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When AI stopped being just another tech wave

In early 2023, I was at a leadership dinner with part of the Somnio team when we started discussing the first truly useful AI tools coming to market.

I nearly choked. Not because I was scared. Because in the middle of that conversation something clicked,  this wasn't another tech wave. We'd lived through Flutter, new platforms, new stacks. We knew what "a new tool" felt like.

This felt different. This felt like a system change.

A few months later, our client conversations confirmed it. The requests started shifting. It was no longer just "I want to build this." It became:

"I want to build it faster."

"I want to automate my internal processes."

"I want to understand where AI actually applies in my business."

"I want my team working at a new productivity standard."

That wasn't a new feature request. That was clients redefining what they expected from a software partner.

Why AI transformation is different from any previous tech shift

AI transformation is the process of redesigning how a company operates  (its hiring, its structure, its workflows, and its value proposition) around AI as a core capability rather than a peripheral tool.

That definition matters because most companies aren't doing this. They're adopting AI tools. That's not the same thing.

We've navigated a lot of changes at Somnio. New frameworks, new platforms, new architectural patterns. Each required learning and adaptation. But none of them changed the underlying logic of what a software company does.

AI does.

The shift isn't about adding a capability. It's about rethinking every layer of how the business operates. How you hire, how you structure teams, how you measure productivity, how you create value.

The framing I keep coming back to: AI isn't opening a new lab inside your company. It's changing the operating system of the entire business,  how you sell, how you hire, how you train, how you execute, how you measure, and how you define what's worth doing.

If you treat it like a new tool, you'll miss both the real opportunity and the real risk.

How AI changed what a software company sells

Before AI, the central question in every client engagement was: "What can we build for you?" That question still exists. But it's no longer enough.

Somnio had already been moving away from pure staff augmentation toward a product-first model. AI accelerated that shift significantly. The question became: "What problem are you actually trying to solve, and what's the best way to solve it in this new context?"

Our positioning today is AI Native Agency. It means a few concrete things in practice:

We use AI internally across every area of the business,  not just in development. We proactively help clients identify where AI creates leverage in their specific product or workflow. We design products with AI capabilities built in from the start, when it genuinely makes sense. And we've started accompanying transformation processes, not just shipping projects.

That last point matters more than it sounds. There's a real difference between a client who needs a product built and a client who needs to transform how their business operates. The first is a delivery challenge. The second requires a completely different kind of partnership.

Before, you sold execution capacity. Today, you also need to sell transformation capacity.

What AI-era hiring actually looks like

The hiring criteria at Somnio shifted significantly over the last two years.

A few years ago, there were still roles built around repetitive, manual tasks: coordination work, data entry, operational support. Today, every new role has to justify why that work couldn't be better handled by AI or a well-designed automation.

That's not a cost-cutting framing. It's a focus question. If human attention is going toward something a machine can do, you're misallocating the most valuable resource on your team.

What we actually look for now:

Strong technical foundation: The person has to truly understand architecture, quality, security, and scalability.

Judgment to use AI:Knowing how to use tools isn't enough: you need to know when they work, when they don't, and how to integrate them well.

Product and business thinking: Understanding the why, not just the how.

Fast learning capacity: Because everything is changing too fast to rely only on what you already know.

Communication and collaboration: The more automatable the mechanical parts become, the more valuable judgment, clarity, and the ability to work well with others become.

Soft skills: They matter more every day.

The pattern underneath all of this: the relative value of isolated technical knowledge went down. The value of applied judgment went up significantly.

I've started making a distinction more consciously,  the difference between a programmer and a developer. A programmer writes code. A developer understands problems, designs solutions, and uses AI to multiply impact. The market is beginning to price that difference very clearly.

The real obstacles in AI adoption and how to move through them

The main resistance we encountered wasn't skepticism. It was fear. And after fear, the most common pattern was superficiality. People wanted to talk about AI without actually changing how they worked. There was enthusiasm in conversation but almost no change in habits, processes, metrics, or decisions.

Leadership compounded the problem. If a leader declares that AI is a priority but then doesn't change priorities, doesn't invest in training, doesn't measure adoption, and doesn't redefine expectations,  the real signal sent is that it doesn't matter that much. People read that signal accurately.

There was also a cultural resistance we underestimated: accepting that ways of working that had been excellent for years are no longer sufficient. That hits organizational identity in ways that are difficult to name directly.

And then the time problem. Many people said they wanted to learn but couldn't find the space to actually study, experiment, fail, and try again.

The way we moved through this wasn't with inspirational talks. It was with clear direction, practical training, real experimentation, and clear expectations. Motivation, yes!  but without naivety.

