Picture three technology executives: one in cybersecurity, one in software as a service, and one in payments. Each is trying to figure out how to deploy artificial intelligence across their organization. The cybersecurity CTO wants to know how quickly the firm can roll out AI across more tasks. The SaaS lead wants to know where AI can sharpen growth and give products an edge. The payments executive wants to know where AI can earn organizational trust, reduce risk and prove its worth.
Previous PYMNTS Intelligence research finds that the tech industry is at the forefront of AI adoption. But “tech” is a broad category, and AI strategies aren’t one-size-fits-all. Adoption reflects an individual sector’s core pressures, from customer protection to revenue expansion to transaction control. Cybersecurity firms use AI most broadly, SaaS firms are most willing to experiment and payments firms are most disciplined about where they invest.
The obstacles aren’t the same, either. Some firms wrestle with change management, others with oversight and still others with security and data handling. Taken together, the findings show that enterprise AI is about fit. Firms that scale the fastest match AI to the parts of the business where it can do the most useful work.
These insights are explored in “New Data Shows How Tech Sectors Are Turning AI Into Strategy,” the latest edition of the Enterprise AI Benchmark Report, a PYMNTS Intelligence exclusive series. This edition draws from a survey of 60 senior technology executives at U.S.-based enterprises with at least $1 billion in annual revenue conducted in April 2026. The data show that different tech sectors are scaling AI in distinct ways that align with core business pressures, not a single, shared playbook.
Cybersecurity Leads in AI Breadth
The cybersecurity sector is adopting AI the most broadly but not necessarily the most deeply.
Cybersecurity shows the broadest AI adoption among the three sub-industries, but its lead doesn’t indicate dominance across every function. The sector reaches high adoption on 23 of 75 tasks grouped under marketing and sales, supply chain management, data and technology, product and customer experience, risk and compliance, corporate and strategy, HR and workforce, and payments and finance.
That compares with 18 tasks for SaaS and payments providers, suggesting a wider footprint across the business rather than concentration in a few areas. The breadth is especially visible in the functions where cybersecurity firms are closest to their core value proposition, including product and customer experience. On that front, they lead at a 60% high adoption rate, while on risk and compliance, they stand at 32%.
The pattern points to a distinct operating logic. Cybersecurity firms aren’t simply adding AI for its own sake. Instead, they’re embedding it where it improves detection, protection and customer interaction. This in turn suggests that trust comes first.
Meanwhile, SaaS leads in AI adoption for growth and revenue functions, with half showing high adoption, as well as in data and technology (35%) and corporate strategy (20%). That points to AI being used less as a shield and more as a springboard for expansion, product development and operating efficiency.
Payments firms, meanwhile, lead only in payments and finance, at 20%, showing a narrower but highly focused approach. Rather than spreading AI widely across the organization, payments firms are concentrating it near the transaction layer, where reliability and control matter most. The result is a smaller footprint than cybersecurity, but one tightly aligned with the business’s core function.
Different Industries, Different AI Bets
SaaS enterprises make the biggest bet on experimental AI.
Each of the three sub-industries is making different bets about what AI should do for their business. SaaS firms show the strongest appetite for experimentation, while payments firms are the most cautious and cost-conscious.
Half of the SaaS executives in the sample say they are likely to fund experimental or innovation-oriented AI projects, compared with just 10% of payments leaders. That gap suggests SaaS firms are more willing to test AI in new workflows, then refine the use case as the value becomes clearer. In software, that kind of experimentation can translate into faster product cycles, better customer experiences and stronger operating leverage.
At the same time, 85% of SaaS firms cite financial return on investment, and another 85% cite strategic or competitive positioning as reasons to fund AI. Those priorities show that these firms are willing to test more, but they still want AI to support growth, differentiation and measurable business value.
Payments firms prioritize proven returns over experimentation.
Payments firms, meanwhile, are focusing on risk and compliance reduction (80%) and margins and profitability (70%). That makes sense in a sector where trust, regulation and operational control are central to the business model. Payments firms are directing AI toward use cases that protect the business, improve economics and strengthen the core payments operation.
In payments, 75% of firms cite financial return on investment, 70% cite risk reduction, and 80% cite productivity or efficiency gains as the main reasons to justify spending on AI. The figures show a desire to use the technology to improve economics, reduce exposure and increase operational performance. These companies want AI to show a direct path to safer, more efficient and more profitable operations before they scale the technology further.
Cybersecurity balances experimentation with risk reduction.
Cybersecurity is somewhere in the middle, emphasizing risk reduction and productivity gains more than experimental bets. This sub-industry’s most common motivation for AI spending is productivity or efficiency gains, with 80% of firms citing it as a driver, followed by risk reduction, at 75%. Those motivations fit a sector whose value proposition depends on resilience, vigilance and speed. Cybersecurity firms appear to view AI less as a way to chase novelty and more as a means to strengthen performance in areas of critical importance to customers.
More than enthusiasm is driving AI funding. Across the three groups, the technology is being filtered through each sector’s unique definition of value, whether that means innovation, protection or disciplined returns.
Different Industries, Different AI Barriers
Each sector faces a different set of organizational and technological barriers to scaling AI.
The obstacles to scaling AI can also vary by industry. Cybersecurity and SaaS both identify change management and user adoption as the top organizational barriers, at 40% each. In effect, the biggest challenge is not the technology itself but the effort required to get internal teams to change routines and trust new workflows. Adoption can stall when organizations struggle to translate technical capability into everyday practice.
Meanwhile, no single organizational barrier exceeds 25%, and five different issues cluster between 15% and 25% at payments firms, suggesting a broader set of friction points rather than a single dominant obstacle. That means the challenge in payments isn’t just related to change management. Instead, it’s a distributed execution problem, where governance, integration, data quality and evidence of return all matter at once. Payments firms aren’t blocked by one big issue so much as slowed by several medium-sized ones.
Similarly, the biggest technological constraints vary by sector. Cybersecurity and payments firms both cite security, privacy and data handling as their top technical limitation, at 40%, while SaaS puts human oversight first, at 30%. That difference tracks with the nature of their work. Firms that handle sensitive data are most concerned with control and protection. SaaS firms, which may deploy AI more broadly across products and internal workflows, are more focused on how to supervise AI, explain its outputs and maintain consistent performance at scale.
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Methodology
This report is based on a survey of 60 verified senior technology executives at U.S.-based enterprises with at least $1 billion in annual revenue conducted in April 2026. All respondents were primary decision-makers or the most knowledgeable people in their organizations regarding AI strategy, adoption and operations.
The survey measured AI adoption, performance, investment, barriers and strategic outlook across eight business functions that use 75 AI-supported tasks. The three sub-industry groups included cybersecurity and data protection, SaaS, platforms and APIs, and digital payments and financial infrastructure, with 20 respondents in each group. The sub-industry comparisons are directional and intended to surface patterns across business models rather than precise market estimates.
