Adding artificial intelligence (AI) features to business functions yields many benefits. These include automating routine tasks and using predictive analytics and upskilling professionals to improve employee retention. To gain these advantages will require a substantial increase in the number of skilled AI professionals. The demand for trained AI professionals exceeds the available supply by a factor of ten; the number of trained AI professionals worldwide is estimated at 300,000. Similarly, 93% of US and UK organizations consider AI a business priority, but 51% acknowledge that they don’t have the in-house talent needed to develop AI applications.

A major hurdle is the need for skilled AI trainers. There are not enough trained AI specialists to teach the many AI disciplines: machine learning (ML), deep learning, computer vision and natural language processing. As the private sector poaches AI specialists from academic institutions, universities are unable to produce the same number of graduates with AI skills. Thus, poaching leads to both present and future shortages of AI talent.

Here are actionable measures that enterprises can take to assess and build in-house skilled AI talent.

Start With a Needs Assessment

AI skills are diverse. AI is not defined by one set of skills, nor by a single role in the organization. In fact, AI has become an umbrella term to define all of today’s technology. Some skills, such as programming languages, can be developed across the organization. Other skills are more complex and are found among higher-skilled professionals in data engineering, neural network architecture and other areas. Conducting a needs assessment can help map the locations of required skills.

Enterprises must understand their AI needs and identify which business areas require AI skills. For example, a needs analysis can clarify whether there is a need for data scientists in marketing to achieve campaign effectiveness, or if engineers are needed to build automation tools for customer interactions.

Different businesses need different AI skill sets, based on the industry in which they operate. In manufacturing, ML engineers can improve quality control by detecting faulty products; in health care, they might be involved in the drug discovery process. To optimize training efforts and build need based programs, corporations must first identify and understand the skills gaps within their employee base.

After the Assessment: Building an Impactful Training Program

A needs assessment provides companies a map to how they can best execute training programs. Enterprises can work with third-party instructors, or they can hire trainers to build a tailored curriculum. A comprehensive AI curriculum might include math and computer science foundational concepts, as well as coding and analytics. Next could come modules in ML and deep learning. Learners with a foundational understanding of these subjects can grasp concepts more easily.

When designing training programs, enterprises must prioritize skill progression. Initially, learning elements for all employees can be structured into three high-level categories: foundational AI literacy, analytical competency and critical AI knowledge.

  • Foundational literacy provides employees with the conceptual understanding of data by developing the ability to interpret and work with AI-driven tools. Instructors may enable employees to assess AI opportunities for strategic ways to attain business goals. This basic knowledge can be taught to both technical and non-technical employees.
  • Analytical competency should encourage learners to consider AI to identify growth Technical employees should be trained to develop pre-built AI models to accelerate solution building. A number of these analytic skills may be applicable across technical and non-technical teams.
  • Critical AI knowledge should include courses that help build AI solutions and manage the AI production process. This learning stage should specifically cater to technical stakeholders such as data scientists.

Upskilling Powered by AI

By 2025, the World Economic Forum predicts that AI will produce 97 million new jobs. Thus, it is critical that organizations invest in employee reskilling and upskilling initiatives.

Employees run the risk of stagnation if they don’t learn new skills. Lifelong learning must be the mantra of today’s professionals. 59% of L&D experts worldwide proclaim that their top priorities are upskilling and reskilling. Upskilling accelerates growth and assures higher employee retention rates while improving job satisfaction.

With AI everywhere, it’s worth thinking about how to leverage the technology to address the internal talent crisis. Increased digitization and remote work have incentivized employees to seek different skills to advance their careers. Companies can leverage AI to offer personalized learning to their employees. AI-powered upskilling platforms can identify required competencies and offer employees personalized plans to close specific skill gaps. These platforms enable enterprises to provide tailored programs that tap into employees’ interests and business needs. They also build a culture that encourages lifelong learning for everyone from interns to C-suite executives.

The Bottom Line

The success of training programs is contingent on the results. Continuous assessment measuring the effectiveness of the training program helps to gauge employees’ skill growth. This can be done when employees apply their learning in different AI projects.

Regardless of the training pathway enterprises use, they must follow a long-term roadmap on nurturing and managing AI talent. Relevance, scalability and engagement are the key values that must be prioritized in any talent-building efforts. These increased investments in empowering professionals to understand and interact with AI have pay-offs in the long run. The future of AI is the future of work.