What skills do your technologists need to use AI effectively?
To use AI tools effectively, your teams need engineering and programming skills, plus data science, data analysis, tech fluency, and critical thinking skills.
Oct 13, 2023 • 6 Minute Read
Generative AI is expected to become a $1.3 trillion market by 2032. As GenAI continues to grow, organizations need people who know how to use it to gain a competitive edge.
Whether you want to develop AI engineers, upskill existing talent for more senior roles, or ensure everyone in your org understands AI, building adjacent skills for the future will help them use AI tools effectively.
Table of contents
How do supplemental skills help teams use AI tech?
No one works in a silo, and technology works the same way. Take cloud engineers as an example. They need to understand cloud platforms, infrastructure, and architecture. But well-rounded cloud engineers also understand programming languages, security practices, and data management.
Likewise, tech and non-tech employees need adjacent skills to take full advantage of AI tech’s capabilities. When employees understand cloud, security, sales, and other areas of the business, they can identify where AI would bring the most value to the organization. This makes them smarter, more efficient users who understand the business implications of using new technologies.
6 technical skills teams need to use AI technology effectively
The skills and experience someone needs to leverage AI tools successfully often depend on their role and how they plan to interact with AI. For example, an AI engineer who develops AI and ML models needs to understand programming languages, data science and analytics, and how to communicate their insights with other teams.
A marketer, on the other hand, doesn’t need to know how to code in R or build artificial intelligence algorithms. Getting a foundational understanding of AI, ML, and prompt engineering will be more valuable for their role.
Depending on your team’s responsibilities, these six technical skills can help them use AI technology more effectively.
Data science and data analysis
This one may seem obvious, but only 25% of technologists are completely confident in their data skills. And anyone who builds or uses AI technology needs some skill with data analytics, data visualization, and machine learning to better understand the data, identify patterns, share insights, and solve problems with AI.
Check out these Pluralsight courses to build data skills for the future:
Python, R, and other programming languages
Team members building and testing AI algorithms need programming skills. That includes knowledge of programming languages such as Python, R, and Java, as well as APIs like OpenAI and TensorFlow.
Learning Python and R isn’t all that matters, though. Programmers often use structured processes for building, testing, and deploying new applications. These skills are helpful when working with AI to ensure solutions are easy to maintain and meet business needs.
Boost your team’s programming fundamentals:
Mathematics and statistics
As much as it may pain your programmers to hear it, mathematics and statistics skills will help them use AI technology more effectively. After all, math is the underlying principle of AI/ML concepts like natural language processing and deep learning. And evaluating machine learning models for accuracy and precision also requires some basic math skills.
To understand AI on a deeper level, they need to know linear algebra, calculus, probability, and statistics. Get them started with Math For Programmers.
Prompt engineering
While generative AI can augment everything from sales and customer service to coding, it isn’t perfect. GenAI often gives inaccurate responses, hallucinations, and garbled text. Prompt engineering isn’t dead—it enables your teams to craft detailed prompts with a higher chance of returning valuable, relevant, and accurate information.
Develop prompt engineers and boost prompt engineering skills in your team:
Cloud computing for AI
AWS and Anthropic, Microsoft and OpenAI . . . there’s a reason why cloud service providers are partnering with AI vendors: AI and cloud computing are complementary technologies that enhance each other.
AI automates complex cloud processes and makes it easier to manage multicloud environments. Cloud, on the other hand, provides the scalable infrastructure and data storage needed to test and deploy AI models.
Having a firm grasp on cloud skills enables someone to make the most of both technologies, whether they’re a cloud engineer or not. For example, technologists will be better equipped to deploy AI applications as microservices in the cloud and improve scalability and performance.
Give your teams a crash course in cloud:
Security and compliance
Between general data privacy concerns and new attacks targeting AI tools like ChatGPT, everyone needs security skills to mitigate risks when using AI technologies.
But security skills are especially important for teams building and implementing AI models. Security knowledge will enable them to build, test, and deploy AI solutions with security best practices and AI-specific risks in mind from the very beginning. These teams should familiarize themselves with:
AI-specific attacks, like data poisoning and model inversion
AI compliance frameworks
AI security controls, such as data protection, auditing, and secure AI model training
3 soft skills for AI
As important as technical skills are when it comes to using AI, soft skills are just as critical. Teams need these skills for the future to hold productive conversations about AI tech and explain how it drives business value.
Effective communication
AI professionals should know how to explain their AI algorithms and insights to other teams and non-technical stakeholders. Without communication skills, they won’t be able to share the impact of their work and why it matters to the business in a way other teams can understand and appreciate. Help them develop:
Strong verbal and written communication skills
Presentation experience and best practices
Domain knowledge in data science, engineering, sales, and other relevant fields or industries
Build better communication skills:
Critical thinking and problem solving
AI can be inaccurate. It can inherit biases from the data it was trained on. It can also be used to spread misinformation or create deep fakes. With so much information at our fingertips (AI-generated or not), critical thinking skills ensure we interact with and use data responsibly.
Whether teams use AI to generate code or an email, they need to exercise critical thinking skills and question the accuracy and assumptions of the results. Only then will they be able to use AI to solve problems and make ethical decisions.
Check out these tips to improve critical thinking skills in your organization. Give your teams critical thinking practice:
Empathy and emotional intelligence
Artificial emotional intelligence or artificial empathy enables AI to understand human emotions and respond with the appropriate tone and concern. This can enable things like more empathetic customer support chatbots, but it can’t replicate “real” human empathy. For example, it might assume that crying always means someone is sad.
When building AI systems, teams need to exercise real empathy and emotional intelligence to ensure the resulting AI technologies are free from systematic biases, applied to business needs ethically, and respond to customer and user concerns considerately.
Build up your team’s emotional intelligence:
How to help your teams develop AI skills for the future
Whether your teams need technical skills, soft skills, or a combination of both, upskilling helps them develop the skills they need to use AI effectively. This includes just-in-time learning that empowers teams to learn skills on an as-needed basis and formal learning paths that help teams dig deeper into specific areas needed for their roles.
Pluralsight offers a wide range of beginner, intermediate, and advanced courses for technical skills and soft skills. Give your teams the skills they need to leverage AI technologies ethically and effectively no matter their role.
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