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Do you think only mathematicians and software engineers can work in AI? Well, if you do, you're wrong. Many successful people in data science and AI don't have a technology background.
So, yes, even if you start your career in marketing, psychology, law, design, etc., you can still move to AI.
There are five practical ways to do this:
1. Become an AI person on your team
You do not need permission to start using AI with your team. Well, most of the time, you don't. One problem is sharing AI tools and company data. Nevertheless, be someone who explores those tools, becomes familiar with them, and perhaps brings more efficiency to your team.
Do you know how Excel Champion or SQL God is on every team? You can become that person in AI. The idea is to start small, for example.
2. Learn the technical basics
You don't need to start coding your machine learning models immediately. Start with the basics of machine learning and AI. Be familiar with basic terms and tools.
Here's an overview of the technologies you need to know.
There are also tools that will help you get used to yourself.
More information resources:
3. Position yourself as an AI translator
AI does not exist in vacuum. It is there to solve the actual problem. When you're talking about business issues, machine learning and AI need domain expertise to provide the right solution. Who do you think provides that expertise? That's right. you!
Use that knowledge to position yourself as a bridge between AI translators, high-tech and non-technical stakeholders. you can:
- Convert business problems into data problems
- Know how AI fits them
- Machine learning model assumptions are flawed
- Describes model output to non-technical stakeholders
Then you start by understanding certain aspects of machine learning modeling. For example, translate model results such as confusion matrix and accuracy into actual effects. From this high level of understanding of AI, if it is the goal, we can slowly move forward to building a real model.
4. Start with the no-code or low-code tool
You don't need to work with Python proficiency for years before you start building a less complicated machine learning model. Today, there are already tools that allow you to build codeless or low-coded AI projects using the drag-and-drop interface.
They also help you position yourself as a translator. These tools + knowledge of your domain allows you to show:
- Understanding real-world problems
- Identify AI solutions
- Use that AI solution to solve the problem
Here are some useful tools.
| category | tool | What you can do |
|---|---|---|
| No Code AI Builder | lobe.ai | Train an image classifier with a drag and drop UI. |
| Teachable machine | Build a simple classification model in your browser. | |
| Monkeylearn | Create a custom NLP model for emotions, topics, or intentions. | |
| Clearly ai/zams | Upload the CSV and perform binary classification or regression. | |
| Low Code AI Builder | Naim | Build ML workflows using visual nodes (suitable for low-code, tabular data). |
| Datarobot | Upload data, select models, and deploy with minimal coding. | |
| Microsoft Azure ML Designer | Build and deploy machine learning models using drag-and-drop modules for data preparation, training and evaluation. | |
| AI-powered creative and productivity tools | Runway ML | Removes the background of the video and generates an image from the text. |
| Durability | Build your business landing page in seconds. | |
| Jasper AI | I will write ad copy, product descriptions, and blog intro. | |
| Canva AI | Auto-generated captions, remove backgrounds of images. | |
| Concept AI | Summarize notes, draft content and extract key points. | |
| Description | Edit podcasts and videos like text documents. | |
| chatgpt | Brainstorming ideas, report summary, draft content. |
5. Pivot to the role of Ai-Adjacent
A great start to pivot into AI is moving to a role that requires knowledge of AI but does not require the construction of a real model. Such positions are as follows:
- Project Manager – for coordination between stakeholders and machine learning engineers/data scientists
- Technical Writer – To document your workflow and create user guides
- Product Designer – To understand how users interact with AI systems
- Policy Analyst – to flag risks such as fairness and explanationability of AI systems
All these positions give you the opportunity to learn as you go. As AI is becoming more and more part of many roles, it can provide a solid foundation for moving towards building a real model.
Conclusion
Data scientists and machine learning engineers are not the only positions that work with AI. Many people from non-technical backgrounds do so too.
During the transition, don't amortize what you already know for no use. Find the intersection of machine learning and domain knowledge and start from that point. Next, knowing more about AI will allow you to decide whether you want to build a real machine learning model or to bridge between technical and non-technical stakeholders.
Nate Rosidy Data Scientist and product strategy. He is also an analytics teaching adjunct professor, founder of Stratascratch and a platform that helps data scientists prepare interviews with real-world interview questions from top companies. Nate writes about the latest trends in the career market, provides interview advice, shares data science projects, and covers all SQL.
