Firms Need to Address AI’s Impact on Culture
Artificial intelligence and machine learning have moved beyond being science fiction tropes today and firms that do not investigate and eventually adopt them quickly will find themselves at a severe disadvantage.
The technology enables processing of things that were impossible to process before, noted Martin Migoya, co-founder and CEO of IT and software development firm Globant, during a recent Inside the ICE House podcast.
“Suddenly you need to rethink all of the processes inside your corporation to understand how you can apply AI and machine learning instead of doing it the traditional way,” he said.
Migoya recommended that firms do not fall into the trap of treating AI and machine-learning implementation merely as a task to be handed off to the organization’s IT department but as a cultural issue that affects the entire business.
For Globant, Migoya had every employee go through a training course on the basics of artificial intelligence.
“I’m not pretending that everyone is going to become an expert, but that people understand what is going on and which things artificial intelligence and machine learning can deliver,” he said. “That is part of the cultural transformation that needs to happen inside an organization because then people will start to think how they can use a piece of machine learning to solve problems they face every day and make this a better company.”
As part of the effort, Globant also wrote an AI manifesto that states what the company will and will not do with AI.
“We want machines to cooperate with humans,” said Migoya. “We want machines augmenting humans. We want machines for all of the tasks that people cannot process, but we want humans doing human work.”
The best way for companies to approach cultural and technological transformations is to start with simple projects that solve simple problems.
“Don’t think of it as a project where you need to spend millions and millions on it,” he explained. “That will come in time if the early efforts are successful.”
Developing a data strategy also will help firms clear the data quality hurdle all AI and machine learning projects face, according to Migoya.
“A lot of firms have nothing but noise pretending to be data,” he added.
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