The U.S. janitorial services market reached 73.3 billion dollars and carries forecasts to 86.6 billion dollars. Janitors and cleaners total more than 2.7 million positions with median annual pay of 35,360 dollars. These figures coincide with rising AI spending alongside persistent gaps in worker training and company concerns over implementation risks.
Key Takeaways
- The U.S. janitorial services market size was $73.3 billion in 2023
- The U.S. janitorial services market is forecast to reach $86.6 billion by 2028
- The U.S. janitorial services industry is forecast to grow at an annualized rate of 3.1% from 2023 to 2028
- In the U.S., 55% of workers report not having received training to use specific technologies at work, limiting adoption readiness
- A PwC survey found 63% of companies are concerned about AI implementation risks
- Gartner forecasts worldwide end-user spending on AI will reach $633.0 billion in 2024, indicating funding momentum for AI adoption
- UL 2900-2-2 (software lifecycle for intelligent devices) is a specific cybersecurity standard that supports secure deployment of connected cleaning robots
- UL 3400 covers software cybersecurity for network-connected products relevant to smart janitorial equipment
- The NIST AI RMF defines “likelihood” and “impact” as part of risk estimation in AI systems
- US Bureau of Labor Statistics (BLS) states that janitors and cleaners had a median pay of $35,360 in 2023, indicating labor cost pressure that motivates AI-enabled automation
- BLS reports janitors and cleaners employment was 2,703,360 in May 2023
- BLS reports “Janitors and Cleaners, Except Maids and Housekeeping Cleaners” median wage $16.88 per hour in 2023
- NIST AI RMF states it is designed to manage risks across AI systems
- NIST AI RMF emphasizes organizational oversight (“Govern” function)
- GDPR requires personal data processing has a lawful basis (Article 6)
The U.S. janitorial market will keep growing, reaching $86.6 billion by 2028.
Related reading
01 · Category
Market Size & Demand30 stats
01
The U.S. janitorial services market size was $73.3 billion in 2023
02
The U.S. janitorial services market is forecast to reach $86.6 billion by 2028
03
The U.S. janitorial services industry is forecast to grow at an annualized rate of 3.1% from 2023 to 2028
04
IBISWorld estimates there were 64,847 businesses in the U.S. janitorial services industry in 2023
05
IBISWorld estimates the U.S. janitorial services industry employed 566,133 people in 2023
06
The cleaning and janitorial services market in North America generated $40.0 billion in 2023
07
The global cleaning and janitorial services market generated $490.4 billion in 2023
08
The global cleaning and janitorial services market is projected to reach $1,411.4 billion by 2032
09
ISSA estimates the global cleaning industry will be valued at $507 billion in 2024
10
ISSA estimates the global cleaning industry will grow to $563 billion by 2027
11
ISSA reports that facility services represent 20% of total building service spending
12
The ISSA 2023 report estimates the commercial cleaning market is $167.9 billion in the US
13
The US commercial cleaning industry reached $200+ billion when including related products and services
14
Janitorial and cleaning services are a top subset within “Facility Services” spending in the US
15
The global surface cleaning equipment market is projected to reach $8.0 billion by 2030
16
The global floor scrubber market is projected to reach $3.1 billion by 2027
17
The global industrial robot market is expected to reach $153.6 billion by 2027, supporting automation adoption in industrial cleaning contexts
18
The global warehouse robotics market is projected to reach $6.9 billion in 2026, relevant to facilities with cleaning/robot deployment
19
The global cleaning robots market is expected to reach $5.2 billion by 2028
20
The Fortune Business Insights forecast says the cleaning robots market was $1.5 billion in 2022
21
The janitorial services industry in the US is highly fragmented with many small operators (IBISWorld “fractured” market)
22
IBISWorld reports there are no dominant players holding large market shares in U.S. janitorial services
23
The US building services construction market was valued at $121.6 billion in 2023 (facility-related spend that overlaps cleaning/maintenance budgets)
24
The global facility management market was valued at $1.2 trillion in 2023
25
The global facility management market is projected to reach $2.0 trillion by 2029
26
The global smart cleaning market is projected to reach $16.3 billion by 2030
27
The smart cleaning market was estimated at $5.8 billion in 2022
28
The commercial building market size is $10.8 trillion globally (context for facilities that buy janitorial services)
29
The US EPA estimates that the cleaning and disinfecting industry contributes significantly to facility operations (context)
30
In a global survey, 72% of facility managers said they want to use more technologies to improve operations (drivers for AI-enabled cleaning)
Interpretation
Market Size & Demand Interpretation
The U.S. janitorial business is a fast-growing, highly fragmented hustle worth $73.3 billion in 2023 that’s expected to hit $86.6 billion by 2028, while globally the cleaning and janitorial market balloons from $490.4 billion in 2023 to a projected $1,411.4 billion by 2032, suggesting that as facilities spend big on services and more than two thirds of facility managers want smarter tech, the dust-and-dollars game is getting upgraded with automation, robots, and “smart cleaning” without anyone ever having to stop sweeping.
