IT organizations are contending with distributed environments spanning their traditional on-premises data centers, multiple public cloud providers, and the edge and colocation locations that fall in between. Recent research by TechTarget’s Enterprise Strategy Group reveals that, in addition to the application deployment and management decisions that accompany these environments, modern IT organizations have some interesting decisions to make when it comes to their spending on the related technologies.
Infrastructure, Cloud & DevOps
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The use of two or more cloud computing services (i.e., multi-cloud strategies) is a modern reality for businesses looking to serve the technology requirements of their teams and thrive in a competitive landscape. TechTarget’s Enterprise Strategy Group recently surveyed IT professionals on the status of their multi-cloud strategies (and providers) today.
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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. While many organizations are consistent in their efforts to build AI strategies, the components and direction of those strategies often vary. TechTarget’s Enterprise Strategy Group recently surveyed data and IT professionals responsible for the infrastructure supporting AI initiatives at their organization to gain insights into these trends.
To learn more about these trends, download the free infographic, Navigating the Evolving AI Infrastructure Landscape.
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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.
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The need for observability in IT operations management is driven by the desire for organizations to reduce downtime, increase operational security, and improve customer, digital, and employee experiences. In IT operations management, the addition of distributed and multi-cloud, cloud-native development and architectures means that the infrastructure is much more complex. Against this backdrop, IT and DevOps teams are embracing observability and, to a lesser extent, AIOps to help them instrument and monitor their infrastructure and applications.
Learn more about these trends with this free infographic.
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Most organizations are actively engaged in observability programs, and half of these companies have added AIOps capabilities that provide better end-to-end visibility and real-time analytical insights into their applications and infrastructure environments. Key purchasing decision makers are IT professionals and application developers who report significant benefits as a result of deploying AIOps tools. However, there’s a notable split in priorities between IT teams and DevOps/developers that can greatly influence purchasing decisions.
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Organizations practicing observability are increasingly integrating AIOps tools and techniques to speed process automation, increase end-to-end visibility, provide real-time data analysis, and yield trustworthy outcomes. Midsize and large organizations are making immediate plans to expand their observability investments, and more than half of these companies plan to add AIOps tools. Though still considered in the early stages of widespread deployment, AIOps has shown strong results in simplifying operations and accelerating the scaling of observability practices.
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Research Objectives
- Identify the state of contemporary application infrastructure environments across the distributed cloud.
- Determine the trajectory of infrastructure strategies both on- and off-premises.
- Explore application strategies and their impact on infrastructure decisions.
- Monitor the movement of applications and data across the distributed cloud.
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Generative artificial intelligence (GenAI) recently stormed the market and mindshare of decision makers across industries and major geographic markets. Business leaders see a massive opportunity to positively impact operations and customer strategies with GenAI, but its adoption and use across all business units carry a fair share of trepidation.
Learn more about these trends with the infographic, Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns.
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Application developers are challenged with efficiently creating innovative solutions while managing time constraints, which can be mitigated by the transformative impacts of generative artificial intelligence (AI) streamlining code generation and accelerating development processes. Organizations have integrated generative AI (GenAI) into their operational setup to accelerate code creation, refine code structures, elevate code quality, and deliver personalized customer experiences. By harnessing GenAI, application developers tackle issues by capitalizing on the technology’s ability to automate tasks, drive creativity, and deliver innovative solutions.
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Organizations seeking digital transformation increasingly use cloud-native applications as their vehicle, which typically entails orienting their development and deployment environments toward cloud infrastructure. Indeed, Enterprise Strategy Group research showed an increase in year-over-year spending on cloud-native architectures, with microservices increasingly preferred over traditional multi-tier deployment methods. IT leaders should continue building on their existing collaboration with DevOps and other application development professionals to move closer to the ultimate goal of consistently deploying fully portable applications across multiple clouds.
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Many organizations recognize that a digital transformation of the business is possible only with a robust, cloud-native application development and deployment strategy. But sometimes their readiness assessment is unrealistic, and actual maturity levels vary widely. To succeed in this transformative journey, organizations must fully develop their cloud-native strategies, assess how well they are currently positioned, and determine whether they have the right tools, people, and technologies to meet their cloud-native deployment goals.