Mykhailo Zborovsky, expert in strategic development of iGaming products, writes for SBC News exploring whether iGaming companies should take rigid or flexible approaches towards staff, technology and processes.

Just two decades ago, large companies all looked the same: hundreds of employees meant multi-level hierarchies, strict regulations, and endless procedures. It seemed like there was simply no other way to organise work.

Startups broke this pattern. At first small, and later even as corporations, they proved that work could be organised differently — more democratically, flexibly, and faster. Research shows that 92% of employees see excessive hierarchy as an obstacle to productivity, and companies with a “flat” structure retain staff 38% better.

In other words, society formed a demand for freedom, autonomy, and responsibility in decision-making. It seemed like just a few more years — and the classical bureaucratic model would disappear like a dinosaur. But then artificial intelligence entered the game. And it showed a paradox: precisely where everything had been documented and systematised for years, AI integrated the fastest. The reason is simple: algorithms require clear logic and well-defined rules. Where processes were thoroughly described, documented and structured, machines were able to start quickly and effectively.

This is clearly visible in real examples. Those who had worked for years under strict rules turned out to be better prepared for AI than expected. Meanwhile, companies with freer cultures — despite their flexibility and speed in decision-making — turned out to be less prepared. The very lack of clear structure and systematisation slows down the integration of new technologies.

When Structure Becomes an Advantage

Take Oracle, for example. For years, the company built a clear structure: formalised processes, SOPs, defined roles. At some point, it seemed like this was holding back growth. But with the arrival of AI, everything changed. For AI, logic is crucial: if A — then B. Adaptability is not what it cares about.

It was exactly this structured legacy that became an advantage. AI easily integrates into prescribed processes, takes over routine tasks, automates HR, finance and analytics. Everything works because there is a foundation — a clear and predictable system. In large-scale business, this is critical. The same principle is already partly working in gambling: KYC, AML monitoring, support automation — all rely on documented protocols.

But there are also limits: when the system faces a new task or an unfamiliar market, it starts to stall. A rigid structure requires ‘repair’, and decision-making slows down. Routine procedures accumulate, resources are wasted, and any change can destabilise critical elements. In such conditions, flexibility becomes a true luxury and a key factor in determining who can adapt quickly and stay competitive.

When Flexibility Wins

On the other side are companies with maximum organisational flexibility. Valve, for example, operates without a classic hierarchy, allowing teams to choose projects themselves. This gives freedom and speed, but sometimes decisions are made without enough verification, and quality control depends more on culture than on processes.

Zappos once introduced holacracy – a system of roles and circles that combines adaptability with some structure to avoid chaos. There are also examples from the new wave: companies integrating so-called agentic AI – autonomous AI agents that detect problems, launch updates and respond in real time. But this only works where culture and technology allow quick intervention without bureaucratic delays.

Moving Without Guarantees

If we draw a parallel with gambling, things are even more dynamic here.

Something is constantly changing: what was approved yesterday may be revised today under the influence of new decisions or social demand. The payment infrastructure evolves, the player accounting system adapts, and tax conditions are adjusted. This creates a dynamic but not always predictable environment that businesses must be ready for.

Of course, in certain areas  such as support, retention, or payment routing, a structured approach is entirely appropriate. Where everything repeats, with a clear flow and a defined user, bureaucracy provides advantages: stability, control and the ability to integrate AI. This is especially important when automation deals with sensitive data or behavioural patterns. But overall, adaptability plays a bigger role in this industry. The key skill is not following protocol but reacting quickly to external changes.

And this pays off. An adaptive system quickly adjusts, tests hypotheses and sees the market live. But at the same time, it’s hard to transfer. Without documented experience, scaling is difficult. If AI doesn’t see a pattern, it can’t help.

Here it’s important to be honest: the adaptive model is not a magic pill. It also has limitations. For example, delegation is harder when the process is “in someone’s head.” Or it’s difficult to implement automation systematically when there’s no basic framework. That’s when you start to value simple things like an internal knowledge base or even short SOPs for repetitive processes.

So the point is not to choose between bureaucracy and flexibility. The point is to learn how to combine them.

Where to Look for Balance?

I believe this is not about opposition but rather about evolution. The world has long been searching for ways to combine the efficiency of structure with the flexibility of living decisions. In management, this is called “organisational ambidexterity” — when a company simultaneously explores new opportunities (explore) and exploits proven ones (exploit).

Today, both large corporations and small startups are moving in this direction. Corporations are launching autonomous product teams, switching to Scrum and flexible budgeting to test new ideas quickly without losing stability. Startups are creating internal knowledge bases to avoid burning out by the third month of scaling.

What Could This Mean for Gambling?

Perhaps the gambling market doesn’t need a complete transformation. It’s enough to solidify what already works. Start small: analyse repetitive processes, add structure where it really helps, and at the same time keep the most valuable thing — the ability to react quickly and move forward.

Because the future is not bureaucracy. But it’s not pure improvisation either. It’s a combination.

Today we are learning to combine: to fix effective practices while leaving space for real-time action. And it seems that precisely such a hybrid model — with a specific balance of “structure + freedom” — could become the key to successful gambling management in Ukraine.