AI After the Chatbot: 6 New Rules for Building AI Organizations

Six Things That Will Create Long-Term Differentiation in an AI-Everywhere World

2026 has made one thing unmistakably clear: AI is becoming more capable in many dimensions. Its most significant leap, however, is not simply answering questions. It is the growing ability to give AI the tools it needs to complete work end to end.

AI can create a presentation using a company’s brand and summarize its internal information. It can build a web application, connect it to a database, and add cool features. There are many more examples, and using AI to create these things can appear both faster and less expensive.

But if every business can use AI in roughly the same way, and anyone can now build a product or start a company more easily and quickly than before, what will create durable competitive advantage?

This is a question we think about constantly at SCB 10X. As a disruptive technology investment company, SCB 10X’s Tech Intelligence team, including myself, Oravee Smithiphol, and Dr. Tanwa Arpornthip, closely follows AI and speaks with founders and experts around the world through SCB 10X’s AI-Volution program. We have distilled six ideas to help organizations stay oriented in the AI era.

1. Domain Expertise and User Judgment Will Become More Valuable

How often have you scrolled through your feed and come across AI-written content, AI-generated images, or AI-built websites? Have you ever felt that they are all starting to look the same?

This is a natural side effect of a world in which the cost of production is falling quickly. When everyone can make something faster, the scarce capability is no longer simply getting it done. It is deciding what should be made, what should not be made, and what is good enough to use in the real world.

Alex Svanevik, CEO of Nansen, raised this question on AI-Volution: if everyone can use tools such as Claude Code or Codex to build basic software in roughly the same amount of time, what gives one software company an advantage over another? His answer was not faster coding. It was the expertise embedded in the product.

Nansen is a particularly clear example because it operates in a world where raw blockchain data is more broadly accessible than data in many other industries. Everyone may be able to see the same transactions, but not everyone can interpret why a large amount of money is moving or which signal actually matters. The advantage is therefore not in having data alone. It is in knowing how to read it, then turning that understanding into a product that is difficult for others to replicate.

The same principle applies even to people who do not build software. When we use AI to help us work, whether the output is good or poor often reflects the quality of the user’s judgment more than the model’s capabilities alone. AI can help us work faster, but it does not guarantee that the work is appropriate, contextually sound, or trustworthy.

We are seeing clearer examples of this in Thailand and elsewhere. AI-generated visuals for crime news have sometimes resembled movie posters, creating an inappropriate tone. Starbucks Korea’s “Tank Day” campaign was also heavily criticized for touching a historical wound that the marketing team may not have fully recognized. These examples are a reminder that the issue is not only that AI can be wrong. People can stop thinking too early and publish work without a further layer of judgment.

As the cost of creation falls, domain expertise, taste, and an understanding of people will become even more valuable.

2. Individual AI Productivity Does Not Automatically Create Institutional Productivity and Memory

Many organizations have begun buying AI subscriptions for employees, expecting organizational productivity to rise as a result. And it is true that many people work faster with AI. The problem is that today’s improved AI memory is still primarily tied to an individual’s account. It may remember more of a user’s context, but it does not yet systematically remember the context of the organization as a whole.

When an important employee leaves, a great deal of knowledge may leave with them: chat histories, prompts, workflows, and decision-making methods that were never converted into something others can reuse.

This creates an important distinction between individual productivity and organizational productivity. One person may become significantly better at using AI, but that does not mean the organization becomes better in a way that compounds over time. Not if knowledge remains isolated within each individual.

One useful example is an investment screening workflow. Each month, the SCB 10X team reviews hundreds of new startups. Investors need to research and assess which startups are appropriate for investment. If an organization interviews its experts to understand the criteria they use to screen opportunities out, such as the red flags that should trigger immediate caution, this reasoning can be embedded into an AI system. The system can then handle simpler work and reduce the team’s workload, while the knowledge no longer resides with just one person.

Building institutional memory does not mean remembering everything. An organization that stores everything without discernment often becomes slower and more confidently wrong at the same time. What is worth retaining includes the reasoning behind decisions, experiments that did not work, and judgment that previously lived only in the minds of experienced people. What should be discarded includes outdated policies, pricing conditions that have changed, and stale information that a system might retrieve with unwarranted confidence.

Over time, people who oversee AI systems within an organization will no longer be only operators. They will also need to become governors of this knowledge system: deciding what the organization should remember, what it should forget, and what it needs to update. Sometimes the organization has not changed, but the outside world has changed faster than the memory it once recorded.

3. Users Want Systems That Deliver Outcomes, Not a Nicer Software

The software world used to teach us that, if we wanted to get work done, we first had to learn how to use the tool. We had to know where the menu was, what each button did, and which sequence of steps was correct.

But as AI becomes a new interface layer, user expectations are shifting. Instead of asking, “How do I learn this software?” users are asking, “Can I state the outcome I want and have the system handle the rest?” This is not merely a matter of making interfaces more attractive. It is a shift from clicking through predefined steps to communicating in natural language.

For example, an internal AI product at SCB 10X was not designed around a dashboard or a long list of menus. Users simply tag the AI and say what they need. The team understands how to use it immediately because it feels more like assigning a task to a colleague than learning a new piece of software.

