AI in business:
3 takeaways from Beko Europe’s CEO on scaling AI
In this article
Summary
Three takeaways from Beko’s AI journey: use AI to free people from repetitive work, treat experimentation as essential but keep risks manageable, and don’t let short-term AI wins replace long-term strategy.
Artificial intelligence in business is changing
Three years ago, many leaders were still asking:
“Should we do something with AI?”
Now the question is much sharper: “What’s our plan, and where’s the impact?”
In Software Improvement Group’s 2026 AI Boardroom Gap report, we describe this shift and reveal the widening gap between AI ambition and reality in enterprise organizations. Honestly, the numbers speak for themselves.
- AI is present in enterprise portfolios but not yet dominant: From all the systems (in production) we analyzed at Software Improvement Group in 2025, roughly (only) 1.5% qualify as an AI system.
- 88% of organizations report using AI in at least one business function, and 64% say AI is enabling innovation. Yet, most enterprises are still experimenting or piloting AI, and only 39% report EBIT impact (often single digit).
- Despite $30–40B invested in enterprise generative AI, 95% of initiatives show zero return.
- Only 1% of executives describe their gen-AI rollouts as “mature.”
So yes, AI in business is everywhere. But the use of artificial intelligence in business at scale, with measurable outcomes and real oversight, is still where many organizations get stuck.
That’s exactly why I enjoyed recording the latest SIGNAL episode with Akin Garzanlı (CEO, Beko Europe). We didn’t want another “AI inspiration story.”, we wanted to zoom in on a real situation: What works (and what doesn’t).
How artificial intelligence in business shifted short-term vs long-term thinking
One insight from Akın has been looping in my head since we stopped recording:
Artificial intelligence in business is changing the balance between long-term strategy and short-term decision-making.
As productivity expectations rise, the cadence in the boardroom speeds up. More pressure for quick wins. Faster cycles. Immediate proof.
That can be helpful. AI can absolutely accelerate learning loops and remove friction.
But it can also distort priorities if leaders don’t actively protect the long-term direction while still enabling fast learning.
Which links to the second theme that ran through the whole conversation: Akın Garzanlı strongly beliefs that the real value of AI lies in removing operational burden, so people can spend more time on creativity, judgment, and innovation.
This might sound admirable, but it was impressive to hear how he was able to put that in practice at such a large and global company. I mean, with over 400 million customers across 130 countries, Beko is one of the largest home appliances companies in the world. Last year, they even won an award for their AI-infusion program. Beko’s company-wide learning and transformation initiative designed to embed artificial intelligence into daily work and accelerate adoption across all business domains.
Three takeaways from Beko Europe’s AI journey
There is of course lots to discuss and I would love for you to hear the entire conversation, but if you don’t have 30 minutes, here are my 3 takeaways.
1. Start where AI removes operational burden
A lot of organizations start their AI journey with the most impressive thing they can demo. However, the best examples of artificial intelligence used in business are the ones that remove friction and give teams time back.
In our conversation, Beko’s examples were grounded in exactly that logic, taking repetitive work off people’s plates so they can focus on higher-value decisions.
“Some view AI as a big replacement for people who are doing operational work which we were very against from the beginning. Our view is AI will help people to be to have more time for the creativity and do the stuff that are creating operational burden with the help of AI.” – Akın Garzanlı, CEO of Beko Europe (SIGNAL Podcast)
This is where artificial intelligence in business tends to land best: it reduces operational friction and creates space for better thinking.
A simple filter for when teams ask, “what should we do with AI?”:
- Does this reduce operational burden?
- Does it create time for judgment or creativity?
- Can we measure impact in cycle time, quality, or cost?
2. Progress rarely comes from playing it safe
Akin said something that captured the AI in business leadership challenge in one sentence:
“What I can clearly say is if you are looking for a way that is totally free of risks, then you probably won’t be so fast in the AI. So, you have to take risks, but the risks have to be manageable.” – Akın Garzanlı, CEO of Beko Europe (SIGNAL Podcast)
In my view, that’s exactly why the use of artificial intelligence in business needs governance, not to slow teams down, but to make risk manageable.
Too many organizations get trapped at the extremes:
- Move fast and hope (innovation without control)
- Wait for certainty (control without learning)
I feel the truth is somewhere in the middle: Create an environment where experimenting is encouraged, learning happens quickly, and mistakes are treated as part of growth, but all within boundaries.
In practice, “manageable risk” usually means you can answer questions like:
- Where do we pilot safely, and what does “success” look like?
- Who carries accountability?
- What security and compliance controls are non-negotiable?
This aligns with what we call out in the 2026 AI Boardroom Gap: Many organizations aren’t failing because the AI technology doesn’t work, but because the organization can’t support AI at scale.
3. AI speeds up decisions, so protect the long-term vision
If AI makes iteration cheaper, speed becomes addictive.
That’s why Akin’s “short-term vs long-term” observation matters so much at the board level. Don’t get me wrong, I’m not saying leadership should slow down. I’m saying they should find ways to move faster, responsibly.
Especially with AI evolving at breakneck speed, the gap between AI ambition and operational readiness only widens further without governance and when AI becomes invisible, oversight is impossible.
As an organization in 2026, you need to be able to show what AI technology you have, where it runs, who owns it, how it’s controlled, and that it’s safe and compliant.
Enterprise AI adoption and readiness
If you’re debating how to implement AI in business successfully, we can help. Feel free to reach out to me directly, or read more here
About the author
Werner Heijstek is the Senior Director at Software Improvement Group and host of the SIGNAL podcast, a monthly show where we turn complex IT topics into business clarity.
Werner is a software development, IT management and governance expert (Ph.D) with over 20+ years of experience in management of complex, distributed software environments. He’s consulted 100+ organizations world-wide on digital transformation and IT strategy and has extensive (10y+) experience in pre-sales as well as in leading large consultancy engagements, specializing in AI code governance, technical debt management, security, and IT due diligence engagements.
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