In the support industry, and especially in EOR/payroll (my domain expertise), AI is not only about efficiency; it directly impacts compliance,trust, and employee experience. When setting up both client-facing and employee-facing supportteams,the right approach is to design AI as a support multiplier, not a replacement. I am also a firm believerthat you must have clear objectives and be AI-ready. AI is not a magic wand that fixes broken foundations. If your documentation and knowledge base are in disarray,the firstlayer of AI adoption should be using AIto identify gaps, structure knowledge, and build or draft documentation—before talking about efficiency gains or deflection. Here is how Ithink aboutit: 1. Treat knowledge as infrastructure, not content (I strongly believe this) AI in EOR only works if your knowledge system is enterprise-grade. Supportleaders should invest early in: • Single source oftruth per country • Versioning + legal validation flows • Clear “safe vs. unsafe” response boundaries • Human ownership per policy / domain Ifthe knowledge base is weak, AI will hallucinate answers—creating compliance risk and eroding employee trust. Many EOR AIfailures are actually knowledge governance failures. 2. Start with “jobs to be done” – where human effort is wasted Automate high-volume, repeatable questions: • How do I download my payslip? • What documents are needed to do XYZ? • Whatis the leave policy in XYZ country? Ideally, automate repetition. Neverfully automate areas involving risk or emotion (especially employee sensitivity around data). 3. Adopt AI in layers AI at the front door(Deflection + Guidance): This reduces load, improves response times, and drives better SLA/TAT. AI as agent co-pilot: • Auto-summarise tickets and provide historical context on the customer and similar pastissues • Suggestreplies based on history and the knowledge base • Improve grammar and sentence construction This also reduces burnoutfor experienced resources. AI as an ops engine – forleaders to leverage insights: • Predictticket spikes • Auto-detect emerging issues (client, process, platform, feature gaps) • Identify knowledge gaps and policy confusion signals • QA automation (risk flagging,tone checks, compliance hints, potential escalation / high-risk customers) In EOR support, successful AI adoption is less abouttools and more aboutfoundations—strong knowledge, clear guardrails, and disciplined, phased execution.