What leading an AI transformation actually requires

The first thing I've learned as a CEO is that you can't fully delegate AI transformation. You have to understand it yourself. Not at a surface level. Not just enough to ask the right questions in a board meeting. You need to actually use these tools, live through the friction, and experience what changes in your own work.

Think about the shift from a paper notebook to a computer. It wasn't just a new tool. It was a cultural, operational, and mental shift. If the CEO doesn't genuinely incorporate it, the organization won't either, regardless of what the strategy deck says.

The second thing I've internalized: the real challenge isn't technology. It's the speed of organizational adaptation.

Every company today can access the same AI tools. The advantage isn't in access. It's in who learns faster, who changes habits faster, and who manages to move the team before the window closes.

For me, leading this transformation has meant:

• Being proactive, not reactive to external pressure
• Starting with the end in mind: what does this organization look like in three years if we adapt well?
• Managing time fiercely: AI transformation can't be a side project
• Working closely with managers so the change actually reaches teams
• Creating AI demo days and internal learning channels
• Rewarding real adoption, not just talk about it
• Making AI part of the daily language of the company
  

One more shift that's changed how I think: I no longer think about teams as just a combination of skills and headcount. I think about them as a combination of capabilities, context, memory, and AI amplification. I had a conversation at 11pm recently about what an "organizational chart" actually looks like when you design it around that logic. That question isn't theoretical anymore,  it's becoming a real design decision.

AI and jobs: the question worth asking

I don't think the most useful framing is "will AI replace people or not?" That simplifies something more nuanced and focuses attention in the wrong place.

What's actually happening: AI is replacing tasks, redefining roles, and raising the expected productivity standard across every industry. It's not a story of disappearance. It's a story of redistribution.

What I expect to see more of over the next few years:

Fewer purely operational or repetitive roles. More people functioning as "mini CEOs" of their own scope,  with context, judgment, and accountability. More weight on roles that require integration, design thinking, leadership, and deep business understanding.

Most positions won't disappear. But they will transform  in the value they're expected to deliver, in the skills that justify them, and in the output that makes someone irreplaceable.

The question for organizations isn't just "which roles are redundant?" It's: what capabilities do we need to build to stay competitive?

The question for individuals isn't "will AI replace me?" It's: how do I become more valuable working with AI than I was without it?

Those are different questions. They lead to different decisions. And they're the ones worth spending serious time on.

The companies that come out ahead won't necessarily be the largest. They'll be the ones that manage to combine technology, talent, learning velocity, and business focus, faster than their peers.

AI transformation leadership is a leadership problem before it's a technology problem.

The shift isn't going to stop. The question isn't whether it will arrive fully. The question is how prepared we'll be when it does,  in our teams, our products, our clients' businesses, and our own way of working.

If you want to watch the full conversation, you can find it here.

FAQ

What is AI transformation leadership?

 AI transformation leadership is the practice of guiding an organization through the structural, cultural, and operational changes required to make AI a core capability,  not just a set of tools. It requires the CEO to understand and model AI adoption directly, not just delegate it.

What does it mean to be an AI-native company? 

An AI-native company redesigns its internal processes, hiring criteria, and client offerings around AI from the ground up,  rather than layering AI tools on top of existing workflows. At Somnio Software, this means every new role is designed with AI in mind, every product engagement considers AI use cases proactively, and the business is measured against AI-era productivity standards.

How do you measure AI adoption inside a company?
Measuring AI adoption means tracking real behavioral change, not just tool access. Look at whether AI is changing the speed and quality of actual deliverables, whether managers are setting AI-integrated expectations, and whether daily work decisions reflect new ways of operating,  not just new tools available.

What skills matter most in an AI-driven hiring market? 

Technical foundations still matter, but the skills that have risen significantly in value are: judgment (knowing when AI helps and when it doesn't), fast learning (the ability to adapt as tools evolve), and communication. The ratio has shifted, less weight on isolated technical knowledge, more weight on applied thinking and business understanding.

What's the biggest leadership mistake in AI transformation? 

Treating AI as a technology initiative instead of an organizational change. If leadership signals that AI is important but doesn't change priorities, resource allocation, or expectations,  the real message received is that it doesn't matter that much. The gap between stated strategy and real behavior is where most AI transformations stall.

Closing Insights

At Somnio Software, we work closely with companies navigating exactly this kind of transformation,  designing and building high-quality digital products that take full advantage of what AI makes possible today. 

If you're thinking through where AI fits in your product strategy or how to build a team that genuinely adapts, we'd love to have that conversation.