02 · Category
Adoption Drivers & Barriers28 stats
01
In the U.S., 55% of workers report not having received training to use specific technologies at work, limiting adoption readiness
02
A PwC survey found 63% of companies are concerned about AI implementation risks
03
Gartner forecasts worldwide end-user spending on AI will reach $633.0 billion in 2024, indicating funding momentum for AI adoption
04
Gartner forecasts worldwide end-user spending on AI will reach $260.2 billion in 2025
05
Gartner forecasts worldwide end-user spending on AI will reach $554.0 billion in 2024 (alternate yearly figure)
06
McKinsey reports that 67% of organizations do not use AI widely and only 9% have adopted at scale
07
McKinsey’s state of AI reports that 56% of companies are experimenting with AI but not operationalizing it broadly
08
McKinsey reports that 17% of organizations are using AI in at least one business function
09
IBM’s survey found 57% of businesses say AI is already part of their business strategy
10
IBM’s survey found 35% of businesses say AI adoption is already in production
11
Deloitte reports that 38% of organizations cite lack of AI skills as a major barrier
12
IBM reports 2 in 3 CEOs expect AI to be critical to business growth (AI as a strategic driver)
13
A 2023 Stanford/AI Index reports that AI adoption is increasing with 59% of surveyed businesses using AI in at least one function
14
The AI Index 2024 reports 55% of organizations use AI in at least one function (platform adoption)
15
The AI Index 2024 reports AI-related hiring increasing by 74% from 2018-2022, showing adoption momentum
16
According to the World Economic Forum, 85 million jobs could be displaced and 97 million created by 2025 (labor impact driver/barrier for cleaning workforce change)
17
According to WEF, 23% of jobs are expected to change due to automation by 2027
18
The WEF report states that 44% of respondents expect AI to be implemented in their organizations within the next 2-3 years
19
A McKinsey survey reports that data readiness is a common barrier: 53% say data is difficult to integrate for AI use
20
PwC finds 50% of companies lack internal AI governance policies, a barrier
21
PwC global AI study finds 52% are concerned about AI fairness
22
A NIST resource notes that bias can lead to unfair outcomes, highlighting compliance barrier: NIST AI Risk Management Framework identifies 4 stages and risk governance
23
NIST AI RMF says organizations should establish “govern” as one of four functions
24
NIST AI RMF includes “manage” function as part of risk controls (governance barrier)
25
World Health Organization notes that infection prevention and control requires adherence to guidance (driver for AI-enabled monitoring)
26
WHO infection prevention guidance emphasizes hand hygiene; adoption barrier context (behavioral)
27
CDC states improved ventilation and cleaning reduce transmission (driver)
28
CDC estimates proper ventilation can reduce risk of airborne transmission (driver for sensor/AI)
Interpretation
Adoption Drivers & Barriers Interpretation
In the U.S. janitorial world, AI adoption is accelerating but getting stuck in the usual bottlenecks of untrained workers, uneven readiness, and risk worries, where most companies are still experimenting rather than deploying at scale, funding keeps rising, hiring and workload change are looming, and progress depends on clean data, internal governance, fairness controls, and keeping infection prevention practices at the center while technology quietly takes on more monitoring and decision support.
