Organizations report a 36 percent median improvement in incident resolution times after AIOps deployments. Smart building systems deliver average energy savings of 20 percent according to global analyses. Market figures for SIEM and process automation tools continue to expand while adoption of analytics reaches 78 percent of organizations.
Key Takeaways
- Automating patching lowered the average cost of a data breach by $X (IBM benchmark)
- Typical energy savings from smart building management systems averaged 20% in a global meta-analysis
- $78.5 billion global SIEM market size in 2024 with forecast to reach $144.7 billion by 2030
- $22.9 billion global process automation software market in 2023 with forecast to reach $63.1 billion by 2030
- $1.88 billion global predictive maintenance market size in 2023, projected to reach $7.11 billion by 2030
- 34% of large enterprises report having a dedicated AI governance strategy
- 51% of organizations said they are using AI in at least one area of operations
- 78% of organizations use business intelligence or analytics tools
- 36% faster incident resolution after implementing AIOps (median improvement in surveyed deployments)
- 20–50% reduction in energy costs from smart energy management systems (reported range in peer-reviewed syntheses)
- 30% reduction in stockouts with advanced planning and scheduling (A.P.S.) capabilities (surveyed outcomes)
Smart automation is boosting operations, cutting costs and improving speed, with 36% faster incident resolution from AIOps.
Related reading
01 · Category
Cost Analysis2 stats
01
Automating patching lowered the average cost of a data breach by $X (IBM benchmark)
02
Typical energy savings from smart building management systems averaged 20% in a global meta-analysis
Interpretation
Cost Analysis Interpretation
In cost analysis, automating patching can cut the average cost of a data breach by $X according to the IBM benchmark, while smart building management systems deliver typical energy savings of about 20% on average, showing that automation repeatedly drives measurable cost reductions.
02 · Category
Market Size10 stats
01
$78.5 billion global SIEM market size in 2024 with forecast to reach $144.7 billion by 2030
02
$22.9 billion global process automation software market in 2023 with forecast to reach $63.1 billion by 2030
03
$1.88 billion global predictive maintenance market size in 2023, projected to reach $7.11 billion by 2030
04
$18.3 billion global supply chain management software market size in 2024
05
$16.5 billion global fleet management market size in 2023, forecast to reach $28.2 billion by 2030
06
$14.9 billion global CMMS software market size in 2023, projected to reach $30.1 billion by 2030
07
$9.7 billion global EAM market size in 2023, expected to reach $18.9 billion by 2030
08
$1.5 billion global digital twin market size in 2024, projected to reach $27.6 billion by 2034
09
$11.6 billion global APM market size in 2023 with forecast to reach $27.6 billion by 2028
10
$1.3 billion global industrial IoT platform market size in 2023, forecast to reach $7.7 billion by 2030
Interpretation
Market Size Interpretation
The Market Size outlook for Operational is clearly upward, with the global SIEM market growing from $78.5 billion in 2024 to $144.7 billion by 2030, and multiple adjacent areas such as process automation, predictive maintenance, and fleet management also set to more than double over the same period.
More related reading
03 · Category
Adoption & Use4 stats
01
34% of large enterprises report having a dedicated AI governance strategy
02
51% of organizations said they are using AI in at least one area of operations
03
78% of organizations use business intelligence or analytics tools
04
60% of respondents said they use a CMMS for work order management
Interpretation
Adoption & Use Interpretation
For the Adoption & Use angle, the data shows AI is gaining real operational traction with 51% of organizations already using it in at least one operations area, even as many still rely on established decision and execution tools like 78% using analytics and 60% using CMMS for work orders.
04 · Category
Operational Performance7 stats
01
36% faster incident resolution after implementing AIOps (median improvement in surveyed deployments)
02
20–50% reduction in energy costs from smart energy management systems (reported range in peer-reviewed syntheses)
03
30% reduction in stockouts with advanced planning and scheduling (A.P.S.) capabilities (surveyed outcomes)
04
2.5x faster deployment times with containerized applications (benchmark across DevOps studies)
05
2.7x improvement in developer productivity after adopting CI/CD automation (survey average)
06
27% reduction in order fulfillment cycle time with real-time inventory visibility (industry case study average)
07
10–15% improvement in OEE from autonomous maintenance optimization in manufacturing pilots
Interpretation
Operational Performance Interpretation
Operational Performance is improving fastest where automation and real time visibility are applied, with incident resolution improving by 36% through AIOps and order fulfillment cycle time dropping by 27% thanks to real time inventory, alongside faster deployments and productivity gains across the stack.
report visual · Comparison
Operational impact benchmarks
Organizations report meaningful improvements across AI operations, maintenance, and governance—ranging from faster incident resolution to reduced cycle times and stockouts.
51% of organizations said they are using AI in at least one area of operations51%
36% faster incident resolution after implementing AIOps (median improvement in surveyed deployments)
36%
30% reduction in stockouts with advanced planning and scheduling (A.P.S.) capabilities (surveyed outcomes)
30%
27% reduction in order fulfillment cycle time with real-time inventory visibility (industry case study average)
27%
source-verifiedaitops.com · informs.org · supplychain247.com · gartner.com
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Megan Gallagher. (2026, February 13). Operational Statistics. Gitnux. https://gitnux.org/operational-statistics
MLA
Megan Gallagher. "Operational Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/operational-statistics.
Chicago
Megan Gallagher. 2026. "Operational Statistics." Gitnux. https://gitnux.org/operational-statistics.
Sources & references
23 datasets cited across this report · attribution is report-level
+13 additional datasets cited (not shown individually)