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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

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...