In a presentation at AI & Big Data Expo Global, Jason Smith, Chief Digital Officer of Publicis Groupe, shared insights into the role of AI in reshaping decision-making processes within the realm of advertising and marketing.
The focal point of Smith’s presentation was a strategic experiment conducted by his team to explore the potential of AI in reducing noise and bias inherent in decision-making. Smith began by addressing the common perception of decision-making and the often-overlooked influence of human biases and external factors.
“Let’s recognise that we’re not the best at making decisions, that there are some issues when we make decisions—just as there are some issues when AI makes some decisions,” said Smith.
Smith advocates for combining the strengths of both human and AI decision-makers.
The strategic experiment involved a comprehensive analysis of the human decision-making process, where the team pitted AI against a human team in running a Facebook travel campaign. Smith delved into the intricacies of the human brain’s dual components—the amygdala for intuitive thinking and the prefrontal cortex for reasoning.
Notably, Smith drew attention to the concept of “noise,” a term he differentiated from bias, describing it as the variance in decision-making that introduces inconsistencies. He supported this with examples from various professions, such as judges delivering differing sentences based on external factors.
The challenges within the marketing and advertising space were highlighted, particularly the difficulty of managing a vast number of variables—illustrated by a campaign with a staggering 83 million variations.
“There’s no way that a human can realistically go through 83 million [ad variation] combinations,” said Smith. “AI is better at picking out important signals in large data sets.”
Initially, the results of the strategic experiment showed humans outperforming the AI-optimised campaign, However, the AI campaign quickly pulled away:
While acknowledging AI’s flaws — including bias — Smith advocated for a collaborative approach, envisioning a balance between human intuition and AI assistance. He highlighted the importance of recognising human limitations and leveraging AI to reduce decision-making flaws.
The presentation concluded with key takeaways, encouraging the recognition of human decision-making limitations, leveraging AI to reduce flaws, and finding the right balance between human input and AI assistance.
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