As AI continues its meteoric rise into business and IT environments, organizations are rapidly assembling or accelerating strategies to support AI technologies across every applicable area. Unlike niche technologies that impact only certain processes or personnel, AI has wide-ranging potential to transform entire businesses, IT environments, and associated teams. In turn, AI strategies must be multi-pronged efforts that properly align business objectives with AI initiatives and expectations, which requires thorough participation from stakeholders across the organization. The underlying infrastructure and other supportive elements must be fully capable of supporting that tandem strategy.
While many organizations are consistent in their efforts to build AI strategies, the components and direction of those strategies often vary. To assess the evolving AI landscape and the infrastructure that supports it, TechTarget’s Enterprise Strategy Group surveyed 375 data and IT professionals in North America (US and Canada) responsible for strategizing, evaluating, purchasing, and/or managing infrastructure specifically supporting AI initiatives for their organization. This study sought to answer the following questions:
- What are the primary business objectives for implementing AI? How long does it take for organizations to start seeing value from AI initiatives?
- What are the top challenges organizations encounter when implementing AI?
- What individuals or teams influence decision making related to infrastructure used to support AI initiatives? Which of these has the most influence on final decisions?
- How are organizations planning to address skills gaps related to the selection, implementation, and management of infrastructure supporting AI initiatives?
- In which physical locations do organizations primarily deploy their AI infrastructure? What are the top factors that influence the choice of these locations? Are AI environments mostly centralized, mostly decentralized, or an even mix of both?
- What capabilities of AI infrastructure are most important?
- Are organizations using internal resources, third-party resources, or both to manage their AI infrastructure?
- How important is sustainability and environmental responsibility when selecting AI infrastructure? How important is a vendor’s stance on these factors when making purchase decisions for AI infrastructure?
- What types of data do organizations use to build and train AI models and algorithms? What steps do organizations take to ensure accuracy in the data used for building and training these models?
- How do organizations handle the movement of the large amounts of data required to support AI initiatives? What challenges are involved with this process?
- How are organizations using synthetic and third-party data to support AI model training?
- How are organizations using generative AI (GenAI)? What challenges are they encountering?
- To what extent are developers leveraging AI infrastructure resources? How do developers access these resources?
- How do organizations measure the success and effectiveness of AI initiatives?
- What is AI’s impact on employee productivity, processes, workflows, competitiveness, and other factors?
Survey participants represented a wide range of industries, including financial, manufacturing, retail/wholesale, and healthcare, among others. For more details, please see the Research Methodology and Respondent Demographics sections of this report.
