AI-tisation. A View On The Future Of AI.
“Great. Another opinion on AI. Just what I needed.” I can practically hear you thinking it. And honestly? I’d probably have the same reaction.
Over the past few years, suddenly, everyone has something to say about AI—how it’s evolving, who’s cashing in, and where it’s all headed. But that’s not the point of this article.
Last week, I—Cas—was talking with Milan (yes, the co-author of DualEdge Invest) and another friend (who deserves a shoutout, just in case he’s reading) about AI’s real-world applications in our fields. The more we discussed, the clearer it became: AI is embedding itself deeper into society. And one thing we all agreed on? Once you start using AI to boost efficiency at work, there’s no going back.
That conversation, along with others and my own reflections, got me thinking: How exactly is AI adding value—especially within firms? That’s what I want to explore here. You don’t have to agree with me. In fact, I’d love to hear different perspectives, because I know I’ll miss some angles. AI is shaping the near future, and the more diverse the discussion, the better.
So, if at any point you think, “Hmm, I don’t really agree,” feel free to drop a comment or reach out.
The Public Opinion
Let’s talk numbers. Like I said earlier, once people get a handle on AI, they don’t look back. The value it adds far outweighs the minimal effort needed to learn it. In finance terms: an investment too good to ignore. And businesses are starting to catch on.
Over the past year, companies have seriously ramped up their AI investments. A McKinsey & Company survey found that 72% of respondents now use AI in at least one job function—a big jump from 55% last year. Even more telling? Half of them use AI in two or more areas of their business.
But it’s not just about the workplace. People are incorporating AI into their personal lives as well, further embedding it into daily routines. The more normalized it becomes outside of work, the more inevitable it is within it.
So, overall, the public perception of AI is shifting. Not only is it proving useful, but companies are reassessing the risks. In 2023, 34% of businesses saw labor displacement (a.k.a. job losses) as a major concern. By 2024, that dropped to 27%—suggesting that instead of replacing workers outright, firms are now looking at AI as a tool to enhance their workforce rather than eliminate it.
At the same time, other risks are climbing. Concerns about inaccuracy and intellectual property (IP) infringement have grown significantly—rising from 56% and 46% in 2023 to 63% and 52% in 2024.
To me, this shift signals something important: companies are moving beyond the initial AI panic and are now engaging with it more seriously. Instead of just fearing job loss, they’re actually using AI and considering its real risks—accuracy, ownership, and ethical concerns. In other words, AI is no longer just a buzzword. It’s part of the system now.
The Power of AI
Surveys can be useful, sure, but let’s be honest—they mostly just confirm what we already know: AI isn’t a passing trend; it’s here to stay. Now that we’ve cleared that up, let’s get into my actual thoughts. For me, AI’s future value boils down to three main categories:
AI-tisation – eliminating repetitive tasks.
Customization – analyzing specific data sets.
Integration – connecting different systems seamlessly.
AI-tisation - The Smarter Automation
AI-tisation is like automation but with an upgrade. Traditional automation follows rigid, predefined steps, whereas AI works with guidelines, giving it more flexibility. This is an important distinction—just because something is automated doesn’t mean it’s AI-driven. Ever seen those “AI-powered” trading bots in YouTube ads? (No, I’m still not interested.) Many of them just follow basic “if X happens, then do Y” rules. That’s not AI; that’s glorified scripting. A word of advice: be wary of anything labeled “AI” without solid proof.
That said, real AI-tisation is making life much easier. Take coding, for example. Large language models (LLMs) can generate code based on patterns in massive datasets. They don’t “understand” code in the way a human does, but they can still boost efficiency. Instead of writing out every single line, developers can provide context, and the AI fills in the gaps—leaving them to focus on the more complex, value-adding aspects of programming. This is the real advantage: AI doesn’t replace human skills; it amplifies them.
And let’s be real—you’re probably already using AI-tisation without even thinking about it. Ever asked ChatGPT for help structuring an email or summarizing an article? That’s AI-tisation in action. It’s broad, it’s convenient, and it works. But that brings us to our next level: customization.
Customization - AI That Knows You
According to a McKinsey survey, about half of organizations rely on off-the-shelf AI solutions—things like text processing tools that help with writing, email drafting, or internet searching.
These are useful, but they’re also generic. The real value of AI? Custom tools trained on specific datasets.
The beauty of customized AI is that it doesn’t need extra context every time you use it. No more, “Imagine you’re a Substack writer…” prompts. Instead, the model already understands the relevant data and operates within those parameters. This also means it can recognize patterns—and more importantly, deviations from those patterns—way before humans can.
For example, AI in equipment maintenance can analyze machine data, predict potential failures, and flag issues before they become major problems. This alone has already saved businesses huge amounts in repair costs. And when these custom AI solutions start talking to each other? That’s where things get really interesting.
Integration: The AI Ecosystem
AI is great in isolated cases, but its true potential emerges when multiple systems work together. The more data flows between them, the better the predictions become. Think about IoT (Internet of Things) combined with AI—smart refrigerators analyzing your grocery habits, wearables tracking your health, and AI nutrition planners suggesting meals. You confirm the plan, and an ordering system (not necessarily AI-driven) gets your groceries delivered. Meanwhile, an AI-powered inventory system at Walmart predicts stock levels and triggers restocking orders. And the cycle continues.
