There are numerous reports on the AI talent shortage. Some estimate that there are perhaps 300,000 AI experts worldwide but millions of open positions. Another report says the number of jobs requiring AI has increased by 450 % since 2013.
Whatever the real numbers are, companies investing in AI are feeling the competition when trying to secure talent.
Most business leaders see AI as fundamental to the future; 72% called it a “business advantage” in an Adobe study. Adobe also found that 47% of digitally mature organisations, or those that have advanced digital practices, say they have a defined AI strategy.
A significant reason why businesses want more AI in the workplace is financial gain. Forrester reports that by 2020, insights-driven businesses will steal $1.2 trillion per annum from their less-informed peers. AI provides a competitive, financially-lucrative advantage to companies that turn their attention to it.
Many businesses also note Google’s new “AI-first” approach. Shifting from dedicating itself to mobile-first, Google is now looking at AI as the innovative solution to its suite of products.
Whatever the motivation, whether it be financial or competitiveness, global businesses are trying to arm AI teams, and hitting a block due to the skills gap.
What many don’t realise, however, is that building the perfect AI team isn’t just about data scientists. Here’s why you need to think outside the box when assembling your AI team.
It would be great to find that data science unicorn – but trying to find one data scientist that can do everything is a highly unlikely scenario. Not to mention that that candidate would be incredibly expensive.
Companies that can find an “AI architect” really hit the jackpot, but that position is incredibly difficult to fill. Instead, many companies are combining the skills of data engineers, data scientists, and software engineers to create a robust AI team.
Businesses need a data engineer first to organise the data. Then a general data scientist who can test and adapt algorithms. And lastly, a software engineer who can implement the applications. Depending on the company’s needs, there may be more than one of each of these positions.
It’s interesting to note that a few years back, organisations building AI teams first looked to PhDs to lead the charge. Candidates skilled with a PhD in data science are clearly still in demand, but a shift has taken place. CIOs now want candidates who convert learning algorithms to business value.
Given this, more CIOs are assembling blended teams that can tackle collecting the right data, building the right models, and using these to make the right business decisions.
A successful AI team does not only include data scientists and software engineers, though those positions certainly lead the team. More AI teams are adding project managers, marketers, and UX/UI staff to round out the team. The idea here is that while data scientists and engineers have the technical skill sets, the other positions fully understand the product.
Marketers, for example, understand consumer demand. They have extensive knowledge of buyer personas, the market landscape, and competitor’s value. This intelligence can help to shape the AI product or service, ensuring it appeals to the market.
Businesses using AI for personalisation should have a marketer working with the AI team. The consumer demand for personalisation in numerous aspects is growing, and marketers have a strong understanding of this want. AI teams that work in silos, without the input from other positions, may find themselves missing the mark.
Although data positions still lead AI teams, companies are beginning to see the value of pairing the team with other departments. To fill these positions, businesses are working with universities and bootcamps to recruit top talent. Organisations are also working to upskill current employees to fill vacancies on AI teams.
Globally, companies are pushing more resources into AI. Forrester notes that investment in AI will increase more than 300% over the next year. The insights and data-driven decision-making capabilities AI provides are incredibly valuable.
Given that, competition for AI talent will continue, though how AI teams are formed will likely take a blended approach.