Bank of America maintains a bullish outlook on ServiceNow, citing its deep workflow and infrastructure data as a potential differentiator for AI-driven enterprise solutions, even as rising costs and margin pressures remain in focus
Bank of America is doubling down on its positive view of ServiceNow, reaffirming a Buy rating and a $130 price target after the software company posted stronger-than-expected contract growth for the second quarter. The target implies a roughly 36% upside from the $95.46 share price cited in the bank's July 23 report. ServiceNow's subscription revenue climbed 24.5% to $3.88 billion, while its current remaining performance obligations-contracted revenue expected within the next year-rose 21% to $13.2 billion, both outpacing Wall Street forecasts.
While much of the market's attention has focused on headline growth, Bank of America's latest analysis highlights a less obvious factor: ServiceNow's AI agents may have a unique edge because they can tap into the company's extensive workflow history and infrastructure records already stored on its platform. This built-in context could allow ServiceNow's AI to handle complex enterprise problems with fewer integration hurdles than third-party AI tools, which often require additional data extraction and mapping across multiple systems.
Enterprise Context as a Differentiator
Basic service-desk automation-like password resets or ticket classification-can be handled by most AI tools. But when it comes to more complicated incidents, such as application outages or cross-departmental issues, ServiceNow's platform can provide AI agents with immediate access to workflow histories, system relationships, permissions, and approval rules. The company's Configuration Management Database (CMDB) and Context Engine aggregate information about applications, servers, dependencies, user roles, and company policies, enabling AI agents to assess the broader impact of any action before proceeding.
For organizations using third-party AI, replicating this level of context typically means pulling records from disparate systems, reconciling data relationships, and then reintegrating the AI's output back into operational workflows. ServiceNow's integrated approach could reduce deployment costs and complexity, while allowing its AI agents to automate a larger share of the process. As companies move beyond routine support requests to tackle poorly documented incidents and multi-departmental work, the ability to interpret company-specific information becomes increasingly valuable.
This strategic positioning is reminiscent of how other tech giants are leveraging proprietary data to strengthen their AI offerings. For example, recent analysis of Intel's earnings has also highlighted the importance of unique data assets in driving competitive advantage in the AI space.
Commercial Momentum and Margin Pressures
ServiceNow's second-quarter results offer early evidence that its AI strategy is gaining traction. The company reported that annual contract value for its AI products surpassed $1 billion, and the number of customers running ServiceNow's agentic AI in production increased ninefold over the past nine months. ServiceNow is also seeing higher pricing when customers upgrade to packages that include its latest AI features, with established Pro Plus packages commanding price increases above 30%, and new AI-native packages delivering 20% to 30% higher prices.
Security remains a key area of growth. In the second quarter, ServiceNow's security and risk products were included in 16 of its 20 largest deals, and the company completed 24 security contracts worth more than $1 million each. Recent acquisitions, such as Armis and Veza, have expanded ServiceNow's ability to integrate device, vulnerability, and identity data into automated workflows, further strengthening its enterprise appeal.
Despite these gains, the cost of running advanced AI workloads is starting to weigh on profitability. ServiceNow's gross margin for the quarter fell to 77.9%, down from 81% a year earlier and below Bank of America's estimate. The bank trimmed its fiscal 2026 gross margin forecast by 45 basis points to 78.9%, citing higher expenses tied to AI adoption, increased use of hyperscale cloud infrastructure, and integration of recent acquisitions. Adjusted operating margin, however, reached 29.5%, beating Wall Street expectations thanks to strong subscription revenue and disciplined spending elsewhere.
Outlook and Risks
Looking ahead, Bank of America's bullish thesis for ServiceNow hinges on three unresolved questions: whether its AI agents can consistently deliver reliable results on complex cases, whether customers will expand their usage, and whether the company can maintain pricing power to offset the rising costs of AI operations. ServiceNow's management is holding to its full-year operating margin forecast of 31.5%, but acknowledges that AI usage and acquisition integration will continue to pressure expenses in the near term.
It's also worth noting that some of ServiceNow's second-quarter subscription revenue outperformance was driven by strong demand from U.S. federal customers, which shifted some on-premise revenue recognition forward from the third quarter. As a result, the company's third-quarter subscription revenue forecast of $3.975 billion to $3.980 billion is slightly below analyst expectations, suggesting that not all of the recent growth can be attributed to AI demand alone.
For investors, the key question is whether ServiceNow's embedded workflow and infrastructure data will translate into a sustainable competitive advantage as enterprise AI adoption accelerates. While early commercial results are promising, the company must continue to demonstrate that its AI agents can resolve complex cases efficiently enough to justify higher prices without eroding margins.
According to ServiceNow's Q2 2026 earnings release, the company's AI products reached over $1 billion in annual contract value, and the number of customers using agentic AI in production grew ninefold in nine months. Security deals worth more than $1 million totaled 24 for the quarter, highlighting the growing demand for integrated AI-driven solutions in enterprise environments.
ServiceNow's approach to AI highlights the importance of context and data integration in enterprise automation. Unlike standalone AI tools, which often require extensive data mapping and system integration, ServiceNow's platform-centric model allows AI agents to access workflow histories, system relationships, and approval structures natively. This can reduce deployment friction and enable more sophisticated automation, but it also means that the company's competitive edge depends on its ability to maintain data quality, manage costs, and deliver consistent results as customer needs evolve. As the enterprise AI market matures, the balance between innovation, operational efficiency, and profitability will remain a central challenge for ServiceNow and its peers.