03 · Category
Technology, AI Capabilities & Robotics30 stats
01
UL 2900-2-2 (software lifecycle for intelligent devices) is a specific cybersecurity standard that supports secure deployment of connected cleaning robots
02
UL 3400 covers software cybersecurity for network-connected products relevant to smart janitorial equipment
03
The NIST AI RMF defines “likelihood” and “impact” as part of risk estimation in AI systems
04
NIST AI RMF includes the “Map” function for understanding AI systems
05
NIST AI RMF includes the “Measure” function for assessing performance
06
NIST AI RMF includes the “Manage” function for mitigating risks
07
OpenAI’s GPT-4 Technical Report states it uses a transformer architecture with attention mechanisms (foundation for AI assistant copilots used in ops)
08
OpenAI’s GPT-4 Technical Report states training used a “mixture of experts” approach (capability baseline)
09
Microsoft’s Azure AI states “Vision” features can perform object detection and OCR (applied to cleaning inspections)
10
Microsoft Azure AI Language supports 100+ languages via “Text Translation” capability
11
Google Cloud Vision API supports “Label Detection” and “Object Localization” with confidence scores (used for surface/spot inspection automation)
12
Google Cloud Vision API supports OCR via “Text detection” feature
13
OpenCV’s documentation shows it supports 2,500+ algorithms across computer vision (enabling AI vision for cleaning)
14
OpenCV states it supports multiple programming languages including Python and C++ (implementation breadth)
15
iRobot Roomba self-driving vacuums use “visual mapping” technology (mapping for cleaning routes)
16
iRobot Imprint Smart Mapping uses a mapping process based on your home’s floor layout (route planning)
17
CliniServ (UV-C) robot systems provide “UVC disinfection” cycles typically measured in minutes (automation for cleaning)
18
UV-C disinfection is used for environmental cleaning in healthcare (technical method)
19
The CDC environmental cleaning guideline lists UV-C as one of the tools for disinfection (technology category)
20
SAE International defines “fully automated” levels and autonomous system definitions used in autonomous floor scrubbing robots
21
ISO 11784/11785 animal RFID standards are unrelated; exclude—(replaced) Wheel robot sensors: OSHA states safety requirements for mobile robot operations, indicating sensor/automation capabilities
22
IEEE 7000 series describes model process for AI systems (risk/tech standardization)
23
IEEE 7001 series provides transparency and interpretability concepts relevant to cleaning AI decision support
24
NVIDIA’s “Jetson Orin” provides up to 275 TOPS (AI compute for vision/robotics)
25
NVIDIA Jetson Orin Max is specified for up to 130 TOPS (compute for edge AI)
26
Intel OpenVINO supports running deep learning models at the edge (for cleaning robots)
27
OpenVINO toolkit supports multiple frameworks (TensorFlow, PyTorch, and others) per Intel docs
28
Azure IoT Edge enables deploying AI models to edge devices used in smart cleaning assets
29
AWS IoT Greengrass supports local ML inference on devices (edge AI for autonomous cleaning)
30
AWS IoT Core defines MQTT-based device connectivity used for cleaning equipment telemetry
Interpretation
Technology, AI Capabilities & Robotics Interpretation
Taken together, these standards and tools say that secure, truly autonomous smart janitorial cleaning is getting built from three directions at once: cybersecurity and risk management (UL 2900-2-2, UL 3400, NIST AI RMF, IEEE 7000 and 7001, plus IEC 62443 for control security), perception and decision support (Vision OCR and object detection, OpenCV algorithms, Roomba style visual mapping), and practical edge deployment and robot compute (Jetson Orin, OpenVINO, Azure IoT Edge, AWS IoT Greengrass, and MQTT from AWS IoT Core), all so that disinfecting cycles like UV-C can be automated and validated without turning “smart” into “recklessly connected.”
More related reading
04 · Category
Workforce, Cost & Operational Impact30 stats
01
US Bureau of Labor Statistics (BLS) states that janitors and cleaners had a median pay of $35,360in 2023, indicating labor cost pressure that motivates AI-enabled automation
02
BLS reports janitors and cleaners employment was 2,703,360 in May 2023
03
BLS reports “Janitors and Cleaners, Except Maids and Housekeeping Cleaners” median wage $16.88per hour in 2023
04
BLS reports “Maids and Housekeeping Cleaners” employment 1,122,320 in 2023
05
BLS reports “Maids and Housekeeping Cleaners” median wage $15.77per hour in 2023
06
BLS reports “Janitors and Cleaners” job outlook growth 6% from 2022 to 2032, affecting staffing demand
07
BLS reports “Maids and Housekeeping Cleaners” job outlook growth 10% from 2022 to 2032
08
IHL Group reports that labor is the largest share of cost in facility services budgets at ~50%, motivating productivity tools
09
ISSA’s research indicates cleaning is labor-intensive and staffing is a major driver of costs (quantification in report)
10
McKinsey estimates AI could automate work equivalent to 60–70% of workers’ time (productivity pressure)
11
McKinsey estimates genAI could add $2.6to $4.4 trillion annually to the global economy (benefit pool for ops)
12
McKinsey estimates genAI adoption could deliver $200to $340 billion in productivity value to US healthcare and social assistance (adjacent for healthcare cleaning)
13
McKinsey reports genAI could add $0.1to $0.4 trillion to productivity in US retail (adjacent for retail cleaning)
14
Microsoft report finds organizations using AI saw productivity gains of 20-30% in certain workflows (general)
15
A Gartner forecast indicates that AI automation can reduce operational costs by 30% in some sectors (general productivity)
16
A Forrester report says AI can reduce labor-intensive tasks by 60% (general)
17
Reducing rework in facility cleaning improves compliance; 1 in 5 facilities report rework due to inspection misses (general quality)
18
OSHA reports injuries and illness in the janitorial field are significant; cleaning is hazardous—(use OSHA BLS data)
19
OSHA reports that slips, trips, and falls are a leading cause of workplace injuries (relevant to floor cleaning)
20
NIOSH says falls are the leading cause of workplace injury (general)
21
CDC states that cleaning and disinfecting reduces healthcare-associated infections
22
CDC notes that environmental cleaning is part of infection prevention strategies with measurable impact
23
WHO states adherence to infection prevention and control prevents HAIs (impact driver)
24
BLS reports workplace injuries in “custodians and cleaners” are a major subset; use BLS Census of Fatal Occupational Injuries (CFOI)
25
Forrester (employee productivity) indicates AI can save 50-75% time in some knowledge tasks
26
UiPath states automation via AI reduces average handle time by 30% for some processes (general)
27
A Tech-enabled cleaning ROI report indicates reduction in man-hours per job by up to 25% (vendor)
28
A report on automated floor scrubbing claims up to 40% time savings (vendor)
29
iRobot documents that Roomba models can reduce manual vacuuming time (vendor marketing) by up to 75% in some households
30
UV-C robot vendors state cycles can complete room disinfection in minutes; example cycle duration 12 minutes (vendor spec)
Interpretation
Workforce, Cost & Operational Impact Interpretation
With janitors and cleaners earning modest median wages and comprising a workforce larger than most people realize, the BLS outlook plus the facility-services reality that labor runs up to about half of operating costs, injuries, and infection-control pressure has turned “cleaning smarter” into a serious business case, since McKinsey and others project AI can automate a majority of work time and deliver huge productivity gains, making automation, robotics, route optimization, and smarter dosing appealing not just for efficiency but for safer, more compliant outcomes.
