In Forrester’s Marketing Survey, 2026, B2B marketing decision-makers frequently selected data privacy and security concerns (30%), lack of understanding where to apply AI (26%), and data infrastructure (26%) among the greatest challenges to AI adoption in their marketing organization — alongside employee experience and readiness (25%) and integration difficulty (24%).

GenAI tool adoption is already high; 88% of B2B marketing leaders say that their organization has adopted use of genAI for their employees (EUR: 90% and non-EUR: 88%), but AI adoption hurdles remain led by data privacy/security concerns (30%), lack of understanding where to apply AI (26%), data infrastructure (26%), and employee readiness (25%). In parallel, “lack of integrated technology solutions” is still a core marketing challenge according to European respondents (24%) (and 22% non-EUR).

The emergence of genAI is also increasing the focus on data quality and security: When we asked B2B marketing decision-makers about their main challenges with adopting genAI for marketing, two of the four most frequent responses were data privacy and security concerns (29%) and data infrastructure (26%). Leaders expect much more from their primary data provider than account and contact lists.

The most reported responses included improved security: 29%; on-demand capacity and scalability: 27%; access to new and innovative services or infrastructure: 26%; and improved ability to meet compliance regulations: 26%. Source: Forrester’s Cloud Survey, 2025. The online version of this graphic contains filterable data. The filterable graphic has an associated spreadsheet that includes the filtered data. Please access the spreadsheet for details.

Digitizing operations: 26%. Procuring data center infrastructure — servers, storage, and networking equipment — with reduced environmental footprint: 25%. E-waste management, device circularity, and asset disposal optimization for data center infrastructure: 25%. Figure 1 Supplemental Material Research Methodologies Forrester’s Infrastructure Survey, 2025

Twenty-seven percent of enterprise infrastructure hardware decision-makers that we surveyed indicated that big-data techniques were one of the main reasons their firm was increasing its storage (see Figure 2). Collecting more IT operations data (26%) was another commonly cited reason for increasing storage needs.

Other functional use cases include: quality assurance (46%), computer vision and video analytics applications (40%), supply chain management (36%), remote office applications (31%), enhanced customer experiences in public places (30%), inventory management (26%), augmented reality (AR)/VR field service (26%), streaming video applications (26%), AR/VR applications (26%), environmental monitoring (26%), and fleet management (20%).

When asked why their organizations were using or expanding third-party managed services, half of enterprise respondents cited simplifying IT network and telecom operations, while 46% pointed to enabling network/telecom teams to focus on core business functions (see Figure 4). Enterprise respondents reported similar motivations for adopting as-a-service models (see Figure 5).

Forrester’s Business And Technology Services Survey, 2024, shows that services decision-makers at enterprises that work or plan to work with third-party professional services for infrastructure over the next 12 months currently outsource or plan to partially or fully outsource the following over the next 12 months: network services (70%), mainframe operations (66%), server operations (66%), storage (67%), and server virtualization and/or container operations (70%).

As for IT’s initiatives and actions over the next 12 months, cost and quality of service lead to enterprise respondents’ focus on modernizing business applications and infrastructure (68%) and adopting agile budgeting (59%) while transforming operations to proactively address issues (53%), especially as AIOps takes off (see Figure 6). Modernizing business applications and infrastructure remains a major and ongoing challenge, particularly for enterprises that have high technical debt.