4 Tasks, 1 Skill: What AI Professionals Know About Surviving Automation
- S B
- Jul 10, 2025
- 8 min read
The Rise of the Data Wizard
8 years ago, I stepped into a professional role as a senior data scientist. They called me a unicorn. They said I was the future. Everyone wanted a data wizard. Or as my sister called me, the “biomagician.” After studying biological science as an undergraduate, I had transitioned into computational work. Now my value was tied to my coding abilities coupled with my domain expertise: I knew biology and chemistry. Landing on a team in the Biopharma industry seemed like a match made in heaven.
But have you heard the saying “nothing lasts forever”?
What if I told you that 8 years ago, my teammates and I joked that we were coding ourselves out of a job?
The Tasks vs. The Skill
When I joined, I wasn’t just replacing analytics professionals; I was filling a new kind of role. They were deep specialists, but I was an end-to-end architect, bringing deep domain expertise in biology and chemistry combined with coding abilities. I hadn’t gone to school to become a data scientist: that role didn’t exist yet.
I was a scientist who could code, uniquely positioned to fill a gap the technology boom had created. I could collect data (a task), clean data (a task), analyze data (a task), visualize results (a task), and present findings: the skill. The skill was connecting all these tasks into business insight. And even then, I knew, as did my teammates, that we were on borrowed time. Because even though Gartner said every company needed to invest in data science, we knew this wasn’t the end game.
If you look back at this paragraph you will see that I was simply the prototype for AI automation: 4 tasks, 1 skill.
4 tasks. 1 skill.
That’s what made us valuable.
That’s what made us vulnerable.
The Evolution and the Reckoning
My title evolved from Senior Data Scientist to AI Solutions Lead. The job was clear: build, implement and scale AI tools and solutions while also connecting with the business to demonstrate their value and drive adoption.
I loved my job.
I still love my job.
Then it happened. November 2022. The day of reckoning came. ChatGPT was released, powered by generative AI (GenAI). And what was once an academic discipline entered the market as a product. Productized consumer-facing AI arrived. The end game had arrived. And with it, the beginning of transformation.
4 tasks. 1 skill.
Became 1 skill.
The Moment of Truth
I read the papers. My family members, mainly my sister, sent me text messages. “Have you seen it? What is it? Is it really that good?”
Yes. It’s AI. Yes.
It was … that good. See, the end game was powered by gains in computing power and in the science behind building artificial intelligence: breakthroughs in how much data our computers can process and new methodologies for training AI models.
The underlying science, neural networks, was over 60 years old. As a graduate student I had written code “by hand” and then on my computer to build small neural networks.
And so when I sat down with the ChatGPT interface for the first time it was both a reunion with an old friend and a handover of responsibilities.
It was a reunion because my crude programs were astronomically simple compared to the sophisticated AI models that had been unleashed with unprecedented scale and capability. I was no longer just coding my predictions. GPT wasn’t just doing the work: it was talking me through it.
It was a handover of my responsibilities because GPT would replace my 4 tasks with efficiency. Tasks that previously took weeks dwindled down to 2 minutes. I sat in awe, in reverence and admiration. But mostly, I sat with the truth: it was time to grow again. I had done it before from biology to AI, where would I go now?
The Early Warning Signs
Remember how I mentioned transitioning from biology to AI? What I left out was the sobering tale I witnessed during that time.
As a student I had witnessed biology labs shut down, graduate students displaced from a lack of funding in research grants. Not because there weren’t funds available. But because the funds were going elsewhere. And where were those funds being funneled to? Back then AI wasn’t the common term. It was machine learning or specifically to our discipline, bioinformatics and computational biology. Suddenly publish or perish had become adapt to technology or perish.
This experience was as much a part of my training as biology 101. I learned at a young age that when technology disrupts it leaves no stone unturned.
As a student I was somewhat sheltered and protected. I didn’t need to put food on the table. But now I was facing disruption as a seasoned professional, not just any professional, an AI professional. The stakes were higher, and the irony was unmistakable: the very field the market said was future-proof was now automating itself.
Adaptation or Perish
So I played with ChatGPT. I signed up. I used it at home. And then in January 2023 I knew it was time to do what I had done before.
Many times when we hear career journeys they are polished and pristine. My path was rocky, rough and disconnected. I didn’t land in Biopharma because of planning. I got there because of adaptation. I had learned that out of challenges comes opportunity.
So in early January 2023, I met face to face with GPT and said, “Today we’re going to build an app together.” I had decided that I wouldn’t perish.