This interconnected AI approach applies to industries far beyond groceries. Imagine factory AI that detects machine wear and tear, feeding data into a production AI that optimizes workflows, which then communicates with supply chain AI to ensure materials arrive just in time. At a higher level, an overarching AI could identify patterns across the entire operation—patterns we wouldn’t even think to look for.
And here’s the kicker: once AI starts integrating more deeply, it creates a snowball effect. More AI usage leads to more development, which leads to even more adoption. Ever since ChatGPT went mainstream, AI tools have been popping up at an insane pace—not just because we’re noticing them more, but because AI itself is fueling new innovation.
Who Benefits the Most?
The companies that will gain the most from AI are those with:
A lot of repetitive, non-value-adding tasks (think report writing, endless emails).
Massive amounts of data that require precise analysis.
A complex, scattered system landscape that could be streamlined through integration.
Sound familiar? This describes big, bureaucratic organizations—the same ones with deep pockets to fund AI projects. But here’s the good news: smaller startups and scale-ups can take advantage too. As demand for specialized AI tools rises, these companies can step in with tailored solutions, benefiting the entire economy.
If you’re curious about what a custom AI tool looks like in practice, check out the AI-generated podcast at the end of this article. I used Google NotebookLM to generate insights from an OECD report—no extra context needed, just pure, focused AI processing. If nothing else, it’s a great example of where we’re headed.
AI isn’t the future—it’s the now. And if we use it right, it won’t replace us; it’ll make us better at what we do.
Some Real Risks
AI isn’t all sunshine and efficiency—there are real risks too. But I’m not here to rehash the usual concerns like bias, inaccuracy, or AI making up nonsense. Those are growing pains, and I’m confident they’ll improve as AI evolves. What really interests me are the deeper, structural risks that come with this shift.
Data is King… But Not Everyone Has a Throne
AI runs on data—lots of it. But here’s the problem: many small and medium enterprises (SMEs) are just now getting into the data game. Some are still figuring out basic digitalization, let alone sophisticated AI implementations. And without large, high-quality datasets, AI models simply don’t work well. If a company has only just started collecting data, the AI’s predictions will be all over the place, anomalies can’t be detected properly, and the whole thing ends up being more frustrating than helpful. In short: AI is only as good as the data it’s trained on, and for many businesses, that’s a big (and expensive) hurdle.
Security Nightmares: Who Owns Your Data?
AI tools can be black boxes. Does your input get stored? Is it used to train future models? Does it get deleted after a while? These are critical questions that most users never ask—and that AI providers don’t always answer clearly. The last thing you want is confidential business data unknowingly feeding a public AI model. Beyond that, AI-powered scams, deepfakes, and fake news are already a problem, and they’re only going to get worse. The solution? Stay skeptical. If something feels off, verify the source. Critical thinking will be more important than ever.
Balancing AI and Human Judgment
Early on, people treated ChatGPT like a magic oracle. It seemed like it could do anything—until it confidently started spitting out complete nonsense. That’s why human oversight is essential. Sure, AI can generate code, summarize documents, and even make decisions, but if you don’t have at least a basic understanding of the subject, you won’t know if it’s making sense or just fabricating plausible-sounding garbage.
Take coding as an example. A seasoned programmer can instantly tell if AI-generated code is solid or if it’s riddled with mistakes. He didn’t write it himself, which saves time, but he’s acting as a quality control layer. This kind of human-AI collaboration is the sweet spot. The real risk? Going too far in the other direction—relying so much on AI that human judgment erodes. The key is balance: automate, but don’t overdo it. AI should enhance human decision-making, not replace it entirely.
At the end of the day, AI is a powerful tool—but like any tool, it needs to be used wisely. The businesses that strike the right balance between automation and human expertise will be the real winners.
Final Remarks
AI is a game-changer, but like anything powerful, it comes with trade-offs. It can automate tedious tasks, boost efficiency, and uncover patterns we’d never see—but only if you have the right data. It can make decisions faster than any human—but only if security risks are managed. It can enhance human expertise—but only if we stay critical and don’t blindly trust its output. The sweet spot is somewhere in the middle: leverage AI where it adds value, but don’t lose sight of the human judgment that keeps everything in check. Balance is key—use AI, but don’t let it use you.
On a different note, stay tuned for Friday’s article, where I’ll dive into Economic Value Added (EVA)—a fresh way to measure a company’s real financial performance. If you’re tired of traditional valuation methods, this one’s for you!
📢 What’s your take? AI is shaping the future, but there are always different perspectives. Do you see the risks differently? Have I missed an angle? I’d love to hear your thoughts—drop a comment below and let’s discuss! 👇
🔔 Don’t miss out! This Friday, we’re diving into Economic Value Added (EVA)—a fresh way to measure a company’s real financial performance. If you’re tired of traditional valuation methods, this one’s for you. Stay tuned!
AI-generated podcast on the OECD report
References
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
Lane, M., M. Williams and S. Broecke (2023), “The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers”, OECD Social, Employment and Migration Working Papers, No. 288, OECD Publishing, Paris, https://doi.org/10.1787/ea0a0fe1-en.
