AI at LinkedIn. <\/i><\/p>\nTreat AI agents like new hires<\/b><\/h2>\n
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Feedback loops and performance reviews\u2014forever<\/b><\/h2>\n
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Why this is urgent now<\/b><\/h2>\n
A practical onboarding checklist<\/b><\/h2>\n
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