The 37% Threshold: Automation Reaches the Hourly Workforce
Artificial intelligence is no longer confined to corporate offices and software engineering teams. According to recent data, 37% of hourly workers in the United States report that new automation or AI has been introduced at their workplace within the last 12 months. The finding, highlighted by Ingo Payments on LinkedIn and detailed in a PYMNTS.com report, marks a significant milestone: AI has crossed from knowledge worker experimentation into the daily reality of frontline and hourly employees.
The 37% figure is not a projection. It is a measurement of what has already happened. And it carries particular weight for the payments and fintech sectors, where hourly workers represent a large share of the customer base for earned wage access, payroll processing, and gig economy platforms. When a third of this population encounters AI on the job, the financial products and services built around their income patterns must reckon with how those patterns are shifting.
Retail, Healthcare, and Manufacturing Lead Frontline AI Exposure
The penetration of AI into hourly work is not evenly distributed. Gallup data reveals that among industries with higher concentrations of frontline employees, 38% of workers in manufacturing reported using AI at work, followed by 37% in healthcare and 33% in retail. These three sectors employ millions of hourly workers in the United States alone, and their adoption rates suggest that automation is being embedded into scheduling systems, inventory management, patient intake processes, and customer service workflows.
In manufacturing, AI-driven quality control and predictive maintenance systems are becoming standard on factory floors. In healthcare, algorithmic tools are assisting with triage and administrative tasks that previously consumed hours of nursing and technician time. In retail, self-checkout optimization, demand forecasting, and automated restocking systems are reshaping how hourly associates spend their shifts. The common thread is that these tools arrive before the workforce is prepared to interact with them.
The Training Deficit Behind the Adoption Curve
The Ingo Payments findings carry a blunt subtitle: automation affects one in three hourly workers without training. This is the critical detail beneath the headline number. Workers are not being upskilled in parallel with the technology being deployed around them. They are encountering AI as a fact of their workplace, not as a skill they have been taught to leverage.
The PYMNTS report reinforces this concern, noting that a growing share of hourly workers are encountering artificial intelligence on the job before they feel financially prepared for it. Financial preparedness, in this context, means having both the income stability and the digital literacy to navigate a workplace increasingly mediated by algorithms. When workers lack training, they also lack bargaining power. They cannot negotiate around a technology they do not understand, and they cannot position themselves for the higher-value roles that AI adoption sometimes creates.
For fintech companies serving this demographic, the training gap represents a risk. Products like earned wage access and digital payroll accounts depend on predictable income streams and stable employment. If AI-driven scheduling or automation reduces hours, eliminates positions, or shifts workers into lower-paid roles, the credit and payment profiles of these consumers change. The data suggests that at least some of that disruption is already underway.
Knowledge Workers’ 75% Adoption Rate Widens the Divide
The hourly worker data becomes starker when compared against knowledge worker adoption. Microsoft’s Work Trend Index reports that 75% of global knowledge workers are now using generative AI, with use nearly doubling in six months. Knowledge workers are not only adopting AI faster, they are often bringing their own AI tools to work, circumventing institutional gaps in technology strategy.
This creates a two-speed economy. Knowledge workers use AI to accelerate their output, enhance their market value, and reduce the burden of high-volume tasks. Hourly workers experience AI as something deployed upon them, often without consultation or preparation. The gap between these two populations is not just about access to technology. It is about agency, training, and the financial resilience to adapt when job requirements change.
Microsoft’s data also reveals that while business leaders view AI as a strategic imperative, many organizations lack a coherent plan for implementation. This absence of planning falls hardest on hourly workers, who are least likely to have independent resources to fill the gap.
Union Warnings and the 90% Exposure Forecast
The anxiety among hourly workers is not speculative. In Luxembourg, unions have warned that up to 90% of jobs could be affected by AI, as government, employers, and unions convened to discuss risks. While the 90% figure represents an upper-bound estimate rather than a precise forecast, it signals the scale of concern among organized labor. Similar conversations are intensifying in the United States, where retail, logistics, and healthcare unions have begun negotiating AI deployment clauses into collective bargaining agreements.
The World Economic Forum’s report on jobs in the new economy acknowledges that displacement and job churn have risen, but frames AI as an opportunity rather than a threat for governments, businesses, and workers who adapt. The challenge is that adaptation requires investment, and the 37% of hourly workers currently seeing AI on the job are not the population most likely to receive it.
How Payments Fintech Must Respond to the Automation Shift
For the payments industry, the 37% figure is a leading indicator. Hourly workers are the primary users of check cashing services, prepaid cards, earned wage access platforms, and gig economy payment systems. When their work patterns change because of AI, their financial behavior changes too. Shifts in scheduling algorithms can make income more volatile. Automation in retail and logistics can reduce overtime opportunities. New roles created by AI may pay differently, or pay on different timelines.
Fintech companies that serve this segment need to build products that account for income volatility driven by automation, not just by seasonal demand or gig economy dynamics. Earned wage access providers, for example, may need to recalibrate their risk models if AI-driven scheduling makes hourly income less predictable. Payroll processors may see changes in the frequency and consistency of deposits. Digital banking platforms serving hourly workers should consider how financial literacy tools can address the specific challenges of navigating an AI-transformed workplace.
The data from Ingo Payments, Gallup, and Microsoft converges on a single conclusion: AI has arrived for hourly workers, but the infrastructure to support them through the transition has not. The companies that recognize this gap first, whether in fintech, workforce management, or financial services, will find a population of 37% and growing that needs new tools, new training, and new financial products designed for the reality of automated work.
