I’m writing to you at a very special moment in my career. As of May 2026, I have officially closed the chapter of my traditional 9-to-5 employment to dedicate myself fully to our growing community. This transition has finally given me the luxury of time—time to reflect deeply on where we stand as an industry. And let’s be honest: we are currently at a crossroads that many feared, while others anticipated with perhaps too much hype.
I receive countless questions from you about how the work of a data analyst actually looks in today’s business reality, rather than on the glossy slides of tech conferences. Is it still worth learning SQL? Has Python been “eaten” by large language models? Is the intellectual drain I’ve mentioned in my videos a reality or just a theory? Today, I want to break this down for you, piece by piece, from the perspective of someone who has spent over seven years architecting data systems.
The Big Lie About the End of the Data Analyst Era
Let’s start by bursting the bubble that has been surrounding us for years. If you follow tech media, you’re likely familiar with figures like Dario Amodei. For a long time, we’ve been hearing regular predictions that “in six months, the world as we know it will cease to exist,” and that AI will take over every form of cognitive labor. I understand this narrative. If you’re running a multi-billion dollar company that produces AI models, you have to sell a vision of total revolution to keep investors interested and the hype cycle spinning.
However, the reality in a standard business—the kind where I’ve worked as a data architect—looks very different. It is not the case that analysts have disappeared because AI took their jobs. That is perhaps the biggest lie sold to us in recent years. Yes, the tools have changed, and the pace has accelerated, but the need for critical thinking, business context, and a deep understanding of data structures is higher today than it has ever been. In fact, as data becomes more abundant and AI-generated, the “human filter” becomes the most critical part of the pipeline.
The Trap of Perfect Communication and the Loss of Authenticity
One of the first phenomena I noticed in this AI-saturated era is the communication paradox. Just a few years ago, the ability to clearly formulate thoughts, write meaningful reports, and extract the essence from a chaos of data was a “superpower.” Today, anyone can generate an email with perfect bullet points using a single prompt.
The problem is that when everything looks perfectly written, we stop reading it. We are being flooded with a wave of content that is grammatically flawless but hollow inside. As analysts, we often fall into this trap—we let AI generate summaries of our findings, losing our unique “voice” and business perspective in the process. In a world where intelligence is becoming a mass-produced commodity, authenticity and the ability to prove that a real thought process stands behind the numbers have become your greatest bargaining chips. When everyone can produce a “perfect” report, the person who can explain why the data matters in a face-to-face strategic meeting is the one who gets promoted.
Intellectual Drain: Why Juniors Are Facing an Uphill Battle
This is a topic that hits close to home for me, as at KajoData I help people start their careers every single day. There is a real danger that I call “intellectual drain.” To become a great data architect or a senior analyst, you must go through the stage of building “muscle memory.” You need to make mistakes in hundreds of SQL joins, and you need to spend hours debugging Python code to truly understand how these mechanisms work under the hood.
If you rely solely on AI to generate your code from day one of your learning journey, you lose the opportunity to build these essential foundations. I see this happening more and more: people can copy and paste code, but they don’t understand why it works (or why it suddenly stopped working). This creates a generation of “black-box operators.” In two or three years, the market will harshly verify these gaps. Those who took the time to master the craft will be worth a fortune because only they will be able to fix what AI breaks in complex, non-standard corporate systems.
The “AI Ready” Fever in the Corporate World
I’ve sat through dozens of meetings where the main topic was the question: “Is our organization AI ready?” Often, these discussions feel more like modern-day shamanism than engineering. Companies are desperately searching for use cases for artificial intelligence just so they can report to the board that they are keeping up with the times. We look for places to put chatbots in knowledge bases or automate simple tasks, but rarely does anyone ask: “What is the actual business value here?”
Being “AI ready” is largely a matter of PR and stock valuation. Investors love the word “AI.” Inside the companies, however, there is a constant struggle with reality. Data is messy, information silos are massive, and technology that looks perfect in a demo often “hallucinates” when faced with a real, dirty database. This is where the role of the modern analyst comes in: someone who can separate the hype from actual business value and ensure the data being fed into these models is actually worth something.
Prompt Shamanism vs. Business Determinism
In data analysis, especially in finance, healthcare, or logistics, we need deterministic results. If I ask a system for the profit margin of the last quarter, I expect a specific, verifiable number—not a “probabilistic answer” that changes every time I refresh the query or tweak the wording of my question.
Currently, we are observing a phenomenon I call “prompt shamanism.” We add phrases to our queries like “think step-by-step” or “you are an expert who makes no mistakes,” hoping the model will listen. In a serious business environment, this won’t fly in the long run. We cannot base critical, multi-million dollar decisions on technology that is inherently non-deterministic. This is why foundations like SQL or a solid architecture in Power BI remain untouched—they provide the certainty that AI, for now, cannot guarantee 100% of the time.
Intelligence as a Commodity: What Does It Mean for You?
There is a growing concept that intelligence will become as cheap and ubiquitous as electricity from a wall socket. Models will write simple apps, create visualizations, and clean data for pennies. This might sound threatening, but look at it from another angle. If the “production” of an analysis becomes cheap, what gains in value?
Direction gains value. The ability to ask the right questions gains value. In a world of data overproduction, the most important person is not the one who can “click out” a report, but the one who knows which path the company should take based on that report. AI is brilliant at creating MVPs (Minimum Viable Products). I use it myself at KajoData to implement new ideas faster. But a stable, large-scale business cannot rely on the MVP level forever. It needs craftsmen who know how to put those blocks together in a way that doesn’t collapse under pressure.
Why I Still Bet on SQL, Python, and Excel
You might think: “Kajo, why do you still teach all these tools in KajoDataSpace if AI can do it faster?” The answer is simple: AI is your co-pilot, not your captain. If you don’t know the basics of Excel or SQL, you won’t know when your co-pilot is trying to land in a field instead of on the runway.
Technical knowledge gives you authority. It allows you to understand data architecture deeply enough to manage automation processes. I don’t want you to be someone who just copies prompts. I want you to be an architect who understands why source data is contaminated and how to fix it at the root, rather than just “polishing” the results with AI. Remember, technology changes, but business logic and the principles of working with data are universal.
Conclusion: Your Role in an AI-Driven World
Looking at the world in 2026, I see the future of analytics not as a battle between humans and machines, but as a new form of symbiosis where humans hold the steering wheel. The arms race will be won by those companies and specialists who combine the raw computing power of models with human experience, ethics, and the capacity for strategic planning.
Don’t fear the new, but don’t be fooled by the narrative that learning the basics is no longer necessary. It’s quite the opposite—the more processes are automated, the more valuable the “human factor” and a deep understanding of the craft become. I intend to spend the coming years helping you become exactly that kind of conscious, indispensable expert.
If this article helped you look at your career path from a different perspective and calmed some of your AI-related fears, please share it on your social media. Your support builds our community and allows me to create more content like this!
The article was written by Kajo Rudziński – analytical data architect, recognized expert in data analysis, creator of KajoData and polish community for analysts KajoDataSpace.
That’s all on this topic. Analyze in peace!
Did you like this article 🙂?
Share it on Social Media 📱
>>> You can share it on LinkedIn and show that you learn something new every day.
>>> You can throw it on Facebook – and perhaps help a friend of yours who is looking for this.
>>> And remember to bookmark this page, you never know if it won’t come handy in in the future.
You prefer to watch 📺 – no problem
>>> Subscribe and watch my English channel on YouTube.
Prefer to read in Polish? No problem!
Other interesting articles:
