In the recent blog, we talked about AI being a team sport. Today, let’s dive deeper into building a winning AI team. It is not just about collecting diplomas—it is about assembling practical, hands-on skill.

Projects involving AI solutions are often complex and demand more than one specialist. But what if the specialists are only specialists on paper, having learned theories that are now outdated? Successful projects cannnot be conducted without a proper, up-to-date, set of competencies.

AI skills hand in hand with diplomas?

The fast-paced AI reality challenges us with the need for constant upskilling. At the same time, the GenAI revolution (powered by transformers) has been commercially viable for less than a decade. That means the mythical "10-year GenAI engineer" simply does not exist.

So how do enterprises build the AI muscle they need?

By recognizing that degrees are only one part of the equation—and by combining smart hiring with serious upskilling.

The capabilities that matter the most today include:

Data Wrangling and Preparation

Good AI needs good data. Practical skills in data cleaning, transformation, and augmentation (think Pandas, SQL, or real-time pipelines) are foundational.

Model Fine-Tuning and Experimentation

It is not enough to pull a pre-trained model off the shelf. Talent must adapt models to business needs, tune hyperparameters, and deeply understand evaluation metrics.

Deployment Pipelines and MLOps

A model sitting on a laptop delivers zero business value. It needs to be deployed into environment, where real users or systems can access it. Building robust pipelines (Docker, Kubernetes, CI/CD for ML) to get models into production is essential.

Strong Domain Understanding

Even the most accurate AI model can fail if it does not reflect the real-world context of the industry—whether that is legal, healthcare, or retail. That is why domain experts (or subject matter experts) are necessary in any AI project, working alongside product leaders to make sure developed solutions are relevant and impactful.

A need for future-proofing

According to McKinsey, up to 55% of tasks in software development can now be accelerated by GenAI tools. And a BCG study stresses that upskilling programs—not just external hiring—are critical to future-proof AI teams.

In today’s dynamically evolving market, degrees alone can’t guarantee readiness. Practical skills, agility, and cross-disciplinary collaboration are the real differentiators.

Is your organization hiring, upskilling, or both, to meet its AI ambitions? Let's talk via hello@joinexperthub.com or LinkedIn.