Man vs. machine: How humans tackled AI…and lost
2018 was most definitely the year in which AI and Machine Learning (ML) took the lion’s share of technology media headlines. Like social media, virtual reality, drones, and cloud services before them, the impending doom of AI robots taking our jobs drew conference audiences, filled social feeds and consumed traditional media channels.
Neil Rankin of Predictive Insights explains, “Identifying regular trends is difficult for humans, particularly if there are lots of overlapping patterns. What this means is that people see patterns where there are none and fail to see those that are there. This means that, for things like staffing and stocking, there are unnecessary inefficiencies that can be solved through data. In addition, some employees are often asked to do things they are not very good at, like analytics, when they were hired to manage a team and interacting with customers. It’s surprising how many resources, energy and time can be misdirected to meet an objective.”
“Identifying regular trends is difficult for humans, particularly if there are lots of overlapping patterns. What this means is that people see patterns where there are none and fail to see those that are there. This means that, for things like staffing and stocking, there are unnecessary inefficiencies that can be solved through data.”
Neil Rankin, predictive insights ceo
| To prove this, Predictive Insights, a subsidiary of the Alphawave Group, decided to pitch (hu)man versus machine for one of their clients, Hungry Lion, a quick-service restaurant chain with almost 200 branches in a number of African countries. Using a demand forecasting tool from Predictive Insights’ suite of machine learning and econometric models, they used the client’s store-level sales data, as well as external data pertinent to their industry – such as weather, local economic conditions or days of the week – to more accurately predict sales per day at an individual store level. This then compared the model’s accuracy to the predictions made by experienced branch managers with surprising results. |
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