Thailand Focuses on AI Governance Amidst Rapid Technology Adoption

You hire an astute, hard-working, fresh graduate to run things for you. You hand them the keys to everything in your company; that includes every system, every endpoint, every file, and every password, all of it. Your only instruction to them? “Go ahead and improve things!” Then, trusting in their competence, you leave them to it. Doesn’t that sound like a recipe for disaster? Yet that’s precisely what’s happening in IT departments worldwide. The intern we’re talking about here goes by the fancy name of artificial intelligence (AI), and it’s not just an accessory anymore. It’s being woven into the very fabric of our IT service management (ITSM) platforms across the board. As you may know, it’s no longer just a chatbot with some basic logic. By 2026, AI will have become the invisible force toiling in the background around the clock, summarizing your tickets, flagging incidents before they explode, and resolving issues—all while your team goes to sleep. To complicate things even further, while AI capabilities have skyrocketed, our governance frameworks have barely budged. We’ve simply unleashed this extraordinary technology without establishing clear rules of engagement. This results in a dangerous gap that exposes organizations to all kinds of data breaches, algorithmic bias, and operational meltdowns. In other words, adopting and weaving AI capabilities into our daily operations and workflows is no walk in the park. To do it safely and successfully, we need a paradigm shift from simply utilizing AI to governing it. This shift is already playing out on a regulatory level, with Thailand serving as a prime regional example. As organizations accelerate AI adoption across customer service, cybersecurity, software development and enterprise operations, regulators are placing increasing emphasis on responsible AI governance rather than AI deployment alone. The Electronic Transactions Development Agency (ETDA) has introduced Thailand’s AI Governance Guideline and expanded practical guidance for organizations adopting generative AI, while the country’s National AI Strategy (2022–2027) positions trustworthy and ethical AI as a key foundation for long-term digital competitiveness. The same principle applies as it does behind the wheel of a car: technology should remain under the driver’s control, not the other way around. Likewise, a similar question must be asked when it comes to AI. Clear leadership is essential, and explicit rules and guidelines must be established and strictly followed. This article seeks to map out exactly how to go about achieving that. Before going into what AI governance is, let’s first talk about what it’s not. AI governance is not about drowning your team in bureaucratic paperwork and approval chains. It is also not about setting a bunch of rigid limitations that defeat the very purpose behind AI. What it does mean, though, is to build a sensible framework that ensures AI is operating safely, fairly, and in sync with what your business actually needs. Consider it like guardrails on a mountain road; they’re there to make your drive easier and safer, not to hinder your progress. This shift is becoming increasingly critical in Thailand as companies quickly adopt these new capabilities. With AI anchoring itself deeper into daily operations, the focus for business leaders has to expand beyond just performance. It is now about making sure these systems are transparent, accountable, and secure. Furthermore, with Thailand’s Personal Data Protection Act (PDPA) already setting clear boundaries for customer and employee data, building these guardrails isn’t just a smart strategy – it’s a legal necessity. The uncomfortable truth is that AI systems inherit the biases baked into their training data. So if your historical ticket records reflect unconscious human prejudices even in the slightest, and they almost certainly do one way or another, your AI will absorb those patterns and amplify them at machine speed. How does it do that? It can de-prioritize requests from certain departments, for instance. Perhaps it generates knowledge base articles with subtly biased language. Or it could recommend solutions that systematically favor one user group over another. Beyond being ethically problematic, this creates business liability. So, how do you build that moral compass? Start by pulling together an ethics committee—and no, this isn’t just another IT meeting. Bring in the HR and legal teams and people from your core business units. Their job is to define what ethical AI actually looks like in your specific organizational context. Run regular bias audits while you’re at it. Schedule periodic reviews of your AI’s outputs. Check whether ticket routing is equitable. Examine whether proposed solutions show favoritism. Approach it like you would any employee performance evaluation. Don’t forget to interrogate your training data. Have a frank conversation with your ITSM solution vendor about data feeding its models. The more diverse and representative that data is, the fairer your AI will behave. Imagine your AI agent autonomously resolves a critical incident overnight. By the next morning, no one can explain how it reached its decision or why it chose that solution. While the incident has been resolved, the lack of transparency means you can’t validate the outcome, learn from it, or confidently rely on the AI when the next crisis arises. AI that operates as a black box is fundamentally unmanageable. If you can’t understand it, you can’t control it—and if you can’t explain it, you can’t trust it. The fix is to make explainability nonnegotiable. When you’re evaluating or purchasing an ITSM tool, explainability must be your deal-breaker. The system must be capable of documenting its reasoning in language that humans can actually understand, not just technical logs that look like esoteric inscriptions only a semiotician can decipher. Build in human checkpoints for high-stakes decisions, too, like pushing changes to production systems. Configure your AI to recommend actions but require human approval before execution. Think of it as a measure twice, cut once approach for automation. This is similar to when your system asks for your direct approval before proceeding to install certain updates, for example. Let’s tackle this with a simple scenario: An AI-driven automation script operating with the best intentions accidentally takes down a business-critical service. Now what? Who’s on the hook: the developer who originally wrote the script, the manager who green-lit the automation, or perhaps the vendor that provided the AI platform? If you can’t answer this question with certainty, you have a serious governance gap. Without clearly defined accountability, crisis response devolves into an ugly game of finger-pointing and blame-shifting exactly when you need decisive action most. The solution is simpler than you think: Map it out. Create a straightforward, res