AI skills gap: Strategies for a future-ready workforce

Understanding the AI skills gap

The AI skills gap is the difference in the number of AI-skilled workers needed and the number that currently exists. There simply aren’t enough AI-proficient professionals to fill the need.

The AI skills gap is a growing concern for numerous industries that increasingly need to adopt AI into their processes. This gap is particularly pronounced in specialized areas such as data analytics, machine learning (ML), natural language processing (NLP) and in some cases, even to create their own AI tools.

A recent survey from Forrester found that 73% of its respondents named “skills” as an area that needed improvement to best leverage AI for content intelligence. Even with cutting-edge technology and robust datasets, success hinges on human capability. An organization can leverage the same AI tools as a competitor and achieve vastly different outcomes due to varying skill levels and understanding. Empowering employees with strong AI skills and knowledge is essential for effectively and safely using AI in content intelligence.

Additionally, your employees are eager to upskill. According to research commissioned by Hyland and conducted by Walker Sands, 98% of respondents want gen AI training, with AI ethics, compliance and security topping the list of focus areas.

Current impact on industries 

This skills gap is already having an impact on businesses in many industries. In insurance, AI can scan thousands of claims in seconds, identify patterns that suggest possible fraud and send those to human agents for detailed evaluation. For this to be possible, an individual insurance business must leverage AI technology with those capabilities and have the personnel with the required expertise to manage the systems.

As for software development, generative AI has made waves in how well certain models can understand and write code. But this code isn’t plug-and-play most of the time, and many enterprises rightly avoid feeding open models (such as ChatGPT) proprietary code. Businesses are working to create their own large language model (LLM) AI tools for code development and analysis, but creating these tools requires skills that many do not have.

No matter which tools a solution development firm or department uses, the employees using the tech must understand how to use it effectively, while understanding its limitations and ethical considerations. All of this leads to a point of differentiation as some businesses are reaping AI benefits, while others struggle to adapt and adopt.

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Challenges in bridging the AI skills gap 

High demand for skilled workers 

In many industries, the mentality is “adopt AI or get left behind.” Missing the AI train could be an existential threat, so it’s no wonder that everyone wants to hire workers with these skills. Demand is outstripping supply with only 1 in 10 global workers having the in-demand AI skills organizations are looking for, according to new research by Salesforce.

As AI continues to gain prominence, the demand for proficient AI professionals will only continue to increase. With competition for talent becoming fiercer, the importance of offering attractive compensation and benefits to secure talent is now a necessity.

Insufficient training programs

Organizations may recognize the need to upskill their employees to be AI proficient, but who is going to provide the training if the organization doesn’t yet have an AI ready structure or AI training program in place?

Lack of proper training makes it hard for businesses to develop the talent necessary for their AI goals and lack of internal talent makes it hard for businesses to excel using AI. External training programs exist, but many of these do not prepare workers for specific challenges and technologies they encounter in AI roles. 

Budget constraints 

Investing in AI training and development programs can be costly, and high-level decisions are often out of the hands of those who need these programs. As a result, organizations, especially the small- to medium-sized ones, are unable to pursue the needed training. 

Strategies for mitigation 

Through a combination of approaches, it is possible to mitigate these challenges at your organization.

Upskilling existing workforce

Although we have mentioned there are challenges in doing this effectively, there is little question that upskilling the existing workforce is one key to solving the AI skills gap. Up to 93% of U.S. and U.K. organizations consider AI to be a business priority and are either using it or planning to do so in the near term.

It can take time to build a robust AI upskilling program, but organizations can start immediately with smaller steps, such as workshops, online courses and certification programs. With a portion of the workforce likely concerned that AI will render their jobs obsolete, offering this kind of program can be uplifting and improve employee morale, as well as their skills.

> Learn more | AI in the workplace: Navigating a new frontier

Enhancing education and collaboration 

There is a need for institutions to update curricula to include AI-related subjects, ensuring students are equipped with the technical skills and understanding of AI’s power. Collaboration between educational institutions and industry leaders can provide students with the hands-on experience needed to ensure the next generation is ready to enter the AI-enabled workforce.

Through workshops and seminars, institutions can promote a culture of continuous learning to ensure students are engaged and updated on AI advancements. This type of interdisciplinary collaboration fosters broader AI adoption and sparks innovation across different fields.

> Learn more | The role of AI in transforming higher education

Leveraging technology 

Emerging technologies — including AI itself — can play a powerful role in training and skills development, as well as in simplifying the complexity of building AI tools.

AI-powered learning platforms personalize educational experiences by assessing individual learning styles and recommending tailored content. Additionally, automated training tools deliver customized modules, track progress and provide real-time feedback, making skill acquisition more efficient and effective.

> Learn more | AI-powered content and process solutions

Upskilling with user-friendly tools 

Much of the conversation around enterprise AI is focused on highly configurable advanced solutions and those developed in-house, but that’s only one aspect of the AI transformation that’s underway. Businesses can also leverage a host of user-friendly tools that rely on AI but don’t require significant technical knowledge from the user.

For example, drag-and-drop no code AI builders and visual programming interfaces enable the creation of fully functional tools, apps and services with relatively minimal technical skill. These tools, along with other applications, now integrate generative AI capabilities and provide an easier on-ramp to AI upskilling that is valuable to employees of varying skill levels.