Summary: As AI reshapes the workforce, the most valuable professionals may not be pure technologists—but domain experts who also understand AI and machine learning. In this post, we explore how combining domain knowledge with AI/ML skills creates a powerful edge, and why these hybrid professionals are becoming increasingly sought after across industries.

According to the World Economic Forum, “AI skills could soon rival experience when employers select job candidates.” But what happens when domain experts gain AI skills? Isn’t that a powerful combination? Could they become the next rulers of the professional world?

Whether or not the day will come when domain experts are no longer needed—as Bill Gates once predicted—remains to be seen. In the meantime, a new type of professional is gaining ground. This isn’t about becoming a data scientist or a machine learning engineer—although both of which are still in demand. It’s about domain experts from a wide range of fields gaining AI/ML skills. And this is not a romantic notion or speculation—it’s already happening.

According to the Burning Glass Institute, employers are offering higher wages to professionals with data science skills across many occupations and sectors. The most substantial wage premiums go to those with the most technically “sophisticated” skills, including Data Strategy and Machine Learning, with an average wage premium of 14%. Meanwhile, a PwC study covering five countries (the US, UK, Canada, Australia, and Singapore) found that the wage premium for workers with specialist AI skills can reach up to 25%.

I’m currently enrolled in a data science and ML bootcamp myself. Alongside learning Python basics like for x in xs and if, elif, and else, one phrase I keep hearing is: domain expertise. For example, when setting threshold values in a variable correlation setting, the go-to person is always the domain expert.

Let’s take a look at how, in practice, domain experts are integrated into ML projects:

ML Project Phase	Role of Domain Expertise
1. Problem Definition	Critical: Helps reframe vague or complex real-world problems.
2. Data Collection	Important: Guides what data to collect and the relevance of variables to use.
3. Data Preparation	Essential: Informs how to handle outliers, interpret missing values, etc.
4. EDA	Important: Helps make sense of patterns, anomalies, and relationships in the data.
5. Modeling	Less Critical: but still feature engineering benefits from expertise.
6. Evaluation	Important: Guides the choice of evaluation metrics and interpretation of model outcomes.
7. Deployment	Context-Dependent: Needed if domain-specific constraints affect deployment decisions.
8. Maintenance & Iteration	Important: Helps spot when a model’s predictions are off-road.

As you can see, domain experts play a crucial role throughout the ML project pipeline. It’s no surprise that hybrid professionals are rising to the top

Relevance for Career Choice

You might ask: Why does it matter whether domain expertise combined with AI/ML skills is in high demand? Or maybe you’re thinking: Wouldn’t it be better to just become a full-fledged AI/ML engineer?

Let’s address that second question first. Becoming an ML engineer is certainly a solid choice—it’s a high-demand, high-prospect career. But the reality is, not everyone is meant to become a core data scientist or AI/ML engineer. And that’s perfectly fine.

The key message is this: if you’re a young person just starting out, there are still many rewarding occupations to choose from. However, it’s worth being strategic about the skills you add to your basket. AI/ML skills—especially those that complement your domain knowledge—are among the most valuable today.

If you’re a professional with years of experience, adding AI/ML skills can boost your career and expand your opportunities.

What Does It Mean to Have AI/ML Skills Without Being an ML Engineer?

As mentioned above, the more sophisticated the skill, the better, but, in general, it is about being AI-competent, even if you are not responsible for building models from scratch. This is, understanding what AI/ML can and cannot do, being able to collaborate with technical teams, and knowing how to use AI-powered tools relevant to your field.

Take the example of a lawyer. Building AI skills might include:

• Learning the basics of machine learning and NLP (natural language processing).
• Understanding how AI is used in legal tasks—such as contract review, eDiscovery, or legal research.
• Gaining hands-on experience with AI-powered legal platforms (like Harvey, Casetext, etc.).
• Learning prompt engineering basics to get more out of general AI tools like ChatGPT.
• Understanding ethical implications such as bias and explainability in AI systems.

Going a bit deeper might mean learning how to interpret AI model outputs, understanding how AI projects are structured, or even participating in interdisciplinary teams building legal tech solutions.