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IT Pros Want AI and AIOps but Are Concerned About Data Quality

Despite a near-unanimous desire to adopt AI technology, very few respondents have confidence in their organization's readiness to integrate AI, pointing to limitations in data and infrastructure and security concerns, according to the 2024 IT Trends Report, AI: Friend or Foe?, based on a survey of nearly 700 IT professionals conducted by SolarWinds.

The report found that while IT pros have a growing interest in embracing AI technology, with nine out of ten already using or planning to use AI, concerns remain about data quality, database infrastructure readiness, and — above all else — security and privacy.

"While talk of AI has dominated the industry, IT leaders and teams recognize the outsize risks of the still-developing technology, heightened by the rush to build AI quickly rather than smartly," said Krishna Sai, SVP, Technology and Engineering at SolarWinds. "With the proper internal systems in place and by prioritizing security, fairness, and transparency while building AI, these technologies can serve as a valuable advisor and coworker to overworked teams, but this survey shows that IT pros need to be consulted as their companies invest in AI."

Overall, the industry's sentiment reflects cautious optimism about AI despite the obstacles. Almost half of IT professionals (46%) want their company to move faster in implementing AI despite costs, challenges, and concerns, but only 43% are confident that their company's databases can meet the increased needs of AI. Moreover, even fewer (38%) trust the quality of data or training used in developing AI technologies.

The report unveiled significant insights into IT professionals' perspectives on AI, including:

AIOps Drives Efficiency and Productivity

IT pros cited AIOps as the AI technology that will have the most significant positive impact on their role (31%), ranking above large language models and machine learning. More than a third of respondents (38%) said their companies already use AI to make IT operations more efficient and effective.


Source: SolarWinds

Distrust of Data Powering AI

Only 38% of respondents are very trusting of the data quality and training used in AI technologies, and rank data quality as a major barrier to AI adoption, second only to security and privacy risks. Because of this, today's IT teams see AI as an advisor (33%) and a sidekick (20%) rather than a solo decision-maker.

Privacy and Security Concerns Are Barriers to AI Adoption

Respondents overwhelmingly named privacy and security concerns as the most significant barrier to AI integration. When asked about their challenges with AI, four out of 10 (41%) respondents said they've had negative experiences. Of those, privacy concerns (48%) and security risks (43%) were most often cited as the reasons why.

IT Pros Call for Government Regulation

IT pros specifically call for increased government regulations to address security (72%) and privacy (64%). More than half of respondents also believe government regulation should play a role in combating misinformation, as training AI models — including data quality — is a matter of both ethics and security.

To ensure successful and secure AI adoption, IT pros recognize that organizations must develop thorough policies on ethics, data privacy, and compliance, pointing to ethical considerations and concerns about job displacement as other significant barriers to AI adoption. The report found that more than a third of organizations (35.6%) still don't have these policies in place to guide proper AI implementation.

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IT Pros Want AI and AIOps but Are Concerned About Data Quality

Despite a near-unanimous desire to adopt AI technology, very few respondents have confidence in their organization's readiness to integrate AI, pointing to limitations in data and infrastructure and security concerns, according to the 2024 IT Trends Report, AI: Friend or Foe?, based on a survey of nearly 700 IT professionals conducted by SolarWinds.

The report found that while IT pros have a growing interest in embracing AI technology, with nine out of ten already using or planning to use AI, concerns remain about data quality, database infrastructure readiness, and — above all else — security and privacy.

"While talk of AI has dominated the industry, IT leaders and teams recognize the outsize risks of the still-developing technology, heightened by the rush to build AI quickly rather than smartly," said Krishna Sai, SVP, Technology and Engineering at SolarWinds. "With the proper internal systems in place and by prioritizing security, fairness, and transparency while building AI, these technologies can serve as a valuable advisor and coworker to overworked teams, but this survey shows that IT pros need to be consulted as their companies invest in AI."

Overall, the industry's sentiment reflects cautious optimism about AI despite the obstacles. Almost half of IT professionals (46%) want their company to move faster in implementing AI despite costs, challenges, and concerns, but only 43% are confident that their company's databases can meet the increased needs of AI. Moreover, even fewer (38%) trust the quality of data or training used in developing AI technologies.

The report unveiled significant insights into IT professionals' perspectives on AI, including:

AIOps Drives Efficiency and Productivity

IT pros cited AIOps as the AI technology that will have the most significant positive impact on their role (31%), ranking above large language models and machine learning. More than a third of respondents (38%) said their companies already use AI to make IT operations more efficient and effective.


Source: SolarWinds

Distrust of Data Powering AI

Only 38% of respondents are very trusting of the data quality and training used in AI technologies, and rank data quality as a major barrier to AI adoption, second only to security and privacy risks. Because of this, today's IT teams see AI as an advisor (33%) and a sidekick (20%) rather than a solo decision-maker.

Privacy and Security Concerns Are Barriers to AI Adoption

Respondents overwhelmingly named privacy and security concerns as the most significant barrier to AI integration. When asked about their challenges with AI, four out of 10 (41%) respondents said they've had negative experiences. Of those, privacy concerns (48%) and security risks (43%) were most often cited as the reasons why.

IT Pros Call for Government Regulation

IT pros specifically call for increased government regulations to address security (72%) and privacy (64%). More than half of respondents also believe government regulation should play a role in combating misinformation, as training AI models — including data quality — is a matter of both ethics and security.

To ensure successful and secure AI adoption, IT pros recognize that organizations must develop thorough policies on ethics, data privacy, and compliance, pointing to ethical considerations and concerns about job displacement as other significant barriers to AI adoption. The report found that more than a third of organizations (35.6%) still don't have these policies in place to guide proper AI implementation.

Hot Topics

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...