9 Must-Have AI Skills to Upskill Your Workforce in 2023

elev8 - 6 min read

Ai skills in the digital world

Artificial Intelligence (AI) is quickly reshaping the business landscape, with 37% of companies using it today. From the algorithms that define our media diets to small firms using AI to manage logistics and inventory, these tools are becoming essential for dynamic businesses to thrive.

The flip side is that companies that fail to develop the skills necessary to use Artificial Intelligence and computer systems are at grave risk of being outcompeted. However, hiring a dedicated AI professional is often relatively inexpensive, particularly for smaller firms. That’s why investing in upskilling your existing workforce with specialist AI skills and machine learning knowledge, as suggested by Gartner, is one of the most cost-effective ways to harness the power of AI and stay contemporary with the latest trends in computer science. By developing your skills in this field, you will be well-positioned for a successful career in AI jobs, capable of understanding and carrying out complex tasks.

But you need to know which skills to invest in to do that. Here, we’ll break down the critical technical and power skills you need to utilize AI and how you can invest in those skills.  

What are the most important AI skills that professionals should have? 

The most important AI skills professionals should have include machine learning, programming, data analysis, natural language processing, and problem-solving. These skills are crucial for effectively understanding and implementing AI technologies in various industries and roles. 

What Are the Most In-Demand AI Skills? 

The core AI skills you’ll need to develop are technical hard skills. Below, you’ll find the essential skills your team will need.

The right programming languages 

Start with the building blocks. Whether you want to develop a custom AI solution from scratch or buy something off-the-shelf and customize it, you need a workforce ready to work in the correct programming language. The most widely used programming language in AI is Python. Fortunately, it’s a relatively simple and powerful language. Combine the ease of learning with the immense pre-made libraries for coding AI tools, and it’s clear why it’s so popular. 

Another powerful and popular language used in AI is Java. Depending on what you want to do with AI, Java might be another critical skill to develop. For example, Java is used extensively in search algorithms, machine learning, and neural networks.  

If, on the other hand, you need something more like numerical or statistical analysis, the R programming language might be useful. Lastly, you can’t ignore the versatility of C++ for programming AI and integrating it into operating systems. These programming languages are realistic skills you can upskill your existing workforce for.  

The right frameworks and libraries  

We mentioned libraries above, but what are they, and why are they so important? Frameworks and libraries, such as NumPy, TensorFlow, and PyTorch, enable coders to create AI tools faster and more efficiently using existing code sets. It’s like trying to build a table out of pre-cut wood, screws, etc., instead of making each one yourself from scratch. An intricate knowledge of libraries and frameworks relevant to AI, such as Apache Spark and Scala, including domain knowledge in the form of industry-specific insights and a naturally curious mindset, is one of the essential tools for AI professionals. Understanding these platforms, such as PyTorch, will allow you to write code faster and more accurately for AI applications. 

TensorFlow is the most popular combination framework and library, an open-source platform for creating machine learning algorithms. Other popular Python libraries are SciPy and NumPy. Knowing how to get the best out of these tools is essential for efficiently using AI.  

Neural Networks  

Beyond developing a firm grasp of the necessary programming languages, frameworks, and libraries, it’s helpful to build experience in specific types of AI. Neural networks are a popular tool mimicking how the human brain learns to make complex decisions. 

Deep learning  

This is when you combine many layers of neural networks to get better results. The flip side is that this means deep-learning algorithms, such as complex algorithms, take longer to train. In general, deep-learning algorithms focus on prediction, such as image classification, object detection, face recognition, machine translation, dialogue generation, etc. Suppose you only have a set of inputs with no outputs and want to identify different input patterns and cluster them according to similarities. In that case, you’ll want to use unsupervised learning algorithms. For example, they are often used to identify images and analyze large data sets. Machine learning algorithms, including regression and Natural Language Processing (NLP), are a must-know for anyone working in AI. Coding proficiency for AI/ML algorithms (Supervised, Unsupervised, Semi-supervised, and reinforcement learning) is also essential to stay updated in the field. 

High-level math  

Developing and honing AI algorithms in the AI industry requires quite a bit of high-level math proficiency. Beyond the programming languages and understanding of algorithm types we outlined above, you’ll want to ensure your AI team has excellent linear algebra, statistics, probability skills, JavaScript, and expertise in the AI field. These skills are critical for both creating algorithms and understanding their results. 

