We’re already ahead on AI, we’ve introduced AI tools.” A reaction that reveals a lot
“We’re already ahead on AI, we’ve introduced AI tools.” This is probably the most common statement I hear when talking to people about AI in their organizations. There is a lot packed into this one sentence. For example, the misconception that introducing AI tools automatically meets the real need. The pressure to do something quickly in order to be seen as being ahead. And the misunderstanding that rolling out something unfinished is enough to label it an “MVP” in a modern way.
Anyone who truly wants to be ahead when it comes to AI has to drive the AI transformation of their organization.
That’s what we’ll take a closer look at next. But let’s start with what currently occupies many people: AI tools.
The Flood of AI Tools and Their Hidden Champions
We are being practically flooded with AI tools: twenty to thirty new ones per day, according to the TAAFT AI tool database. Yet no one knows them all. Recently, I ran a poll asking: Which logos do you recognize? The results were telling. ChatGPT and Perplexity were far out in front. Microsoft Copilot and Gemini appeared occasionally. Grammarly, despite its thirty million daily users, was barely recognized. Baidu’s ERNIE Bot from China and Claude by Anthropic are becoming increasingly well known. And then there was unhamster, our own coach AI, which many people recognized. That was probably also because many respondents knew us as Timmermann.
Most organizations think they have “introduced AI” once they enable an AI tool. But that is still very far from an AI transformation.
Ryt Bank: How AI-Native Organizations Work
In ten to twenty years, most organizations will describe themselves as “AI first.” At the same time, this AI-first future is closer than it appears at first glance, because it already exists.
Take the example of Ryt Bank from Malaysia. This financial institution was built as AI-first from day one. The company shows what is possible when you don’t just implement a tool, but rebuild the organization around AI.
- Onboarding and personalization: New registrations are handled entirely by AI. The AI identifies needs, assesses risks, and formulates conversation flows. Customers experience personalized interactions, often without realizing they are communicating with a machine.
- Risk assessment and credit decisions: Small loans are issued automatically. The AI calculates risk and pricing in fractions of a second. In Europe, it is not permitted by regulation to vary prices dynamically in real time. In Malaysia, it is. This illustrates how strongly local regulation shapes the use of AI.
- IT security and end-to-end automation: Ryt Bank uses AI specifically for IT security and end-to-end automation. AI monitors the IT architecture, distributes system loads, detects malfunctions at an early stage, and derives improvements. At the same time, processes are fully automated from initial contact to payout, ensuring that sales and risk logic work together seamlessly.
- Continuous learning: Data from the market and from day-to-day operations is continuously updated and integrated into processes. The algorithms ensure ongoing learning.
Of course, Ryt Bank is not an established organization that had to undergo a complex transformation. It is AI-native, which makes things easier. For established organizations, the transformation process is more demanding.
Is Everything Lost for Established Organizations?
My answer is a clear no.
Established organizations can rely on four factors.
- Brand loyalty
We humans are creatures of habit. We try to avoid change, and with familiar brands and products we know what we’re getting. Something new has to be perceived as significantly better to trigger a switch. For example, only about nine percent of customers change their primary bank account each year.
- Mass inertia
Yes, “almost everyone is doing AI,” but only a small share is doing AI in a truly strategic and scalable way. According to one survey, only one percent currently consider their company to be “AI-ready.” (see IBM Studie 2025). Another survey found that only sixteen percent of AI initiatives have been scaled so far (see McKinsey & Company 2025). This shows that it’s not too late yet. But time is running.
- Further AI development is required
AI can be divided into three stages of development:
- Artificial Narrow Intelligence (ANI) is what we have today. It describes AI that performs tasks based on clearly predefined rules and only approaches human intelligence in very specific domains.
- Artificial General Intelligence (AGI) describes AI that can solve problems independently and apply knowledge across domains, at a human level of intelligence.
Artificial Super Intelligence (ASI) describes AI that surpasses humans in all areas of intelligence.
Depending on who you ask, it will take another five years, according to Silicon Valley, or up to twenty-five years, according to academia, to reach the next stage, AGI.
Leapfrogging
Organizations can skip missed stages of development, for example when they are already in a period of disruption and are fundamentally changing key aspects of their operating model. This can allow them to move from a disadvantageous position to being “ahead of the wave” relatively quickly. One example is M-Pesa, a mobile banking service from Kenya. Founded by Safaricom and Vodafone, two telecommunications providers, it revolutionized banking in Kenya and other parts of Africa. Today, around seventy-nine percent of all Kenyans use M-Pesa as their primary account. Hardly anyone had a traditional bank account, and suddenly many people had a mobile account on their smartphone.
Many organizations observe, hesitate, or assume the hype will pass. Only sixteen percent of companies have AI initiatives, and only one percent see themselves as AI-ready. At the same time, other organizations are rapidly gaining ground, and technological development is accelerating.
To take a small next step in AI transformation, we offer several formats: an AI Future Workshop, where we jointly analyze your current position and create a roadmap; executive inspirations to engage top decision-makers on AI topics; and keynotes to anchor the topic within your organization. You can also try our AI coach unhamster at unhamster.com.
In the next article on AI transformation, we will continue with maturity levels of AI transformation. And with how organizations can adapt through the further development of their operating model and reach these levels earlier.
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