AI and machine learning have advanced rapidly over the past few years, and many have suggested that 2019 will be the year for businesses that have waited to finally embrace this new technology. Only 15% of enterprises are currently using AI, but 31% are slated to add it to their strategy over the next 12 months, according to a Digital Trends Report by Adobe. There is arguably tremendous value for companies that utilize automation technologies for data and analytics, and the process of making machine learning models more precise for business isn’t just about technology, but also the roles of people in the technology sector. While many point to the rise of artificial intelligence and machine learning as the beginning stages of human obsolescence, they couldn’t be more incorrect in their assumptions. Human beings are, and will remain, the biggest factor in successful analytics.
Those looking to enter the data analytics workforce can breathe a sigh of relief knowing that AI will not be taking their job. It will, however, become a vital skill required of many—if not all in the coming years. According to the Digital Trends Report, the share of jobs requiring AI has increased 450% since 2013. Every industry has seen an exponential increase in data—with a great many organizations quite literally drowning in their proverbial data lakes. Expert projections suggest this issue is only going to grow, with a 4,300% increase in annual data production that will create 35 zettabytes by 2020.
With ever-growing hordes of data, businesses are beginning to recognize the necessity for increased automation strategy across their organization. According to Narrative Science, 61% of companies with an innovation strategy are using AI to identify opportunities in data that they would have otherwise missed, and only 22% companies without this strategy have been able to utilize AI to identify opportunities they would have otherwise missed. This is a huge opportunity for businesses to capitalize on data and button up inefficiencies, yet the majority of employees believe the emergence of AI to be a threat to their livelihood, with two-thirds of Americans expecting that computers or robots will do most of the work performed by humans in 50 years, and a recent study found that these beliefs are so pervasive that they are making employees sick.
The reality is that AI and machine learning amplify human intelligence through technology, and employees can rest easy knowing that according a study released by the World Economic Forum, data-related jobs will be the most in-demand within the next four to five years, along with AI and machine learning specialists. While technologists are still figuring out which business processes should be automated versus what can be automated, striking a proper balance between automation and human intelligence in data analytics is no easy task. A common mistake around automation is failing to recognize the necessity of the human element for the technology. While computers are certainly capable of extraordinary feats, human programming is essentially still the brain behind these outputs. Human intelligence is required to create any kind of automation right now, and the resulting automated processes are therefore not inherently intelligent themselves.
Despite this, there remains continued speculation about the various technological challenges before machines can cognitively match the performance of human counterparts, and whether machines will ultimately replace humans. Matching or exceeding human performance is only part of the conversation. None of these complex tools can exist without at least some initial human involvement, which is why all the discussion of automation and job displacement tends to be exaggerated.
In truth, since our earliest days as a species, we developed and lived alongside technology to create a better life. Why is this stage of development any different?
