How the Implementation of AI Talent feeds Organisations Skill Gap

There is an international shortage of artificial intelligence talent and labour markets across the world cannot keep up with the demand for developers. There is also a shortage of mathematicians and scientists who can develop new and innovative artificial intelligence (AI) technology. A study by Microsoft and IDC reveals that the shortage of workers with artificial intelligence skills has prevented companies that want to embrace AI from being capable of doing so. Enterprises must discover creative ways to supplement the talent they require to initiate AI projects across industries until highly skilled AI developers enter the workforce. Those projects could involve voice, image, or pattern recognition allowing autonomous movement or simulating realistic conversations. Such innovations can strengthen a new generation of healthcare tools and smart home devices. Companies across all industries have been struggling to secure top AI talent from a pool that is not expanding fast enough. Even during the economic disruption and layoffs due to the COVID-19 pandemic, the demand for AI talent has been robust. Leaders are looking to minimise costs through automation and efficiency. In such a scenario, companies need not solely be looking for fresh graduates. Instead, they need to actively start investing in training current employees and recruiting people with AI-adjacent skills. To get deep insights into it, let’s look at how companies can implement AI talent to feed the skill gap effectively:  

Build a team of In-house Expertise

Companies in niche areas and smaller organisations should not be too stubborn about recruiting preferences. Part of this requires not looking for people who already have AI experience. Instead, hire employees who show the hard and soft skills which could eventually make them a valuable asset of an artificial intelligence team. They add flexibility and enthusiasm for learning, complex problem-solving skills, data visualisation and analytics, understanding in mathematics, and security.  

Invest in Learning

Company leadership often talk about a nice game for continuous growth and learning, but they don’t tangibly invest time and resources for people to do so. To save time and invest in resources effectively, companies need to focus on robust skills development in the field of AI. However, this might not provide the expected results. Companies can start small, but the leadership team requires creating the time and resources people need to commit to learning artificial intelligence skills and technologies. Ad-hoc opportunities don’t likely deliver results a company wants. Besides, someone who is worried about if the manager is looking over the individual’s shoulder and wondering the reason why the person is not doing a real job cannot concentrate on meaningful learning. Although skill development reaches its potential when the teachings are grounded in real projects, businesses often are not equipped to train their employees in emerging technologies like AI. Managers should encourage employees to opt for related courses from Coursera, Udacity, Datacamp, etc. They also should support these endeavours by offering employees time to learn the skills they need.  

Structure Team According to the Problem

The composition of an AI team depends on the problem being solved, the team’s approach, and the level of incorporation required with the development team to support production. However, there are a few things to keep in mind. First, hiring solo AI talent is not advisable. Second, AI professionals rely on partnership for ideas and can feel isolated if they are the only member of a larger team. Third, companies may begin with a small team to validate data and the team’s ideas, regardless of the domain. Finally, companies need to ensure that they have a robust AI strategy before expanding the team.