How 121 senior engineering leaders are deploying AI in observability today, where it is delivering value, what is holding back broader autonomy, and where the next year of investment is going.
AI has moved from experiment to production in observability. 79% of senior engineering leaders say AI is now a significant or mature part of their stack, 87% plan to increase investment over the next 12 to 18 months, and root cause analysis is where teams say AI is delivering the biggest impact today. This report looks at where AI runs in observability environments, what value it is delivering, what is holding back broader autonomy, and where leaders are putting their next dollar.
Every respondent is a senior engineering leader at a 500+ employee company who owns a budget or directly influences purchasing for engineering tooling. This is also an active buying audience: 93% are running three or more vendor evaluation processes per year, and 28% are running five or more.
AI in observability has crossed from experiment into production. 79% of senior engineering leaders say AI is now a significant or mature part of their observability strategy, and adoption is no longer concentrated in a single workflow. Teams report applying AI across log summarization, anomaly detection, capacity planning, and root cause analysis. The value they describe is concrete and consistent: faster resolution and earlier detection. Adoption maturity is not evenly distributed. UK and Australian teams report higher rates of mature deployment than their US peers.
Across 8 distinct observability workflows, leaders report using AI for log summarization (74%), anomaly detection (67%), and capacity planning (63%) at the highest rates. When asked which workflow AI most impacts today, 30% point to root cause analysis. The reasons cluster tightly: 40% credit AI with cutting time to resolution, and 36% credit it with catching issues earlier or more accurately. AI in observability has stopped being a question of whether. Buyers are now asking how far to take it.
"Log summarization/pattern recognition. Why? Because we usually used to manually sift through thousands or millions of log lines. AI can now condense into a clear, human readable summary in seconds."
"Root cause analysis, because AI helps quickly connect signals from logs, metrics, and traces, which reduces the time it takes to pinpoint what actually caused an issue."
The ceiling on AI value is autonomy. Only 18% of leaders are comfortable letting AI operate autonomously across production workflows. The other 82% want a human in the loop in some form. The regional split is sharp: just 9% of US leaders are comfortable with full autonomy, compared with 29% in the UK and 25% in Australia.
Teams have proven AI works on individual workflows. The next dollar of ROI sits behind a trust threshold that 82% of leaders haven't crossed. The unlock is specific: a proven track record of accurate outputs (62%), explainable and auditable decision-making (51%), and consistent performance over time (44%). Only 21% picked visibility into model internals. Buyers want evidence that AI works, not a window into the model. UK leaders weight regulatory oversight at 44%, nearly double the US (26%).
Data quality alone won't do it: even among leaders who say their monitoring is strong enough for AI to perform well, only 48% are comfortable with high autonomy. Better data is necessary, but the trust prerequisites above also need to be met. The next section shows why the data foundation is the harder of the two requirements to satisfy today.
"The biggest gap is trust at scale. Today AI is helpful for insights and simple actions, but it is not consistent enough to fully rely on for more complex decisions across all systems. Over the next year or two we need it to be more accurate, context aware and dependable, so we can safely expand from suggestion to action."
"AI tooling mostly reacts to problems but it needs to be proactive and self operating, going from a monitoring assistant to an autonomous operator."
When asked directly whether their monitoring quality limits AI performance, only 42% of leaders say their monitoring is strong enough, and 23% explicitly say monitoring gaps significantly limit AI today. The same answer shows up across the open-ended questions. When leaders describe what is hardest, what is missing, and what is currently preventing their ideal AI capability from working perfectly, they return to the same handful of themes. Trust and accuracy are the symptom most named. When leaders describe the underlying cause, telemetry quality and fragmented data come up over and over.