And this app retained everything I had built over the last 8 years. My workflow. My favorite coding language. I simply integrated GPT into my way of working to prove not just to my organization but to myself that I was ready. The app ran. GPT talked back. I would survive. I knew I could thrive. But even then I knew that my workflow wouldn’t. It was the last time I had used the tools that I built my career on. The end game came and I needed new armor.
The New Armor
What would that armor look like? I couldn’t outcode it. I couldn’t work faster. I could simply invest in the one skill that remained. Presenting findings in an accessible way was often what separated a good data scientist from a great one. But here’s the plot twist. This skill would become equally valuable behind the scenes as it was in the spotlight. My 8 years weren’t in vain. I could build GenAI apps. I could present them. But more importantly, I could communicate with GenAI.
You see many professionals don’t realize that the skill we overlook the most is often the most valuable. Communication about AI was one skill, but the GenAI evolution introduced a new skill: communication with AI. This means learning to ask the right questions, give clear context, and work together with AI to get what you need.
The industry would call it prompt engineering. I called it talking to my new teammate.
What’s particularly unsettling is that today’s AI isn’t just out-coding me: it’s starting to compete at the level of domain expertise, demonstrating knowledge in biology and chemistry that rivals years of specialized education.
Despite this unsettling reality, I continue to work with AI daily. Today, I use Copilot to help with email drafting and document creation. I help build and scale GenAI solutions. I use AI to help with desk research, I brainstorm with it, and I use it to make prototypes of web-based applications. It’s become a partner. But I won’t lie: sometimes I wonder what’s next as it keeps getting better.
Self-Actualization in the Age of AI
So the question that remains is what does the new job market look like for us? Where do we fit? Are we still on borrowed time?
Actually I would argue that we have self-actualized. As AI professionals we are exactly where we meant to be. And we are uniquely positioned to transition into an AI-powered world not because we can code or do math, but because we know best what it means to be both replaced and complemented by artificial intelligence.
And with that knowledge we can provide a framework for professionals in and outside of our field.
Some dos and don’ts:
Do: Accept this change
Accept that AI will transform how work gets done rather than fighting the inevitable
Don’t: Resist
Resistance wastes energy that could be spent on adaptation
Do: Read up on how these models and systems work
Understanding the technology helps you work with it more effectively
Don’t: Try to re-engineer your career
Build on your existing strengths rather than starting from scratch
Do: Adopt new tools
But be selective about which ones add real value to your work
Don’t: Adopt every tool
Tool fatigue is real — focus on what actually improves your productivity
Do: Collaborate with AI
Treat it as a capable partner that can enhance your capabilities
Don’t: Allow it to lead
Maintain human judgment and final decision-making authority
The Real Shift: Partnership, Not Replacement — Even as AI Is Replacing Professionals
Now let’s be clear. I didn’t step aside for AI. I embraced it. There’s a difference. Can I still code? Yes. Do I still code? Only if necessary. Why spend two weeks coding when AI tools can deliver templates or at the minimum a starting point in minutes? And let’s not forget the obvious: AI requires supervision. You can’t supervise what you don’t understand.
Beyond coding and data crunching, I also present and strategize. And I gladly invite AI into those areas as well. But the delivery is all me. And this is where every professional must maintain their edge. Your skills. Your competence didn’t suddenly disappear because AI tools write better. The value proposition shifted. Now you must be confident enough to extract your intrinsic value and be prepared to compete not only against humans for roles but against machines.
Bringing It Home
For most of my career, few people understood what I did for work. Then ChatGPT came and in a single instance I became both villain and hero: a face tied to disruption. What people don't realize is that we are equally vulnerable to displacement. The truth is, AI is replacing AI professionals, not in theory, but in practice. As I said, 4 tasks, 1 skill. 4 automatable tasks and 1 human skill.
If you ask me what’s next, I think that after the age of AI, we will enter the age of EI: emotional intelligence. Because with all the automation happening, we will be able to look up from our desks for the first time in a long time and truly see each other. When AI handles the routine cognitive work, human connection and emotional understanding become the differentiating factors in how we collaborate, lead, and create value together.
I will continue to work with AI.
I will embrace AI.
I will challenge AI.
I will champion AI.
But I will not babysit AI.
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"AI is the tool, but the vision is human." — Sophia B.
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About the Author
Sophia Banton works at the intersection of AI strategy, communication, and human impact. With a background in bioinformatics, public health, and data science, she brings a grounded, cross-disciplinary perspective to the adoption of emerging technologies.
Beyond technical applications, she explores GenAI’s creative potential through storytelling and short-form video, using experimentation to understand how generative models are reshaping narrative, communication, and visual expression.
