3 critical jobs driving success with AI projects
The impact artificial intelligence is having on the workplace is not what you might have expected. Once feared to be a monster that would devour jobs and leave millions without meaningful work, AI is on the brain for forward-thinking business leaders, who are warming to the idea that AI might just be a net job-creator.
As the full picture of AI’s potential comes into focus, those in the know are realizing that it’s people working behind the scenes that will make transformation possible.
That includes industry analysts, many of whom have become more bullish on AI’s prospects. A recent Gartner report suggests that AI will generate 2.3 million jobs by 2020—exceeding the 1.8 million that it will replace. By 2025, the AI-related job creation is forecasted to reach two million net-new jobs.
As more companies adopt AI to improve products, service and profitability, here are a few jobs that are helping propel the industry ahead:
Natural language processing (NLP) is an important requirement for many AI systems. When an application or system that uses NLP misinterprets a message, AI trainers can track and make adjustments, teaching the system how to think like a human. Human feedback is organized into a massive collection of training datasets to improve the performance of applications and systems to prevent the same mistake from happening again.
Machine Learning Engineer
The demand for machine learning (ML) skills has already surpassed the supply, according to job-search engine Indeed. ML engineers are at the cross-section of data science and computer science. Helping build programming languages, neural network types and deep learning libraries, these workers help to infuse machine learning into various AI products and solutions.
AI Interaction Designer
An interaction designer’s role is much like that of a user experience (UX) designer, but it’s specifically for AI applications. AI interaction designers help humanize the autonomous personal assistants who support enterprises in customer service. One of the core responsibilities for the job is to analyze and adjust human and AI exchanges to make the technology more conversational and natural.
Much of the work behind AI comes down to data, which doesn’t always fit nicely into a single consistent job title. Nonetheless, skilled data workers are counted on to collect, clean and organize data for AI systems to operate accurately and optimally.
For example, video annotators identify items in a video to create a collection of structured data as part of a dataset that will teach a machine how to recognize these items. The process is time-consuming: it can take hundreds of hours to label images from a single hour of video. The labeling, also called annotation, transforms unstructured data into structured data that a computer-vision algorithm can consume.
As the evolution of AI continues to impact a variety of industries, one of the biggest priorities for companies will to be to evaluate their workforce strategy in alignment with these burgeoning data needs. The traditional in-house model says hire, train and manage internal teams on site. This remains a good option for high visibility and involvement with staff, but there are other options for organizations to tackle those tedious data tasks and get solutions to market fast.
While hiring new talent to perform data work has its advantages, it also has some drawbacks, and that includes onboarding new talent and keeping them engaged enough to feel like they’re on a strong growth path, which is not always easy, and many AI companies lack the resources to make it happen.
For some organizations, that’s where managed workforce solutions come into play, serving as the missing link between organizations that need to scale quickly and the managed, skilled data workers who can provide support from all over the world. They’re a great resource for startup companies and other disruptive organizations that need to work with speed and agility. While managed workforce teams handle the routine and repetitive data processes, internal workers can focus on other important areas that are equally essential to the success of the business.
As AI technology continues to evolve, the market likely will see more job roles and responsibilities that align with AI. In addition to impacting the job landscape, this shift will have growing influence over organizational workforce strategies.
While it’s tough to imagine the jobs AI will create, it will be helpful to continue looking beyond traditional job titles and ideas about the workplace. Organizations with an eye to the future – both for their businesses and their customers – will be well-positioned for the next chapter of AI’s advancement.