Consumers' economic perspectives play a role in adoption of GenAI tools 451 Research

As generative AI sweeps across industries and the consumer landscape, much attention is being paid to the level of adoption of these new tools and how they are being used by consumers and businesses. Yet, that perspective only captures part of the picture regarding use of GenAI. Another important perspective, especially on the consumer side, is the question of economics: How do consumers' economic standing and perspectives on the economy impact their attitudes toward GenAI technology?

The population-representative sample in S&P Global Market Intelligence 451 Research's Macroeconomic Outlook, Consumer Spending, Impact of Inflation 2024 survey was asked a series of questions to gauge their adoption and use of new GenAI tools. This report examines those results through the lens of macroeconomic conditions such as income, spending outlook and economic sentiment to understand to what extent, if any, these factors affect the broader integration of GenAI tools into consumer households.

Throughout history, technology adoption has been heavily influenced by wealth. Societies with a relative wealth of resources have progressed leaps and bounds ahead of those without, and this remains the case even in today's deeply interconnected world. GenAI represents the most recent example of this scenario playing out on an individual level. A type of tool that could be accessible to all is instead the subject of yet another socioeconomic divide, with a 22-percentage-point gap between higher-income households (more than $100,000 per year) and lower-income ones (less than $50,000 per year) when it comes to GenAI use. We are not surprised by this result. However, we theorize that the inequality prevalent in GenAI adoption, for which the cost of entry is low (or essentially free in deployments such as Google search), can be reduced through support of stronger tech literacy. There is an opportunity to educate less-informed consumers in the name of providing more equal access to such an innovative tool.

Summary of findings

Large segments of consumers are steering clear of AI tools. Overall, 38% said they have used GenAI tools. Among respondents who have not yet used these tools, 21% said they plan to within the next 12 months, while 42% said they have no plans to use GenAI tools at all. In this latter group, the top reason for avoiding GenAI tools is straightforward: They do not see enough reasons to use them (24%). Additional obstacles include data privacy/security concerns (18%) and trust issues (18%). Furthermore, when asked what would encourage them to use GenAI tools, almost half (48%) of this group said nothing would make them feel comfortable using AI tools. These are clear indicators that developers of AI-enabled tools still have a lot of work to do to win over large numbers of consumers who remain skeptical of the new technology.

Income gaps play a significant role in GenAI adoption. Household finances play a large role across all types of consumer technology adoption, so it is no surprise that this would also be the case with GenAI tools. What is surprising is the sizable gap between higher-income (more than $100,000 per year) and lower-income (less than $50,000 per year) households. The current survey shows a 22-percentage-point difference in GenAI usage between the two cohorts (52% of higher-income versus 30% of lower-income respondents). Even more striking is that 49% of lower-income respondents said they have no plans to use GenAI tools, compared with only 27% of higher-income respondents.

Even among those who use GenAI tools, higher-income respondents reported having greater exposure through work and school than lower-income respondents. Our surveys continue to show a tech opportunity gap between higher-income and lower-income households. The big concern is that the potential speed at which GenAI could transform multiple aspects of daily life, both in and out of work, may not just reinforce the existing gap between these cohorts, but could dramatically increase it.

Tech literacy is both a problem and a potential solution for GenAI adoption. Respondents who did not use GenAI tools were asked what concerns they have about the future of such tools, and eight of 11 response options show varying degrees of difference between higher-income and lower-income respondents. Options include scenarios such as AI misuse for the purposes of fraud, data privacy concerns and decreases in interpersonal contact. Three of the four largest gaps leaned toward higher-income respondents: disinformation/misinformation (59% versus 43%), ethical concerns (51% versus 36%) and risks to data privacy (60% versus 48%). The gaps that leaned toward lower-income respondents are those having no concerns at all (17% versus 5%).

In a follow-up question, these same respondents were asked to identify the biggest barriers to using GenAI tools: Lower-income respondents cited not enough knowledge (19% versus 14%) and "don't know" (14% versus 7%) at higher rates than those with higher incomes. Lastly, lower-income households are less able to identify outcomes that would encourage them to try GenAI tools, again citing "don't know" at a higher rate than higher-income respondents (22% versus 14%). Taken together, these results suggest a gap in familiarity with the different issues surrounding GenAI. However, the ability to recognize these disparities can underpin a road map to increasing consumer awareness.

Awareness of advertising and sponsored content varies. Embedded ads and sponsored content have been mainstays of websites and social media platforms for a long time. Naturally, they would also be used to monetize GenAI tools. Respondents were asked how often they think the results generated by GenAI tools are influenced by or contain advertising: More than half (52%) of higher-income respondents believe query results are influenced by ads always or most of the time, compared with 35% of those with lower incomes. On the other hand, lower-income respondents say they "don't know" at a higher rate than higher-income respondents (25% versus 14%).

Furthermore, when asked if they worry about advertising or sponsored content affecting the quality of AI-generated results, higher-income respondents (56%) were more likely to say they worry a great deal/quite a bit compared with lower-income respondents (44%). Again, lower-income respondents (22%) were more likely to say they "don't know" compared to higher-income ones (14%). Despite their differences, the sentiments expressed by higher- and lower-income respondents highlight additional opportunities for GenAI platforms to focus on educating consumers about how the results from these tools are generated and the influences that may affect the results.

Income is not the only socioeconomic divide. In addition to the quantifiable impact of income and financial resources on tech adoption, there is also the qualitative aspect of economic sentiment. Broadly speaking, consumers who believe the economy will improve over the next 90 days (57%) are twice as likely to say they have used GenAI tools compared with those who believe the economy will worsen (28%). This dichotomy extends to spending plans, where consumers who plan to spend more over the next 90 days are also almost twice as likely to have used GenAI tools as those who plan to spend less.

The same can be said of purchasing power, where consumers who believe they are getting more value for their money than expected have used GenAI tools twice as often as those who believe they are getting less for their dollar. The main takeaway here is that consumers who are more financially stable and economically optimistic are less worried about the future and have greater bandwidth to engage with emerging technologies.

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This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.
451 Research is a technology research group within S&P Global Market Intelligence. For more about the group, please refer to the 451 Research overview and contact page.