From AI Adoption to AI Maturity: Why the Next Business Advantage Is Using AI Well

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We recently had the opportunity to share Sertis’ perspective with Ichi Media in an interview with Tee Thuchakorn Vachiramon, CEO of Sertis, on one of the most important questions facing business leaders today: how can organizations move beyond AI experimentation and turn AI into a real business capability?

Across industries, many organizations have already started using AI. Some are testing Generative AI, some are applying automation in selected workflows, and others are using AI to search, summarize, or support day-to-day tasks. These are important first steps, but they do not always translate into lasting business impact. The next stage of AI is not about having more tools. It is about building the organizational capability to use AI with clarity, discipline, and measurable outcomes.

“Many organizations have already started using AI. But the more difficult question is how to make sure AI does not stop at experimentation, or remain a tool used only by certain teams. The goal is to turn AI into an organizational capability that helps people work better and helps the business move faster.”

That shift marks the difference between AI adoption and AI maturity.

Sertis: Helping Enterprises Turn AI into Real Business Capability

Sertis is an AI and Data company working with leading enterprises across Thailand and Southeast Asia. The company helps organizations apply data and AI to real business challenges, from improving operational efficiency and decision-making to enhancing customer experience and building long-term digital capabilities.

For Sertis, successful AI implementation is not only about building technology. It is about making sure AI fits the way each business actually works. That means understanding the business challenge, designing the right data foundation, developing AI models and systems, and working closely with client teams to bring those solutions into real operations.

Over the years, Sertis has developed a wide range of AI and Data solutions tailored to the needs of different organizations. The goal is not simply to help clients “have AI,” but to make AI part of real business processes and create measurable impact. This reflects how AI and Data in business transformed:

“For us, AI and Data are not only about technology. They are about helping businesses make better decisions, work faster, and see opportunities or risks more clearly. Sertis is not limited to being a technology developer. We act as a partner that helps organizations connect business strategy, data foundation, and AI implementation in the same direction.”

AI Adoption Is Only the Starting Point

AI adoption means an organization has started using AI. It may have pilot projects, selected use cases, or teams using AI tools to improve productivity. AI maturity goes further. It means AI is embedded into real business workflows and starts changing how the organization makes decisions, manages knowledge, serves customers, and operates across teams.

As AI tools become easier to access, the real challenge is no longer technical availability. It is whether organizations can use AI in a way that changes how work actually gets done.

“Today, using AI is not as difficult as it was before. Many organizations already have tools, use cases, or some level of experimentation with Generative AI or automation. But that does not automatically mean the organization is using AI maturely or to its full potential.”

The deciding factor is whether AI has moved into the actual rhythm of the business.

“Has AI entered the real workflow of the business? If AI is still a tool people open from time to time to help with certain tasks, that may still be adoption. But if AI starts helping people make decisions faster, reduce operational steps, minimize errors, or allow different teams to use the same body of knowledge, that is when maturity begins.”

This distinction matters because many organizations are now entering a more serious phase of AI implementation. The early stage was about testing what AI could do. The next stage is about designing how AI should work inside the organization.

That requires more than technology. It requires discipline around data, security, measurement, accountability, and governance.

“AI maturity does not come from having the most advanced model alone. It comes from the organization’s ability to use AI continuously, responsibly, and in the right direction.”

The Real Data Challenge Is Not Having More Data

Many enterprises already have more data than ever before. But having more data does not always mean making better or faster decisions.

In many cases, the challenge is not data quantity. It is data readiness.

“Many organizations have a lot of data. But the data is scattered across different systems, different teams, different files, and sometimes even different versions. When people need to make a decision, they still have to spend time searching, asking multiple teams, checking which file is the latest, or trying to understand whether different numbers are telling the same story.”

When this happens, data that should help the organization move faster can become a source of friction. Teams may spend more time verifying information than using it. Executives may receive reports that are not fully aligned. Different departments may work from different assumptions. As a result, decision-making becomes slower, even in organizations that already have large volumes of data.

The real challenge is not simply collecting more data.

“It is making the organization’s data and knowledge more connected, more reliable, and ready to use at the moment decisions need to be made.”

This is where AI can create meaningful value, but only when the foundation is strong.

When AI is built on trusted data and structured organizational knowledge, it can help teams summarize information, connect insights, retrieve relevant documents, identify patterns, and recommend next steps faster. But when the foundation is unclear, AI can also make confusion scale faster.

“If the data and knowledge foundation is strong, AI can help organizations move from information to decision much faster. But if the foundation is weak, AI may increase the speed of confusion instead of improving the quality of decisions.”

For business leaders, this is a critical point. AI is not a shortcut around data readiness. It reveals the strength, or weakness, of the foundation underneath.

The Next Advantage Is Not Tool Ownership. It Is Organizational Design.

In the early stage of AI adoption, organizations that started first often appeared to have a clear advantage. But as AI tools become more accessible, that advantage is changing.

Today, many companies can access similar models, platforms, and applications. The real difference is not who owns AI tools. It is who can design their organization to use AI effectively.

“The advantage is no longer just about having AI tools. It is about how well an organization can embed AI into the way it works.”

Some organizations use AI for small productivity improvements, such as drafting content, summarizing documents, or supporting basic analysis. These use cases are useful, but they are only part of the opportunity.

More mature organizations are beginning to apply AI to deeper business areas. They are using it to improve customer service, strengthen internal knowledge management, support operational planning, and help executives access insights more quickly.

This is where AI moves from being a tool to becoming part of the operating model.