Big data  

This is one of the most popular and exciting AI applications today. Big data, which involves using algorithms to extract enormous amounts of data and extract useful information, is a complex skill set. Utilizing big data involves not just understanding the algorithms to analyze it but also how to clean and prepare the data before analysis. Data scientists play a crucial role in this process as they collect, research, and interpret substantial amounts of data using machine learning and predictive analytics to gain insights beyond statistical analysis. They should have expertise using Big Data platforms and tools like Hadoop, Pig, Hive, Spark, and MapReduce. This is critical because improperly labeled or organized data will never give you meaningful results, no matter how good the algorithms you use to analyze it are.

Machine learning engineers must possess strong software skills, be able to apply predictive models, and utilize natural language processing while working with massive data sets. Data scientists must also have strong analytical skills to evaluate complex data and identify current business and market trends to improve the organization’s profitability and efficiency. You could use an object storage system such as AWS S3 or a data warehouse such as AWS Redshift. The most desired technical skills in data science include in-depth knowledge of SAS and R, Python coding, the Hadoop platform, experience working on cloud tools like Amazon’s S3, and understanding unstructured data. Julia is popular for machine learning because it’s highly performant and has many machine learning packages. It’s also used for data visualization and data analytics. 

The most in-demand AI power skills  

While hard skills obviously get most of the attention when it comes to AI, power skills are just as important. Without them, your teams can easily struggle to understand results and solve problems as they inevitably arise. But which power skills should you focus on?

Creative problem-solving  

This skill ties into every other skill on this list. Ultimately, choosing the right approach to a problem, finding the right resources in a library, and troubleshooting issues along the way all rely on excellent problem-solving skills. 

Decision making  

There are many ways to approach using AI. Being able to weigh the costs and benefits of any approach is essential. Choosing the wrong type of algorithm at the start can lead to plenty of headaches, so you want to ensure your team makes the right decisions.


Often, there’s a real gap between AI’s technical and business sides. Teams developing AI solutions must be able to explain the implications of decisions in the field of AI, including healthcare. For example, using an AI black box can have important legal ramifications. Your team needs to be able to communicate the tradeoffs involved in whatever approaches they want to take so that management and technical teams can be on the same page. 

Communication skills are essential to generating the buy-in necessary to reap the benefits of AI and machine-driven decision-making.

  • Big data analysis  
  •  Fraud prevention  
  •  AI-based assistants  
  • Suggestion algorithms for media or e-commerce  
  •  Analyzing and sorting media content  

In practice, these applications translate into scanning hundreds of resumes intelligently to streamline hiring, predicting when to refill essential inventory, managing supply chains, or extracting insights from sales data. These are all areas where AI is already changing how business is done.  

Upskill your existing workforce to use AI solutions 

Whether you need AI and data science skills to hone further and customize an off-the-shelf solution or to manage work with a development partner, upskilling your existing workforce is a flexible and cost-effective way to build AI skills. Gallup found that the cost of a new hire typically ranges from 150-200% of their annual salary. Hiring is just expensive. In today’s rapidly evolving technological landscape, computer vision is one of the essential AI skills to upskill your workforce in 2023. By equipping your team with computer vision capabilities, you can enhance their ability to identify and analyze objects within images or videos, enabling them to contribute more effectively to AI-driven projects.

By comparison, upskilling is far more affordable in addition to side benefits like improving retention by demonstrating that you’re willing to invest in your existing teams.  

Digital skilling starts with the right partners  

You can talk about the importance of digital skilling all day, but doing it requires the right partner. Elev8 is that partner. 

Born out of a recognition that digital skilling is critical for the future success of its sister companies, Elev8 works with respected organizations like Microsoft and Amazon, as well as governmental partners in Qatar, Costa Rica, and Nigeria, to bring effective, pragmatic, and scalable digital skilling to teams around the world. Elev8 also provides access to cohort experience, Microsoft events, Azure-based cloud computing resources, data training, and technical guidance from Microsoft and GitHub experts.

Offering flexible and cost-effective ways to build key capabilities within organizations, we can help you build the AI capabilities you need to thrive in today’s competitive landscape. 

Explore our enterprise and government pages and future-proof your workforce with essential AI skills.