Barclays has published a short list of U.S. software companies it views as most strategically positioned to benefit from heightened demand for AI infrastructure and cloud-based capabilities. The investment bank focused on large-cap software names and horizontal SaaS providers that display pronounced growth momentum tied to AI capacity, partnerships and product re-architecture.
Oracle
Barclays places Oracle at the top of its list on the strength of a dramatic expansion in the company’s revenue backlog. According to Barclays, Oracle’s backlog climbed from $98 billion in fiscal 2024 to more than $550 billion in fiscal 2026. The bank attributes this jump primarily to large-scale AI infrastructure contracts.
Barclays expects the enlarged backlog to translate into an accelerating revenue trajectory for Oracle. The bank projects revenue growth in constant currency of roughly 9% in fiscal 2025 - the year that ends in May - rising to in excess of 30% by fiscal 2027. Barclays highlights that this acceleration is anticipated to be driven in large part by more than 100% revenue growth in Oracle Cloud Infrastructure, reflecting the ramp-up of AI capacity that was previously contracted.
Market attention to Oracle’s AI infrastructure positioning has already prompted price target increases from at least two firms, Wedbush and Oppenheimer. In a separate board development noted by Barclays, Oracle added Dr. Tomislav Mihaljevic, the CEO and President of Cleveland Clinic, to its board of directors.
DigitalOcean
Coming in second on Barclays’ list is DigitalOcean, which the bank highlights for its role in supplying infrastructure - including a full stack of services - to small and medium-sized businesses and AI-focused startups. Barclays emphasizes DigitalOcean’s differentiation through a user-friendly, cost-efficient product approach aimed at smaller customers, supported by a software layer and add-on services.
The bank notes that DigitalOcean has been rapidly expanding capacity over recent quarters, with demand outpacing supply. Barclays cites the company’s first-quarter 2026 financial results, where revenue of $258 million and earnings per share of $0.44 surpassed analyst expectations. Following those results, UBS raised its price target for the company, pointing to DigitalOcean’s capacity outlook.
Salesforce
Barclays ranks Salesforce third, reflecting an optimistic view that organic revenue growth could accelerate in the second half of fiscal 2027. Factors underpinning that outlook include easier comparisons as the company moves beyond COVID-era baselines, stronger sales capacity and an increasing revenue contribution from Agentforce.
Barclays also points to an expanded partnership between Salesforce and Google Cloud intended to enable AI agents to execute workflows across both platforms. Additionally, Salesforce has revised its revenue reporting structure for fiscal 2027 to align with changes in its product architecture - a reporting change highlighted by Barclays in its assessment.
Snowflake
Snowflake is Barclays’ fourth-ranked pick, with the bank viewing the company as well-positioned over the long term from an AI standpoint. Barclays underscores Snowflake’s data platform as a way to centralize data that resides across disparate enterprise systems, producing a connected data view that is conducive to running AI workloads - particularly analytics-oriented workloads. The bank notes that data silos present one of the major obstacles to cross-enterprise AI deployments, and Snowflake’s platform addresses this challenge.
Barclays also observed recent analyst activity around Snowflake, including price target reductions from UBS and Evercore ISI, while Piper Sandler maintained an Overweight rating. Analysts at Evercore ISI pointed out that in fiscal 2026 Snowflake’s data engineering and AI-related offerings made up a growing share of total revenue.
Overall, Barclays’ selections emphasize companies tied to cloud capacity expansion, AI infrastructure contracts and platform capabilities that facilitate cross-enterprise AI use cases. The bank’s list reflects a focus on balance-sheet-supported capacity builds, contract backlogs and evolving partnerships and reporting structures that could influence revenue mix as AI workloads scale.