“The next advantage will come from workflow, data, governance, and people not only from the model or the software. The winning organization may not be the one with the most AI, but the one that knows most clearly where AI should help, where humans should review, and how outcomes should be measured.”

This is also why AI maturity is closely connected to leadership. Technology teams may build the systems, but business leaders need to define where AI should create value, how it should be governed, and what success should look like.

AI Is Changing the Rhythm of Business

Across industries, one common pattern is becoming clear: AI is helping organizations move from waiting to understand what happened to seeing signals earlier and responding faster.

Traditional business operations often rely on historical reports, manual analysis, and multiple meetings before decisions can be made. AI shortens this cycle by helping organizations detect patterns, identify anomalies, forecast changes, and recommend possible actions from large volumes of data. Another important shift is the narrowing gap between insight and action. Many organizations already have data, but turning that data into action often takes time.

Information needs to be cleaned, interpreted, shared, discussed, and approved. AI is starting to reduce that gap by connecting knowledge, recommendations, and workflows more directly.

This shift applies across sectors.

Retailers need to respond faster to changing demand. Financial institutions need better visibility into risk. Healthcare providers need to reduce operational pressure while maintaining accuracy and trust. Manufacturers need to improve quality, reduce waste, and optimize production. Telecom businesses need to manage complex networks and customer experiences at scale.

“What these industries have in common is the need to make decisions faster, more accurately, and with better use of resources. AI is not only changing the tools organizations use. It is changing the overall rhythm of how businesses operate.”

AI Creates Different Impact in Different Industries

Although AI has common capabilities across sectors, its business impact is not the same everywhere. Each industry has different priorities, risks, and operational realities.

In Retail, AI helps businesses understand customers and market movement faster. This includes demand forecasting, assortment planning, promotion optimization, and personalization. Retail is a fast-moving industry, where even a small delay in decision-making can affect sales performance and customer experience.

In Finance, the focus is on accuracy, risk, compliance, and trust. AI is commonly applied to fraud detection, risk assessment, regulatory compliance, and internal knowledge access. But in this sector, AI cannot simply provide answers. It must also be explainable, auditable, and aligned with strict governance requirements.

In Healthcare, AI supports both medical professionals and hospital operations. It can assist with screening, imaging, documentation, triage, and resource planning. However, the use of AI in healthcare must be handled with particular care because the outcomes are connected to human lives, professional judgment, and public trust.

In Manufacturing, AI creates visible impact in quality, efficiency, and waste reduction. Use cases such as predictive maintenance, defect detection, production planning, and process optimization can turn small operational improvements into meaningful gains in cost, time, and quality.

In Telecom, AI helps organizations manage massive data volumes and highly complex systems. It can support network optimization, customer service, churn prediction, and field operations. The challenge is bringing data from many systems together so teams can make faster and more accurate decisions.

Overall, AI does not create the same impact everywhere. But across every industry, it is moving closer to the core operation of the business.

Trust and Governance Must Be Designed from the Beginning

As AI moves closer to important business decisions, trust becomes essential.

In the early stage, organizations often focus on what AI can do, how fast it can respond, or how much work it can reduce. But when AI begins to interact with customer data, business-critical information, or processes that affect real people, the questions become more serious.

• Can we trust this answer?

• Can we verify where it came from?

• Who is responsible for the final decision?

• What happens if AI gives an uncertain or incorrect response?

Governance cannot be treated as an afterthought.

“Leaders need to see governance as part of AI design from day one, not something to add later. Without good governance, organizations may be able to experiment quickly, but they will struggle to scale.”

Good AI governance means organizations need clarity on where AI gets its data, who has access to which information, how outputs are reviewed, where human-in-the-loop is required, and how the system should respond when confidence is low.

Responsible AI does not mean organizations should be afraid of AI. It means they should use AI with structure, awareness, and a clear understanding of their own risks.

“Responsible use does not mean being afraid of AI. It means using AI with discipline, structure, and an understanding of risk.”

Organizations that build trust well will be able to use AI more broadly over the long term. Employees will feel more confident using it. Executives will feel more confident making decisions with it. Customers and stakeholders will also have greater confidence in the organization’s ability to apply AI responsibly.

The Gap Will Widen Between Organizations That Use AI and Organizations That Understand AI

Looking ahead, Tee believes the next three years will reveal a clearer gap between organizations that simply use AI and organizations that truly understand how to apply it.

Many companies may have similar AI tools, similar models, or similar technology stacks. But their results will not be the same.

The difference will come from what surrounds AI: data, workflows, people, culture, measurement, and governance.

“Organizations that use AI well will not only work faster. They will make decisions from more complete information, see problems earlier, and adapt faster.”

On the other hand, organizations that have AI but lack connected data, clear processes, or trust in the system may find that AI becomes just another tool useful in some areas, but not powerful enough to change business outcomes.

This future gap is becoming visible in three key areas.

Decision Speed

Which organizations can access trusted insights faster?

Execution Consistency

Which organizations can help teams work from the same standards, knowledge, and direction?

Ability to Learn

Which organizations can use feedback and new data to improve how they work over time?

These capabilities will define the next phase of enterprise AI maturity.

In the future, the key question may no longer be which organization has AI. It will be which organization has designed its data, workflows, governance, and people well enough for AI to create real, continuous, and responsible business outcomes.

“The future question may not be which organization has AI, but which organization can design the way it works so AI can create real results continuously and safely.”

Turn AI from Experimentation into Enterprise Capability

AI maturity is not built by adopting more tools. It is built by connecting AI with the right data foundation, business workflow, governance model, and people strategy.

For organizations ready to move beyond experimentation, Sertis helps design and implement AI and Data solutions that create measurable business impact across real operations.