Learn which AI skills for tech teams should be prioritized right away to improve your tech business in the latest article covered by AiTech.
As artificial intelligence becomes more advanced and accessible, it is becoming increasingly important for organizations to integrate the technology into their business strategies and operations. It’s no longer a question of if, but when organizations will build AI capabilities, which largely comes down to their ability to develop AI skills and talent.
While research shows that AI is a top-three area of investment for organizations, it is also among the top 10 most difficult skills to hire for, according to IT decision-makers. Rather than search for AI talent in an ultra competitive hiring pool, there is a bigger opportunity for organizations to look to their existing workforce and focus on upskilling. This approach is not only more efficient, but necessary given the rapid pace of tech innovation. Technical skills already have a short shelf-life of about two-and-a-half years, which is something AI has the potential to accelerate. As AI stands to transform nearly all technology domains and functions, it is more important than ever to ensure your team’s skills are evolving in tandem.
In the age of AI, upskilling is a critical component of business success, though it can be overwhelming if tech leaders aren’t sure where to start or what to focus on. Here is a comprehensive guide to the most important, in-demand AI skills to prioritize on your technology team.
- Data Fluency: Data is the most important building block of AI, and developing data skills and fluency will serve as the foundation for your team’s AI strategy and capabilities. It is important to build both general awareness and knowledge around data – often referred to as data literacy – as well as practical skills that can be applied across a wide range of data topics related to AI. This can include machine learning, predictive analytics, data wrangling, natural language processing, deep learning, large language models, neural networks, generative adversarial networks (GANs), and more. Additionally, foundational skills like mathematics, statistics, and logical thinking will all play key roles in helping your team deploy useful generative AI products, solutions, and services.
- Domain Expertise: While AI innovation may be advancing rapidly, having strong domain expertise is a timeless skill set that should not be overlooked. Knowledge about your industry, business model, and your company’s products, services, and general offerings will be essential for developing and driving an AI strategy forward. Within an IT team, this includes the ability to work iteratively, ideally using an Agile model, to continuously deploy and improve AI capabilities and outputs. Humans will be ‘in the loop’ for the next several years as we move towards a more AI-centric world, and your domain expertise will help identify when something that’s generated is dubious or incorrect.
- Security Awareness: Having a security-first mentality is important for your entire tech team, not just those in cybersecurity roles. The stakes become much higher with AI, as models may overtime learn about your team’s vulnerabilities and determine opportunities for a breach. It is everyone’s responsibility to help maintain the safety and security of your company’s infrastructure and data, which becomes even more important with emerging tools like generative AI in the mix. Security teams will need to have a pulse on the types of information and assets that are shared with generative AI models and what comes back in return, so that they can manage and mitigate risks based on their organization’s security posture.
- Power Skills: Commonly referred to as soft skills, power skills like critical thinking, creativity, and curiosity are paramount to any AI upskilling efforts. The role of humans becomes even more important when working with AI, especially when it comes to mitigating biases and identifying wrong results, which may not seem obvious at first. With AI, it’s often not a question of something being “right” or “wrong.” There are a lot of grey areas, which is where logic, critical thinking, and decision-making come into play. Creativity and curiosity are just as important, especially when developing and determining new ways to use AI to solve problems, increase productivity, and drive real value for the business and your customers. Lastly, having strong leaders who inspire and empower their team members to develop is an important skill set in and of itself.
For any upskilling strategy to work, especially those involving AI, team members need to value and understand the importance of continuous learning. All technologists make an implicit commitment to continue learning and evolving their skill sets given the rapid pace of innovation. In the case of AI, it’s not just about keeping up with the latest technological advancements, but building awareness and knowledge around AI ethics, biases, safety, and more. It is the role of the organization and its leaders to build a culture of learning and to provide employees with the necessary training and guidelines to ensure AI usage is safe, responsible, and productive.
Learning is a catalyst for growth at both the organizational and individual level, especially in today’s fast-paced digital world. Given AI’s potential impact on nearly all knowledge workers, especially those in technology roles, the need for proactive skills and talent development has never been more imperative. In the age of AI, organizations without a plan for upskilling risk falling behind or potentially misusing the technology, whereas those that prioritize learning and skills development will have a serious advantage.
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