A lack of skilled labour is proving a deterrent to corporate AI adoption
That AI and Machine Learning algorithms can churn out decisions from increasingly large datasets is not the headline anymore; instead, the fact that executives and IT leaders today need to carry out more copious checks for fairness, unbiasedness and accuracy than ever is what the ticker must show. These plethora of checks require greater training, higher investments and a much larger skilled pool of data scientists, developers and engineers than ever before, especially given the fact that almost 60% of these organisations are nascent AI users – having used AI technologies for under three years.
A recent survey from business technology firm ZDNet confirmed that AI and ML initiatives are now front and centre for about 44% of the sampled organisations, with another 22% having some project or the other in the development stage. ZDNet reports: Swami Sivasubramanian, VP of machine learning at Amazon Web Services, calls this the “golden age” of AI and machine learning. That’s because this technology “is becoming a core part of businesses around the world.”
An interesting feature noted by the survey was the fact that most IT teams are now taking the front-and-centre role in AI implementation themselves, instead of using external consultants (about 28%, currently), as has been norm in the past. Close to two-thirds of ZDNet’s survey respondents, or 63%, reported that their AI systems are built and maintained by in-house IT staff, while almost half of the organisations surveyed said that they use AI-related services through Software-as-a-Service (SaaS) providers, with another 30% using AI through Platform-as-a-Service (PaaS) providers. About 42% of individual department heads play a role in this process, while one-third of the organisations have dedicated corporate AI teams.
Yet, previously persistent problems remain: only about 14% respondents agreed that their IT workforces use AI technologies as a part of their regular duties. A major reason for this is the lack of adequate volumes of skilled AI labour – an increasing need for which is becoming more visible day-by-day.
Consider this excerpt from Tripti Sethi, senior director at management consulting firm Avanade: “Implementing an AI solution is not easy, and there are many examples of where AI has gone wrong in production. The companies we have seen benefit from AI, most understand that AI is not a plug-and-play tool, but rather a capability that needs to be fostered and matured. These companies are asking ‘what business value can I drive with data?’ rather than ‘what can my data do?'”
Almost 70% of AI firms need data engineers, ASAP
In fact, that availability of skilled labour is a concern should not be shrouded one bit; it is one of the top three issues that enterprises are facing in building and maintaining AI-powered systems. ZDNet reports from their survey: “close to two-thirds of surveyed enterprises, 62%, indicated that they couldn’t find talent on par with the skills requirements needed in efforts to move to AI. More than half, 54%, say that it’s been difficult to deploy AI within their existing organizational cultures, and 46% point to difficulties in finding funding for the programs they want to implement.”
Image:Industry-wise AI skill demand; Courtesy:Survey results from ZDNet
Of the apparent skill-demands of the obviously under-staffed AI industry, data engineering was found to be the most important – almost 70% of firms need data engineers, ASAP.
If there was ever a time to capitalise and build skills to enter the AI industry, it is now. Strike while the iron is hot – or in this case – while the iron requires skilled labour.