From a business perspective, this matters enormously. Customers do not ultimately want to “use software.” They want their problems solved, their customers satisfied, their sales restored, or their work time reduced. If one company continues to sell only features and user seats while another begins to sell an end outcome, the latter will address what users actually need more directly.

This becomes even more important in an era when vibe coding makes it much easier to create polished-looking applications. Many builders may focus on producing as many “apps” as possible. But the next trend for startups may be to become more than software companies: to become service providers accountable for an outcome. You will not only sell software. You will need to sell what that software helps customers achieve.

4. Our Next Customer May Not Always Be Human

One change that many organizations may still underestimate is that the entity searching for, comparing, and deciding to purchase a product in the future may not always be a human directly. It may be AI acting on a human’s behalf.

Customers once had to visit websites themselves, open several tabs, read the information, and compare options. Today, users increasingly ask AI to find options that meet specific requirements: a hotel within a certain price range, flowers with particular specifications, or a comparison across several product models. As this behavior grows, the question for business owners is no longer only whether their website works well for people. They also need to ask: how well can AI read our information?

Many websites remain unfriendly to AI. Their information may be scattered, product pages may lack structured details, or the business may rely too heavily on closed platforms, such as social commerce or mobile apps whose information general-purpose models find difficult to access. If AI cannot see a business, it will be difficult for that business to be recommended, even if its product or service is genuinely good.

This is particularly interesting because a business that appears more technologically advanced will not necessarily have an advantage in the AI era if its information is harder to read. Conversely, a website that looks ordinary but has clear information, a sound structure, and straightforward transactions may make it much easier for AI to act on a user’s behalf.

The opportunity also comes with risk. If we do not deliberately design channels through which AI can interact with our systems, AI may try to find its own path in unexpected ways. This is not simply a marketing issue. It directly involves data architecture, security, and transaction channels.

For organizations, a question worth asking today is this: as younger generations increasingly search with AI before visiting our website or app, have we designed our content, product information, and transaction paths for an AI-first world?

5. A Working PoC Does Not Mean AI Is Ready for Reliable Production

This year, many organizations have launched a large number of AI proofs of concept and pilot projects. That is a positive development. But an increasingly clear problem is that many PoCs that look impressive in a controlled setting cannot make the transition to production and work reliably in the real world.

Automated ordering systems at drive-through restaurants are a classic example. A PoC may seem to work, but once it enters an actual operating environment, it encounters background noise, real customer phrasing that never appeared in testing, edge cases, and the disorder of frontline operations that the technical team may never have seen. What makes the system fail is not always that the model is not intelligent enough. It can be that the organization has not designed the system for a world that is more complicated than expected.

A PoC may demonstrate that a model can take orders, summarize documents, or hold a conversation. In production, however, organizations need to answer much harder questions. Does it still work in a noisy environment? What happens when customers do not follow the script? When should exceptions or disputes be handed over to a person?

Dr. Tanwa experienced a related example with a receipt-scanning system used for parking validation. The document-scanning technology itself may have been very capable, but in practice some restaurant receipts were so long that they became difficult to scan. The failure was not the AI model. It was an incomplete view of the use case across the full journey.

For this reason, organizations that are serious about AI may need more than data scientists and AI engineers. They also need people who understand technology, the business, and frontline workflows at the same time. These are the people who can connect the dots: how a system should be designed, measured, and deployed so that it works in practice, rather than merely looking polished in a demo.

6. Organizations May Underestimate the Cost of AI and Overestimate Its Savings

Because AI can visibly reduce the time required for many tasks, many executives have hoped to see ROI quickly. But many organizations may be calculating it too narrowly.

A common approach is to compare an employee's salary with token or subscription costs, then conclude that AI is cheaper. This captures only the most visible costs. It leaves out the cost of systems integration, workflow redesign, governance, maintenance, and the exceptions that still require human intervention when a system does not behave as expected. These costs are often forgotten, or turn out to be much larger than businesses initially imagined.

More importantly, an organization may achieve short-term cost savings while unknowingly eroding a more valuable long-term capability. It may reduce the role of people with deep expertise until it begins to lose an understanding of the actual work, the judgment to do it well, and the ability to improve its workflows over time.

This is what we call the ROI trap. Attractive early numbers can conceal several layers of cost behind them. Organizations should therefore ask more than whether AI is cheaper than people. They should consider at least three questions at once:

1. How high is the cost of deploying the system and making it reliable enough for real-world use?
    
2. What value does it actually create? How much does it genuinely save, how much faster does it make the work, and what trade-offs are we not yet accounting for, such as a decline in quality?
    
3. As we use more AI, what capabilities will we build, and which might we lose? Which distinctive organizational capabilities are we creating, preserving, or eroding?

Ultimately, all six rules point in the same direction. In a world where AI makes creation faster and cheaper, the advantage will no longer belong to the organization that simply has AI first. It will belong to the organization that knows how to use AI to compound employee expertise into organizational knowledge, designs systems that can be accountable for real outcomes, and calculates costs and value honestly.

The answer to these questions may be what separates organizations merely experimenting with AI from those that build a genuine long-term advantage with it.

---

Oravee Smithiphol is Tech Intelligence & Insights Manager at SCB 10X, where she tracks emerging AI developments across startups, enterprises, and financial services organizations worldwide.