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.
Infrastructure, Cloud & DevOps
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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.
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Research Objectives
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. This is important because software, in many cases, contributes directly to an organization’s bottom line. In IT operations management, the addition of distributed and multi-cloud, cloud-native development and architectures as well as the increasing importance of security mean that the infrastructure is much more complex and significantly more dynamic. For software developers and DevOps teams, understanding the behavior of their code in production and integrated development environments empowers them to troubleshoot and deliver better-performing code and applications in less time. 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.
To determine the current state of observability and AIOps in modern organizations, Enterprise Strategy Group surveyed 374 IT and DevOps/AppDev professionals in North America (US and Canada) responsible for evaluating, purchasing, building, and managing application infrastructure in their organization.
This study sought to answer the following questions:
- What percentage of organizations are using full-stack observability today? How many plan to implement?
- To what extent are organizations deploying observability across their environments?
- How many observability tools do organizations use to collect data from their environments?
- To what extent is observability tool sprawl adding complexity to environments?
- What third-party observability or monitoring tools are in use? Which are most valued by organizations?
- What are the most important organizational priorities for observability?
- What are the most impactful benefits delivered by observability and monitoring strategies?
- What are the biggest challenges or concerns when it comes to deploying observability solutions?
- What are the biggest challenges or concerns in using and supporting observability solutions?
- How are organizations planning to address challenges relating to observability data growth?
- What do organizations consider to be the most important observability tools currently in use?
- Which teams are making the final decisions around which observability tools are used?
- How confident are teams in the observability tools they are using across all stages of the application lifecycle (e.g., build, release, and operate)?
- What are the plans to invest in additional tools and services to support monitoring and observability strategies? Which tools or services are organizations targeting?
- What is the current and future adoption landscape for AIOps?
- Among organizations using AIOps, what benefits are delivered and how impactful are they?
- How important is AIOps in terms of overall observability strategies and future related plans?
- What are the barriers to AIOps adoption?
Survey participants represented a wide range of industries, including manufacturing, technology, financial services, and retail/wholesale. For more details, please see the Research Methodology and Respondent Demographics sections of this report.
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About This Report
This report covers trending areas of interest across 240+ IT markets over the last 6 months (January 2023 – June 2023) in five (5) regions across the TechTarget & BrightTALK network: WW, NA, EMEA, APAC, LATAM.
- Top 20 markets driving activity
- Represents the top 20 broad technology markets driving the most activity in the last 6 months. Activity data can help to show where audience research is growing or declining and therefore help reinforce which markets are hot or declining.
- 25 topic areas on the rise
- Shows the top 25 granular topics growing the most across the TechTarget network in the last 6 months. This gives insight into the content areas that are on the rise right now.
Discover what’s trending on our network, which you can leverage to engage IT buyers in market now and improve marketing and sales effectiveness.
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If you are not yet a Subscription Client but would like to learn more about accessing this report, please contact us.
- Top 20 markets driving activity
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Research Objectives
- Determine the current market state of generative AI, including adoption and budget strategies.
- Identify current and planned GenAI use cases and prioritization across organizations.
- Understand key challenge areas with GenAI and investment requirements to address them.
- Investigate key areas of application and focus for GenAI technologies, including cybersecurity, application development, analytics, and customer experience. (more…)
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Research Objectives
While AI in general was already assimilating into the everyday business and IT lexicon thanks to ongoing AI and analytics strategies and initiatives, 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.
Most organizations are aware of GenAI, and a rising percentage are currently formulating strategies to both harness the technology’s benefits and control its use to prevent data quality issues and information leaks. To assess the state of GenAI strategies and plans, TechTarget’s Enterprise Strategy Group surveyed 670 IT professionals and business decision makers in North America (65%), EMEA (18%), APAC (16%), and LATAM (2%) involved with generative AI initiatives in their organization. This study sought to answer the following questions:
- What is the status of GenAI initiatives within organizations?
- How are organizations using, or planning use, large language models (LLMs) to support GenAI initiatives?
- Are organizations allocating, or planning to allocate, budget to support GenAI initiatives? If so, what is the percentage of IT budgets allocated to GenAI?
- In which lines of business are organizations currently applying GenAI? Moving forward, which of these areas will benefit most from the use of GenAI?
- Which teams or stakeholders actively contribute to shaping GenAI initiatives in organizations?
- What technology investments are needed to support GenAI initiatives?
- What do organizations identify as the primary benefits of using GenAI in their environments?
- What are the most prioritized use cases for GenAI, particularly in environments where the technology is applied across multiple areas?
- What are the biggest challenges organizations face in GenAI implementations?
- In which areas do organizations feel they need to invest (time and/or money) to support the use of GenAI?
- What type of third parties do organizations currently, or plan to, work with to support GenAI initiatives?
- Are organizations more or less likely to consider vendors that incorporate GenAI capabilities as part of their products or services?
- How much more, if at all, are organizations willing to pay for a product or service that uses GenAI versus a comparable product or service that does not use GenAI?
- What types of information or media would help organizations assess GenAI?
- For which application development use cases are organizations using, or planning to use, GenAI? What about use cases for security and customer experience (CX)? Where will investments be made?
- How do, or will, organizations ensure the security and privacy of data used in GenAI models?
- For which security use cases are organizations using, or planning to use, GenAI?
- Which areas of the analytics lifecycle will benefit most from the use of GenAI?
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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IT organizations are increasingly turning to a “cloud-first” strategy for their digital transformation initiatives to overcome challenges related to maintaining operations in a fast-paced business landscape. This approach involves prioritizing cloud-based, developer-friendly solutions over traditional on-premises software and infrastructure to increase agility.
Learn more about these trends with the infographic, Distributed Cloud Series: The Mainstreaming of Cloud-native Apps and Methodologies.