Artificial Intelligence (AI) is quickly reshaping the business landscape, with 37% of companies using it today. From the algorithms that define our media diets all the way 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 are at grave risk of being outcompeted. But the cost of hiring a dedicated AI professional is often prohibitively expensive, particularly for smaller firms. That’s why investing in upskilling your existing workforce is one of the most cost-effective ways to harness the power of AI.
But to do that you need to know which skills to invest in. Here, we’ll break down the critical technical and power skills you need to utilize AI and how you can invest in those skills.
The most in-demand AI skills
The core AI skills you’ll need to develop are technical hard skills. Below, you’ll find the key skills your team will need.
1. 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 right programming language.
Perhaps 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 both programming AI and for integrating it into operating systems.
These programming languages are realistic skills you can upskill your existing workforce for.
2. The right frameworks and libraries
We mentioned libraries above, but just what are they, and why are they so important? Frameworks and libraries enable coders to create AI tools far faster and more efficiently by making use of existing sets of code. It’s like trying to build a table out of pre-cut wood, screws, etc. instead of having to make each one yourself from scratch.
The most popular combination framework and library out there is TensorFlow, 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 efficient use of AI.
3. 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, essentially mimicking the way the human brain learns in order to make complex decisions.
4. 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 take longer to train. In general, deep-learning algorithms focus on prediction. For example, they are often used to do things like identify images and analyze large data sets.
5. High-level math
Developing and honing AI algorithms is something that requires quite a bit of high-level math skills. In particular, beyond the programming languages and understanding of algorithm types we outlined above, you’ll want to ensure your AI team has excellent linear algebra and statistics skills. These are critical for both creating algorithms and understanding their results.
6. Big data
This is one of the most popular and exciting AI applications today. Big data involves using algorithms to take enormous amounts of data and extract useful information out of them. This is a complex skillset. Utilizing big data involves not just understanding the algorithms to analyze it but how to clean and prepare the data before analysis. This is critical because improperly labeled or organized data is never going to give you meaningful results no matter how good the algorithms you’re using to analyze it are.
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, you can easily find your teams struggling to understand results and solve problems as they inevitably arise. But which power skills should you focus on?
1. 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.
2. Decision making
As should be clear by now, there are many, 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 down the line, so you want to ensure your team makes the right decisions from the start.
Often, there’s a real gap between the technical and the business sides of AI. It’s critical that teams developing AI solutions be able to explain the implications of decisions. 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 management and technical teams can be on the same page. Artificial intelligence applications in the workplace While the potential applications of AI are nearly endless, there are a few key applications where it gets the most use today:
- 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 things like intelligently scanning hundreds of resumes to streamline hiring, predicting when you need to refill key inventory, managing supply chains, or simply 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 further hone and customize an off-the-shelf solution or to simply 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% their annual salary. Hiring is just expensive.
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 actually doing it requires the right partner. Elev8 is that partner.
Born out of a recognition that digital skilling is absolutely 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.
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.