The most direct evidence is the simplest: only 42% of leaders say their monitoring is strong enough for AI to perform well, and 23% explicitly say monitoring gaps significantly limit AI today. UK leaders are the most candid. Just 28% say their monitoring is strong enough, the lowest of any region. Open-ended responses converge on the same answer. When asked what is preventing their ideal AI from working perfectly, 53% named trust, accuracy, or reliability concerns, and 36% named data quality or fragmented telemetry. Across all four open-ended questions, 60% of respondents (73 of 121 unique people across Q8, Q19, Q21, and Q22) flagged data quality, telemetry, or monitoring as a constraint somewhere in their answers. The diagnosis varies by region: US leaders point to data quality as the single hardest part (28%), UK leaders split between trust and cost (20% each), and Australian respondents call out integration with legacy systems (29%). The model itself is rarely where leaders say AI breaks. The data feeding it is the constraint that decides how far AI can extend.
What the constraint costs in practice. Asked to walk through the most recent time AI fell short or monitoring gaps mattered, leaders described tangible, executive-level consequences: 70% named wasted engineer time, 52% named slower incident response and stretched MTTR, and 48% named direct customer-facing impact. The cost of the data-layer constraint is paid in engineer-hours and customer experience, not in abstract AI safety concerns.
"I'd want consistently accurate, explainable root cause analysis that works across all services and dependencies in real time, and what's preventing it is fragmented/low-quality telemetry and incomplete system context that makes it hard for AI to reliably correlate signals across the full stack."
"I would choose full automated root caused remediation. The goal is for the system to not just identify the issues, but safely resolve it. Currently, fragmented legacy data and a lack of high confidence telemetry prevent this, as the risk of an automated false fix causing a larger outage is still too high."
"Perfect automated root-cause analysis. Data fragmentation and poor telemetry currently prevent the AI from connecting the dots without manual help."
Investment patterns confirm the diagnosis. 87% of leaders plan to increase observability AI investment over the next 12 to 18 months, and the destination of those dollars is unusually consistent. Data quality, pipeline, and telemetry work is the single largest category. The shape of platform teams want signals the same intent: hybrid AI that combines turnkey defaults with the ability to customize. Leaders are spending against the constraint they named.
Two signals point in the same direction. Where the dollars go: 58% of leaders are putting investment into data quality, pipeline, or telemetry work, the single largest category, ahead of buying new AI platforms (36%), building internal tooling (34%), or hiring AI engineers (15%). What shape they want: 85% prefer either a hybrid platform (50%) or full customization (35%). Only 9% want fully out-of-the-box AI, and two-thirds of teams already deliver AI either natively in their observability platform (35%) or in a combination of native and integrated approaches (31%). The platform-shape preference inverts between regions: 59% of US leaders want a hybrid approach, but 56% of Australian leaders want full customization. UK leaders split nearly evenly between the two. Leaders are spending on the foundation first, and they want platforms that adapt to their environment rather than dictate it.
"If I could wave a wand, the one AI capability would be perfect, real time root cause analysis with safe auto remediation. I feel currently the data quality is not good enough, current integration challenges, and trust and risk of automation errors are currently preventing it from happening."
"The biggest 'ideal' capability is fully autonomous incident resolution, where AI detects, diagnoses, fixes, and verifies issues without human help for routine cases. What prevents it today: incomplete system visibility (logs/metrics are noisy or inconsistent), complex root causes…"
The pattern across all 121 leaders points the same direction. AI is delivering measurable value where it runs, but autonomy is gated by trust, and the data layer is the constraint that decides how far AI can extend. Investment is flowing toward fixing the foundation, and teams want platforms that adapt to their environment rather than dictate it.
Datadog is built for the trust threshold leaders described. The platform runs on a unified telemetry foundation, the prerequisite for AI to perform consistently rather than flake, addressing the 44% of leaders who name consistent and predictable performance as a top trust prerequisite. AI built natively on that foundation produces auditable outputs grounded in the same data engineers already see, addressing the 51% who name explainable, auditable decision-making. By running AI on top of an observability platform already deployed across thousands of production environments, Datadog inherits the operating track record that 62% of leaders say is the top unlock for trusting AI more broadly.
When the foundation is solid, AI becomes safer to trust, easier to scale, and cheaper to run. That is the bet 87% of leaders are making with their next 12 months of investment.
See how Datadog AI works →This research was conducted through a conversational AI-led survey designed to capture both structured responses and substantive open-ended commentary from senior engineering leaders.