Discover targeted strategies that help bridge the AI skills gap and equip your workforce with the necessary skills to thrive in an AI-everywhere world.
As different industries continue to explore artificial intelligence (AI) to transform their business operations, one of the challenges they run into is the AI skills gap.
Technology is evolving quickly, and so are the ways in which businesses interact with AI, leading to a significant gap between available and skilled AI professionals and the industry’s current and future needs. By understanding the current AI skills gap and the effects it’s having across industries, your business will be better positioned to mitigate that gap using the strategies outlined below.
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.
That’s 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 Deloitte identified that 31% of organizations listed a lack of skills around AI as a top-three concern, and another survey from Pluralsight found that only 12% of IT professionals have significant experience working with AI. Even worse, 90% of executives don’t have a clear sense of what their team can and can’t do with it.
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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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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.
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.
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.
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Through a combination of approaches, it is possible to mitigate these challenges at your organization.
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.
The good news, according a Hyland-commissioned Walker Sands study, is that:
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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.
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— Tiago Cardoso, Principal Product Manager, Hyland
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.
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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.
AI fluency is becoming a mission-critical need at the enterprise level. Increasingly powerful AI tools are changing what’s possible in terms of efficiency, and novel applications of AI are propelling businesses forward in new and unexpected ways. To achieve meaningful gains from AI, organizations must meet the AI skills gap challenge head on.
The need for investment in education and training is urgent, and so is the need for collaboration — across industry, academia and government. By investing now to create a future-ready workforce, businesses today can position themselves to capitalize on future opportunities.
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