05 · Category
Risk, Compliance & Privacy30 stats
01
NIST AI RMF states it is designed to manage risks across AI systems
02
NIST AI RMF emphasizes organizational oversight (“Govern” function)
03
GDPR requires personal data processing has a lawful basis (Article 6)
04
GDPR Article 5 establishes principles including data minimization and purpose limitation
05
California Consumer Privacy Act (CCPA) defines “personal information” broadly (Section 1798.140)
06
CCPA provides consumers right to request deletion (Section 1798.105)
07
HIPAA’s Privacy Rule protects individually identifiable health information (relevant for healthcare cleaning data)
08
HIPAA Security Rule requires safeguards for electronic protected health information (ePHI)
09
FTC’s “Keep your eyes on privacy” guidance includes requirements for data minimization and security
10
NIST Privacy Framework includes 7 categories (e.g., manage privacy risks)
11
NIST Privacy Framework states it helps organizations manage privacy risks
12
ISO/IEC 27001:2022 is a standard for information security management systems (security for connected cleaning systems)
13
ISO/IEC 27701 extends ISO 27001 for privacy information management (privacy compliance)
14
ISO 31000 is a standard for risk management principles and guidelines (risk governance)
15
The US FTC Safeguards Rule requires certain entities to develop, implement, and maintain a comprehensive information security program (CIP)
16
The US GLBA requires financial institutions to protect customer information
17
The US COPPA rule applies to children’s data and includes parental consent requirements
18
The EU AI Act sets risk classifications (unacceptable/high limited/minimal) impacting AI governance
19
EU AI Act includes prohibited practices for AI used in certain ways
20
EU AI Act mandates transparency for certain “high-risk” AI systems
21
EU AI Act requires CE marking for certain high-risk systems
22
NIST provides guidance for “Adversarial Machine Learning” concepts in the AI context (security risk)
23
NIST AI RMF’s “Measure” function includes evaluating model performance metrics
24
NIST AI RMF’s “Map” function includes identifying intended uses and characteristics
25
NIST AI RMF’s “Govern” function includes establishing AI policy for governance
26
ANSI/ASIS PSC.1 is an active standard for security risk management and could guide security programs for facilities deploying connected assets
27
The U.S. National Cybersecurity Strategy states “improve detection and response capabilities” (security driver)
28
The White House National Cybersecurity Strategy states a goal to reduce cybercrime costs (context)
29
The NIST Cybersecurity Framework Version 2.0 was released (version number)
30
NIST Cybersecurity Framework includes functions Identify, Protect, Detect, Respond, Recover (5 functions)
Interpretation
Risk, Compliance & Privacy Interpretation
The takeaway is that in a janitorial setting where “smart” machines and data may be everywhere, regulators from NIST and GDPR to HIPAA, the FTC, and the EU AI Act all push the same disciplined idea: govern and measure AI and connected systems responsibly, minimize and secure personal or health data with lawful purpose, and manage cyber and privacy risks so detection, transparency, and safeguards are not optional extras.
Reference
Cite This Report
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APA
Sophie Moreland. (2026, February 13). AI In The Janitorial Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-janitorial-industry-statistics
MLA
Sophie Moreland. "AI In The Janitorial Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-janitorial-industry-statistics.
Chicago
Sophie Moreland. 2026. "AI In The Janitorial Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-janitorial-industry-statistics.
Sources & references
104 datasets cited across this report · attribution is report-level
docs.aws.amazon.com+44 additional datasets cited (not shown individually)