News and Insights
AI, Machine Learning, and Automation: Societal Impacts of Advanced Technologies
August 18, 2021
The COVID-19 pandemic shined a spotlight on technology and changing work practices across the world. In addition to the obvious remote-working enabling technologies, notably the themes of artificial intelligence (AI), machine learning, and automation are becoming ever more prominent. However, the application of these technologies can be controversial, and brands need to tread carefully by responding to concerns and emphasizing the ways these advanced tools can help humanity.
To this end, FINN Partners’ Global Intelligence team synthesized primary and secondary data sources to uncover the issues that people are focused on, and the strategies that brands can use to address public concerns.
Big Brother Isn’t Necessarily a Job Killer
A common theme with AI, machine learning, and automation is “Big Brother is watching”, with citizens concerned about decreased privacy as usage of these technologies increases. They do not trust AI-based tools (even those with good intentions) due to uncertainty over how their data will be stored and used. Indeed, analysis from the social listening platform Infegy shows social media users associate AI, machine learning, and automation with negative terms like “problem,” “issue,” “threat,” and “bias.”
An overview of negative sentiment related to AI, machine learning, and automation since January 2020. Source: Infegy
Another frequent argument is that these technologies will “take over the world” or eliminate human jobs. Usage of AI, machine learning and automation is indeed accelerating. At some companies, low-paid workers train AI systems that may eventually replace them and, not surprisingly, those employees feel exploited. The pandemic fast-tracked this transformation, with rising automation among tollbooth operators and assembly line workers. Data even shows that fields requiring a personal touch, like telecom, travel, and financial services, are feeling the pinch.
Source: Statista Digital Market Outlook
However, the news in other industries is more positive. For example, according to a Department of Labor study, demand for truck drivers will remain strong in the face of automation because self-driving vehicles are still years away from becoming a reality.
As a result, brands must use messaging that calms fears and emphasizes that not every task can or should be automated. They also need to focus on how investors and consumers will benefit from these technologies.
Money Talks, Regulation Doesn’t
For AI, machine learning and automation assets, it sometimes seems there is nowhere to go but up. According to Gartner, despite the global impact of COVID-19, 47% of AI investments remained unchanged since the start of the pandemic and 30% of organizations planned to increase such investments.
Funding rounds similarly abound in the AI space, perhaps driven by a high comfort level with the technology. But these pitches are a far cry from Shark Tank “in or out” boardroom drama. In fact, by 2025, AI and data analytics will inform more than 75% of venture capital and early-stage investor executive reviews, which means a company may never make it to the stage of human evaluation at all.
Of course, with great power comes great responsibility, but the problem is, no one can seem to figure out the best way to regulate the field. Many want to, since the market for these technologies will be worth $100 billion by 2023 and offer considerable profits.
But increased oversight isn’t conducive to making money, so some tech companies addressed the issue by monitoring AI use in-house – to mixed results. Another option is state or federal regulation of AI. When the United Kingdom tried that approach, however, critics called it sloppy and vague, with dangerous loopholes.
Smart oversight makes sense for brands, however, and thankfully many CEOs and business leaders are all for it. Even before the pandemic, over 70% in the US endorsed globally uniform AI regulation.
Source: EY/Statista
Regulating AI, machine learning, and automation isn’t just a good business decision, it also leads to positive audience engagement while benefiting brand health. Managing these technologies can be a difficult and contentious process, but brands that get it right will be more likely to prosper.
Methodology
FINN Partners’ Global Intelligence team performed an in-depth analysis of AI, machine learning, and automation topics, collecting and synthesizing data from secondary sources, audience intelligence research, and social listening exercises. Tools used included TalkWalker, Infegy, Global Web Index, Google Trends, and Statista.