Top Concerns for Tech Decision Makers

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra.

Notably, eight in 10 decision makers (85%) also said that data ownership has changed over the last year with the emergence of AI.

"AI will continue to disrupt and reshape the future of work," said Collibra Stijn "Stan" Christiaens, co-founder and Chief Data Citizen at Collibra. "As organizations look to integrate AI more into the workplace, it is ever more critical to connect data owners with privacy and compliance teams to balance AI innovation with trust and ensure data privacy."

Despite concerns around data privacy and ROI, the survey indicates a strong overall momentum towards AI adoption, with 86% of organizations planning to proceed with their AI initiatives. However, this enthusiasm varies by company size. While nearly all large companies (96%) intend to forge ahead with their AI plans despite the evolving landscape, smaller (78%) and medium-sized (79%) organizations are exhibiting a more measured approach.

The survey also found that nine in 10 employees at larger organizations (1,000+) say their company encourages the use of AI in the workplace and provides the necessary tools to support their work. The same percentage also said that their company has issued an AI use policy or guidelines to their employees.

In addition, the survey found that nearly nine in 10 decision-makers (88%) say they have a lot or a great deal of trust in their own companies' approach to shaping the future of AI, with three quarters (75%) agreeing that their company prioritizes AI training and upskilling, with decision-makers at large companies (1000+ employees) more likely than those at small companies (1-99 employees) to agree (87% vs. 55%).

"As Al continues to be adopted across Corporate America, organizations need to centralize visibility of AI models and agents across AI platforms and ensure traceability between AI use cases and the data that feeds them," stated Christiaens. "By adopting an approach to AI governance that connects models, data, and policies, organizations can protect critical data while ensuring confidentiality measures."

Methodology: The Harris Poll surveyed more than 300 US adults ages 21+ who are employed full-time as data management, privacy and/or AI decision makers at their current companies.

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Top Concerns for Tech Decision Makers

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra.

Notably, eight in 10 decision makers (85%) also said that data ownership has changed over the last year with the emergence of AI.

"AI will continue to disrupt and reshape the future of work," said Collibra Stijn "Stan" Christiaens, co-founder and Chief Data Citizen at Collibra. "As organizations look to integrate AI more into the workplace, it is ever more critical to connect data owners with privacy and compliance teams to balance AI innovation with trust and ensure data privacy."

Despite concerns around data privacy and ROI, the survey indicates a strong overall momentum towards AI adoption, with 86% of organizations planning to proceed with their AI initiatives. However, this enthusiasm varies by company size. While nearly all large companies (96%) intend to forge ahead with their AI plans despite the evolving landscape, smaller (78%) and medium-sized (79%) organizations are exhibiting a more measured approach.

The survey also found that nine in 10 employees at larger organizations (1,000+) say their company encourages the use of AI in the workplace and provides the necessary tools to support their work. The same percentage also said that their company has issued an AI use policy or guidelines to their employees.

In addition, the survey found that nearly nine in 10 decision-makers (88%) say they have a lot or a great deal of trust in their own companies' approach to shaping the future of AI, with three quarters (75%) agreeing that their company prioritizes AI training and upskilling, with decision-makers at large companies (1000+ employees) more likely than those at small companies (1-99 employees) to agree (87% vs. 55%).

"As Al continues to be adopted across Corporate America, organizations need to centralize visibility of AI models and agents across AI platforms and ensure traceability between AI use cases and the data that feeds them," stated Christiaens. "By adopting an approach to AI governance that connects models, data, and policies, organizations can protect critical data while ensuring confidentiality measures."

Methodology: The Harris Poll surveyed more than 300 US adults ages 21+ who are employed full-time as data management, privacy and/or AI decision makers at their current companies.

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Signs It May Be Time to Reassess Your IT Infrastructure Strategy

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

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Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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