Lack Of Data Scientists: The New AI Roadblock

As AI systems are being embedded in everything, there is one powerful differentiator in today’s business environments: the ability to leverage and act on data. AI and ML systems are as strong as the data that supports them, and data scientists are becoming a critical role.?

According to the U.S. Bureau of Labor Statistics projections, nearly 28% in the number of jobs will require data science skills by 2026. However, while the demand is growing every day, there is an important lack of professionals.??

But why is this role so important for AI and ML adoption? How are businesses facing this data science talent gap? Today I want to explore some key topics of a roadblock that is expected to reach many organizations in the upcoming years.?

What’s the Role of Data Science in Business AI Adoption??

Data science is critical to AI adoption in business because it ensures AI models work with clean, structured, and relevant data. Basically, it enables businesses to:?

• Extract insights from raw data for AI-driven decisions.?

• Train, test, and optimize AI models for accuracy.?

• Automate workflows by integrating AI into business processes.?

• Monitor AI performance to prevent model drift and errors.?

How does the lack of data scientists affect AI adoption??

The lack of data scientists directly hampers AI adoption by creating data quality issues, inefficient model development, and slow decision-making. Without skilled professionals, businesses struggle to prepare, clean, and structure data, leading to inaccurate AI predictions.??

This way, both model training and optimization become challenging, delaying deployment and impacting business outcomes like forecasting, automation, and customer insights. Without sufficient expertise, AI models may also remain underperforming, failing to deliver reliable results that drive business real value.?

Can data engineers replace data scientists??

I’ve noticed that many businesses try to replace data scientists with more data engineers. Although both play crucial roles in AI adoption, it doesn’t work that way. Data engineers manage the data infrastructure, ensuring data is clean, accessible, and ready for use. Data scientists, on the other hand, design and build AI models using advanced math and machine learning to extract insights and make predictions.?

While engineers set the foundation for AI, scientists are essential for creating and refining models that deliver business value. In other words, although data engineers can take on tasks like data preparation, they don't work as replacement for data scientists, as they are better prepared to handle tasks like fine-tuning.?

How are businesses filling this talent gap??

Now that we know data engineers can’t fully replace data scientists, let’s look at how businesses are addressing this talent gap. While there are several approaches, I believe the following are the most practical. I’ll illustrate them with Snowflake’s solutions:?

1. Low-code & No-code platforms?

Low and no-code tools like Snowflake Cortex can automate many AI and ML tasks, allowing business users, analysts, and engineers to work with AI models without deep expertise. They simplify processes like data preparation, model selection, and deployment.?

However, although they can help bridge the gap, they have three main limitations:?

• They can’t handle complex AI model customization or deep statistical analysis.?

• They rely on pre-built models, which may not fit specialized business needs.?

• Data quality, governance, and bias handling still require expert oversight.?

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2. AI training programs?

Another option is to upskill your existing teams with online training in tools like Snowflake’s Snowpark, teaching them to handle AI projects without hiring additional specialists.??

3. Contact external certified professionals?

Another popular option to fill this skill gap is to look for certified professionals to manage these low and no-code platforms as efficiently as possible. By leveraging low and no-code platforms in Snowflake to perform key AI and data science tasks without requiring deep expertise in ML or statistics. ?

Now, in the case of Snowflake candidates, you must look for this certificates:?

• SnowPro Core Certification: Validates foundational knowledge of Snowflake's architecture and features, essential for implementing and managing Snowflake solutions. ?

• SnowPro Advanced Certifications: These role-based certifications assess advanced skills in specific areas like data engineering and data science?

• SnowPro Specialty Certifications: These certifications delve into specific Snowflake features like Snowpark (Snowflake's developer framework), which enabling data engineers and developers to write code in several languages for data processing and analytics.?

That said, identifying the right prospects isn’t always straightforward, and the required certifications depend on your specific project goals. But no worries—we’ve got you covered.?

At Inclusion Cloud we can help you find these professionals to streamline your AI adoption process. Let’s connect and give the first step to build the strong data foundations for your AI systems!?