Thailand’s Enterprise AI Readiness in 2026: What Are the Ready Ones Doing Differently?

TH

In June 2026, Microsoft's Global AI Diffusion Report ranked Thailand second globally in AI adoption growth, behind only South Korea.

That's a positive sign for the country's overall AI development. However, a closer look at how organizations are implementing AI reveals a more complex reality. Being ready to actually turn AI into real business results is a different challenge altogether, and a lot of organizations are still trying to solve it.

So What Does "Second Place" Actually Measure?

Thailand earned that ranking thanks to a mix of factors including a national AI policy that got moving earlier than many neighboring countries, continued investment in digital infrastructure, and businesses that have been relatively quick to experiment with and adopt AI.

However, national-level success doesn't automatically translate into organizational readiness. At the enterprise level, AI readiness isn't just about policy or infrastructure. It's about whether a company can scale AI from pilot projects to real production use, whether internal data is reliable and well-organized, and most importantly, whether its people can adapt and work effectively with AI.

Across all these three areas, many organizations in Thailand still have room to improve.

The Actual Readiness Gap

The Logicalis CIO Report 2026, which surveyed over 1,000 CIOs worldwide, found that more than half of business leaders believe AI adoption is moving faster than their organisation can train people to keep up. 

And this isn't just a frontline workforce problem. Many C-suite leaders also admit they don't have a deep enough understanding of AI to make confident calls on where, when, and how much to invest.

The result is that many organizations remain stuck in the pilot stage. The pilots often look impressive, but they never quite make the jump to real business use at scale.

Three Obstacles Standing Between AI and Production

1. The data foundation isn't strong enough yet

Testing AI in a sandbox environment is relatively easy. This is because the testing data is clean, organized, and available in a controlled setting. But once AI is introduced into day-to-day operations, hidden challenges quickly appear. 

Data may be scattered across different departments, stored in disconnected systems, or trapped in applications that were never designed to support modern AI workflows. These issues often keep AI projects stuck in the experimentation phase.

2. AI that doesn't actually understand the business

Another challenge isn't the model itself, it's how well AI understands the organization. Generative AI may know a lot about the world considering its massive database, but it doesn't know internal data such as how a bank's loan approval process works internally, what exceptions exist in a compliance team's SOP, or where employees store the reports they rely on every day.

That's exactly why an enterprise knowledge base is not just a nice-to-have anymore. It's becoming essential for organizations that want AI to support real business work

3. People and culture haven't caught up yet

The final challenge is people and organizational culture. Many companies underestimate how much change AI brings. Adopting AI doesn't end with installing a new system. It also requires redesigning workflows, developing new skills, and establishing clear guidelines for how AI should be used. Without these changes, even the best technology can struggle to deliver meaningful results.

What do the companies that succeed do differently?

Another interesting statistic comes from IBM, Organizations that use AI effectively are up to 3.5 times more likely to outperform their competitors. But the more interesting insight isn't the number itself, it's the mindset behind it.

Organizations that move beyond the pilot stage don't see AI as just another tool added to existing systems. Instead, they treat AI as an intelligence layer that supports how work gets done and how decisions are made. To be precise, rather than simply using AI to do existing tasks faster, they redesign processes with AI built into the workflow from the beginning.

Crucially, these organizations adopt a "Human in the Lead" approach rather than a "Human in the Loop" approach. It's just one word change, but the thinking behind it is completely different.

Human in the Loop means humans step in to review or approve whatever AI produces. Human in the Lead6, on the other hand, means humans are still the ones setting the goals, deciding the business direction, and exercising judgment, while AI helps analyze information and suggest options.

So What Should an Organisation Do To Move Forward?

The first question any organization needs to ask is: what's the right foundation to build so the AI we're investing in actually delivers real results?

At Sertis, we believe the core challenge of enterprise AI is making sure organizations can actually put it to work in practice. That's exactly why we built KMAI and Private LLM to solve these foundational problems directly.

Sertis KMAI (Knowledge Management AI)

KMAI is designed to be an organization's single source of truth. It brings together, organizes, and connects knowledge from across the business: internal policies, SOP documents, SharePoint knowledge bases, reports scattered across different teams into one accessible place. With KMAI, everyone from operational staff to executives can find the accurate information quickly, complete with traceable sources.

Private LLM

Private LLM addresses another major challenge: data security and governance, especially for industries with strict data requirements like banking, healthcare, and manufacturing. In practical terms, it lets organizations process all their data within their own secured environment,  whether on-premise or in a private cloud, while maintaining control over sensitive information and meeting enterprise security requirements.

Building the Right Foundation for Enterprise AI

When enterprise knowledge management and secure AI infrastructure come together, organizations gain a stronger foundation for scaling AI from pilot projects to real business use.

At the end of the day, success with enterprise AI doesn't come from having the most advanced model. It comes from having the right foundation: good data, accessible knowledge, strong governance, and people who know how to work with AI effectively. 

Thailand's AI readiness ranking is a positive sign. The next challenge is helping organizations move beyond experimentation and turn AI projects into real business results.

Interested in learning how KMAI and Private LLM can support your organization's? Contact us