The AI Skills Gap: One Challenge, Three Realities

According to research from Reuters, it was estimated that by 2024, AI spending would grow to over USD 550 billion, and there would be an expected AI talent gap of 50%. The rapid advancement of AI and adoption of generative AI are creating a sizable shift in the types of roles and skills that companies are hiring for. In our recent conversation with Lydia Logan, Vice President of Education and Workforce Development at IBM, on the Tech in EdTech podcast, she touched on the fact that the impact of AI on skills is not the same for everyone.

One thing is clear: even the most advanced AI today cannot operate without humans. So it is important, now more than ever, to close the AI skills gap for companies to thrive collectively, and that begins with understanding the skill requirements at every level.

Understanding the AI Skills Gap

AI technology is advancing faster than traditional training methods, which leaves companies without enough employees who are proficient in areas like machine learning, prompt engineering, and data analysis. AI adoption also remains uneven across companies, with entry‑level, mid‑level, and senior leaders facing their own challenges. There is a need for tailored learning strategies to be implemented across levels because it is important for employees at every level to have a basic understanding of AI concepts.

Entry‑Level: Starting in the Middle

Lydia Logan detailed that entry‑level jobs are almost like starting in the middle, because everything is up‑leveling. Three years ago, an entry‑level job would have focused on mastering internal tools and workflows. Today, most entry‑level employees know everything they need to by the time they are in college. What they need to learn is how to use it to become more efficient, productive, and do it faster, so they can spend more time upskilling.

When they can effectively use generative AI as a building agent or to create efficiencies, it leaves them with precious time to think creatively, collaborate with teammates, and strategize. They need to be able to critically evaluate AI outputs for relevance and tone and use their judgment when it comes to deciding whether a task requires AI intervention.

Mid‑Level: Orchestrating Human and AI Workflows

At the mid‑level, team efficiency is the highest priority. As a mid‑level manager, are you able to deploy generative AI technologies to drive higher performance, manage different projects, and distribute work effectively to get the most out of your team? The challenge is not learning how to use AI but how to manage it. Instead of creating data silos and duplication, work needs to be mapped out, and tasks need to be segregated into automation and human capabilities. At this level, the most valuable skill is workflow design, wherein managers need to distribute cognitive load between humans and machines.

Leadership: Embedding Ethics, Learning, and Culture

When IBM’s Institute for Business Value interviewed CEOs, they found that, over the next three years, their workforce needs to be upskilled due to AI’s impact on jobs. There is a culture change that needs to take place, which can be achieved when you consider large‑scale upskilling initiatives. It could mean driving employees to undertake projects that are research‑oriented or to create drafts and outlines, but the main purpose is to augment their current roles and not replace them.

However, it is important to note that these projects are not one‑time investments but a consistent process. Understanding and optimizing AI should be introduced as a policy and not a perk. Ethics plays a significant role in determining the responsible use of AI. Leaders should ask questions like “What data are we using?”, “Who ensures accountability for its outputs?”, and “How was this model trained?”. The role of a leader is to guide organizations through continuous transformations responsibly.

The Tiered Approach

The reason this works is that the AI skills gap is not a single issue. Treating it as one is as good as wasting resources and providing generic training. A tiered approach enables organizations to target needs and gain progress.

Augmenting Human Capability

AI is here to elevate, not replace, the human workforce. When used ethically and thoughtfully:

• Entry‑level employees can use AI to gain more time for creative endeavors.
• Mid‑level managers can use it to strengthen collaboration.
• Leaders can use it to drive ethical, inclusive growth.

As Lydia stated on the podcast, “Lifelong learning is one of our core corporate values. It’s now and always. So, get started now. Think about the ways that you can apply new learning, and particularly focus on the application of generative AI in everyday use. And think about the teams where it can be most effective because people will always ask, what’s in it for me? Not just I’m doing this because I’ve been told to do it, but how will this make my work better, easier, more valuable, more impactful?”