2026首席信息官指南

How to scale 24/7 IT service worldwide with intelligent automation,multilingual support, and AI agents that adapt to every region Table of contents 01Why standard AI fails to scale global IT support02What makes agentic AI different03How to roll out agentic AI at global scale04The future of global IT — poweredby Moveworks Why standard AI fails to scale global IT support Imagine your company launches a new product in Asia, and suddenly thousands of employees needaccess overnight — just as your U.S. support team logs off. In Europe, strict GDPR compliance slows your teams down. Meanwhile, a new acquisition in Latin Americaadds yet another layer of systems and policies to manage. The result? Support tickets pile up, employees wait longer, and trust in IT begins to slip. When that happens,productivity suffers, people turn to unsanctioned tools, and unsafe shortcuts — like password sharing or •More than 80% of IT leaders expect AI to improve productivity •Yet IT requests surged 39% last year (Salesforce, 2024) Global IT support isn’t just a bigger version of local IT — it’s exponentially harder. You’re juggling time zones,languages, regional systems, budgets, and compliance obligations, all at once. Hiring more support agents in every region isn’t sustainable. Standard chatbots, search tools, andautomations are slow to deploy, break when systems change, and don’t scale across languages or That’s why basic automation or GenAI tools aren’t enough. To deliver fast, consistent service worldwide,you need a new approach: agentic AI. To see why, let’s look at where standard automation breaks at global scale — and how agentic AI succeedswhere others fail. The result? A patchwork of disconnected solutions that can’t adapt to real-world complexity — leaving yourIT team stuck maintaining tools that do not deliver true global scale. Why global IT support breaks at scale Even with multiple tools in place, here’s where global IT support usually starts to fall apart: 1. Hardcoded flows don’t scale Scripts might work in one region or system — but fail when you add new languages, org-specific policies, orregional workflows. Teams end up duplicating effort instead of scaling intelligently. 2. Traditional search tools miss the mark Most rely on static retrieval-augmented generation (RAG) models that pull from outdated wikis or docs,often surfacing irrelevant or outdated content — or worse, hallucinations. In fact, Gartner reports that only 26% of IT leaders say their knowledge management tools provideaccurate, up-to-date answers at scale. When employees get irrelevant answers that don’t reflect their 3. Language support is bolted on, not built in Translation APIs or manual settings can’t keeppace with a workforce spread across dozens ofcountries. For example, an employee in Tokyo maystill get English forms, even if workflows should be 4. Fragmented automations = broken experiences 5 Major Challenges ofScaling Global IT Support Disconnected bots, portals, and forms forceemployees to bounce between tools instead of Ticket surges duringglobal initiatives 5. Governance and security are often an afterthought Quick wins often come at the expense of GDPR,SOC 2, and role-based access. Without zero-trustarchitecture and enterprise-grade governance built The bottom line Language barriers For multinationals, patchwork automations andstandard AI aren’t enough. Rising costs, frustratedemployees, low adoption of IT tools, and even That’s why more global IT teams are movingbeyond basic automation and adopting agentic AI. How Ciena scaled smarter with AI Employees bounced between email, chat,and portals, while support teams wereoverwhelmed by repetitive tickets. Approvalprocesses often stretched from days into Solution: To address these challenges, Ciena deployed their AI Assistant “Navi.” By integratingdirectly with ServiceNow, Workday, and other core systems, Navi automated more than Impact: •Approval times dropped from three days to just 30 minutes •50% of employees engaged with Navi for everyday needs •Adoption grew at a rate of 20% quarter over quarter, freeing support teams to Our adoption and growth are tremendous. Employees are Lalit KumarSenior Analyst, Ciena What makes agentic AI different Most automation tools weren’t built for the complexity of global IT. They automate one process, in onesystem, for one team — not end-to-end support across your enterprise. That’s why CIOs and global IT Unlike static scripts, slow integrations, or shallow chatbots, agentic AI acts like a digital teammate. Itunderstands employee requests, reasons across fragmented systems, and can take secure, end-to-end Core capabilities of agentic AI •Natural language understanding Handles messy, real-world requests without forcing employees into structured forms •Advanced reasoning Chooses the best resolution path across tools, regions, and workflows — even when context is incomplete For example, if an employee req