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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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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

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

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...