American Companies Tiptoe Into AI While Payments Providers Seize the Opportunity

Recent research from Goldman Sachs reports that artificial intelligence (AI) adoption among U.S. businesses remains limited. In the fourth quarter of this year, 6.1% of American companies indicated the use of AI in producing their products or services, compared to 5.9% in the previous quarter. 

This gradual increase suggests that while some firms continue to experiment with AI tools, a broad, industry-wide surge in adoption has not taken place. Many businesses appear to be advancing at a measured pace, with no clear evidence that these technologies have reached a tipping point in standard operations.

Differing Levels of Engagement Across Industries

Reports indicate that the finance and insurance sectors have maintained relatively higher AI implementation rates, compared to other areas that have shown flat or even declining usage. In finance and insurance, the integration of AI may involve applications that support compliance procedures, transaction monitoring, and anomaly detection. 

Within this environment, firms handle significant volumes of data and maintain robust risk assessment practices. These conditions may align more closely with the capabilities of current AI tools, which can process large data sets and detect irregular patterns in real time.

Conversely, data from Goldman Sachs points to reduced AI usage in segments such as information, manufacturing, and education. The specific reasons for these declines are not detailed in the reported findings. 

Factors may include the complexity of integrating new technologies into existing infrastructure, shifting budget priorities, or uncertainties related to regulatory frameworks. Additional data over multiple quarters could provide further insight into why some industries slow their AI adoption while others maintain or increase it.

Observed Productivity Indicators

Studies referenced by Goldman Sachs and related academic work have noted productivity improvements in certain cases where AI tools are deployed. Some research has recorded potential increases of 23% to 30%. Although these figures offer reference points, it remains unclear how widely such benefits apply across different operational contexts. Firms in the payments sector, for example, may consider whether these reported productivity gains could translate into more efficient settlement processes, more accurate transaction handling, or smoother internal workflow management if AI tools are integrated into their systems.

CFO Sentiment and Return on Investment Data

Findings from PYMNTS indicate that CFOs remain cautious when evaluating the impact of AI investments. Only 13% of surveyed CFOs described their returns on AI as “very positive,” down from 27% in earlier assessments. This shift suggests a reserved stance among financial leaders. In the payments field, where capital allocation often depends on demonstrable outcomes, such data may influence decisions regarding pilot programs or further expansion of AI-driven tools.

This restrained outlook on ROI may lead businesses to closely monitor ongoing performance metrics before committing to wide-scale implementation. The relationship between AI integration and measurable results in the payments landscape—such as improved authorization rates, reduced incidence of fraudulent transactions, or more reliable customer verification—may become clearer as these technologies mature and as more firms report detailed performance data.

Cybersecurity Considerations in Payments

Cybersecurity remains a priority for companies that process sensitive financial data. Some businesses exploring AI adoption review whether these technologies can assist in detecting threats, enhancing authentication procedures, and identifying unusual transaction patterns. In the payments sector, such capabilities could be relevant for mitigating risks associated with unauthorized access, data breaches, or fraudulent activity. 

The extent to which AI-based security measures gain traction may depend on clear evidence that these tools can operate within regulatory boundaries while maintaining or improving compliance standards.

Increased AI Usage Among Smaller Enterprises

Recent observations also highlight an uptick in AI adoption among small and medium-sized businesses (SMBs). While their overall usage rates remain low compared to larger enterprises, SMBs have reportedly doubled their AI implementation over certain periods. 

In the payments segment, this may be reflected in modest automation initiatives—such as employing AI-driven analytics to review transaction histories or using machine learning tools for basic invoice management tasks. As more accessible and cost-effective AI solutions emerge, SMBs could continue to incorporate these technologies to support incremental operational improvements.

Ongoing Assessment and Additional Data Needs

The current data portrays a U.S. market in which AI adoption remains cautious and uneven. Differences between sectors like finance and insurance, which appear more receptive, and others that have pulled back, highlight a complex landscape. The payments industry, positioned at the intersection of technology, financial regulation, and customer trust, may serve as an informative case as firms evaluate whether AI solutions align with their strategic objectives.

Further research and longitudinal studies may shed light on how AI affects key performance indicators in payments, from authorization speeds and fraud detection accuracy to compliance management and customer satisfaction. The accumulation of data over time may help stakeholders determine whether initial productivity claims hold true on a broader scale and whether existing caution among financial decision-makers evolves as technologies become more standardized and predictable.

For now, the information available suggests that while certain areas of the U.S. business environment continue to test and implement AI capabilities, many remain in a deliberate, watchful mode. Whether the coming months and years bring a more pronounced shift will likely depend on the demonstration of clear, sustained benefits, especially in sectors—such as payments—that demand reliability, security, and quantifiable improvements.

TL;DR American businesses remain slow to adopt AI, hovering at just over 6%, yet the finance and insurance sectors show it can be done profitably. Payment providers stand to gain from AI-driven efficiencies—faster approvals, fraud prevention, and streamlined operations—but must move carefully amid ROI doubts and cybersecurity concerns. SMBs are doubling down on automation, and those who embrace AI-driven tools can optimize costs, improve customer experiences, and stay ahead of the curve.