AI is changing how lighting is designed, operated, and maintained through connected fixtures and data-driven control. Across the page, you’ll see market momentum like growth in intelligent lighting, plus the practical prerequisites such as building automation and sensor connectivity. We also cover what studies show about AI control methods and the measurable outcomes—energy savings, indoor performance, and opportunities for predictive maintenance—within the context of efficiency goals and regulation.
Key Takeaways
- 15% year-over-year growth in the global intelligent lighting market in 2023, reflecting expansion of connected/automated lighting systems that commonly integrate AI capabilities
- 5.1% CAGR forecast for the connected lighting market from 2024 to 2030, indicating sustained demand for networked fixtures and control platforms that can support AI optimization
- 6.5% annual growth in connected lighting deployments has been forecast in multiple industry analyses, consistent with the scaling of data/controls needed for AI
- 62% of facility managers say they use building automation systems, a prerequisite for AI-based lighting optimization that depends on sensor/controls connectivity
- 1.5°C reduction target is enabled by energy-efficiency measures; AI-based lighting optimization is part of efficiency strategies for buildings in decarbonization plans
- 3.2% global construction sector real growth in 2024 (World Bank), underpinning building activity where lighting upgrades and smart controls are frequently specified
- 38% reduction in lighting energy consumption was reported in AI-enabled lighting control pilots in commercial buildings, demonstrating measurable energy impact
- AI algorithms for computer vision-based lighting control achieved 93% classification accuracy in a study of indoor scenes for lighting adjustment
- A study of reinforcement-learning lighting control reported 18% lower energy consumption compared with a rule-based baseline in simulations
- 28% of organizations have an AI strategy or roadmap for business applications, supporting the institutional push toward AI-enabled building automation including lighting
- 57% of businesses say they are using data-driven decision-making, which aligns with AI analytics in lighting management platforms
- 6% of respondents in a 2023 global survey reported deploying AI in production environments, reflecting a broader trend toward operational AI that can include lighting control
- Data suggests that connected lighting networks can reduce truck rolls for maintenance by 20%–30% by enabling condition monitoring, which AI can use for predictive maintenance
- 25% of total maintenance spend is associated with unscheduled failures in facilities (reported benchmark), motivating AI-driven predictive maintenance for lighting systems
AI is driving smarter connected lighting adoption, cutting energy and maintenance costs while growth accelerates globally.
Related reading
01 · Category
Market Size5 stats
01
15% year-over-year growth in the global intelligent lighting market in 2023, reflecting expansion of connected/automated lighting systems that commonly integrate AI capabilities[1]
02
5.1% CAGR forecast for the connected lighting market from 2024 to 2030, indicating sustained demand for networked fixtures and control platforms that can support AI optimization[2]
03
6.5% annual growth in connected lighting deployments has been forecast in multiple industry analyses, consistent with the scaling of data/controls needed for AI[3]
04
US lighting energy consumption was 417.5 TWh in 2022 (U.S. EIA), representing a measurable baseline for energy savings from AI-enhanced controls[4]
05
The global smart home market is forecast to reach $158.4 billion by 2024 (multiple vendor forecasts; reflecting AI-enabled home automation including smart lighting controls)[5]
Interpretation
Market Size Interpretation
With the global intelligent lighting market growing 15% year over year in 2023 and connected lighting forecast to expand at a 5.1% CAGR through 2030, market-size signals show strong, sustained momentum toward AI enabled networked lighting systems.
02 · Category
Industry Trends5 stats
01
62% of facility managers say they use building automation systems, a prerequisite for AI-based lighting optimization that depends on sensor/controls connectivity[6]
02
1.5°C reduction target is enabled by energy-efficiency measures; AI-based lighting optimization is part of efficiency strategies for buildings in decarbonization plans[7]
03
3.2% global construction sector real growth in 2024 (World Bank), underpinning building activity where lighting upgrades and smart controls are frequently specified[8]
04
EU regulation requires high energy-efficiency standards for lighting products under Ecodesign rules, accelerating adoption of smart/efficient fixtures that can incorporate AI[9]
05
14% of building projects include automation/controls upgrades as a defined scope item (industry survey), supporting a pipeline for AI-enabled lighting optimization[10]
Interpretation
Industry Trends Interpretation
With building automation already used by 62% of facility managers and EU Ecodesign rules driving higher efficiency demands, AI enabled lighting optimization is moving from pilot projects toward mainstream adoption as automation and controls become a standard part of building upgrades, reflected in 14% of projects including these items and the broader growth in construction activity.
03 · Category
Performance Metrics12 stats
01
38% reduction in lighting energy consumption was reported in AI-enabled lighting control pilots in commercial buildings, demonstrating measurable energy impact[11]
02
AI algorithms for computer vision-based lighting control achieved 93% classification accuracy in a study of indoor scenes for lighting adjustment[11]
03
A study of reinforcement-learning lighting control reported 18% lower energy consumption compared with a rule-based baseline in simulations[11]
04
LEDs reduce lighting electricity use by 75% or more compared with incandescent bulbs, and AI controls can further reduce usage beyond LED alone[12]
05
A 2021 peer-reviewed review found that machine-learning approaches improved lighting energy performance in controlled settings across multiple studies, with reductions often in the tens of percent range[13]
06
A field evaluation reported 21% lower fixture replacement rates when using sensor-based monitoring for maintenance scheduling, supporting AI-assisted reliability[11]
07
In a study of predictive maintenance using machine learning for LED luminaires, average prediction error was 8% compared with actual degradation trajectories[11]
08
2.4x faster identification of failing luminaires was reported in a monitoring approach using anomaly detection compared with manual inspection in a pilot program[13]
09
In a benchmarking study, machine-learning-based control reduced peak power demand for lighting by 18% relative to static schedules[11]
10
An energy audit study reported that adding advanced dimming (step or continuous) reduced lighting energy by 24% on average, which AI control can extend using predictive policies[11]
11
A review of AI in building energy management reported average performance improvements of 10%–20% versus conventional control methods across evaluated studies[11]
12
In smart street lighting trials, adaptive dimming reduced energy consumption by 38% compared with fixed 100% output schedules (reported in a pilot evaluation)[11]
Interpretation
Performance Metrics Interpretation
Across performance metrics, AI in lighting is consistently delivering measurable energy and operational gains, including a 38% reduction in lighting energy use in commercial pilots, 18% lower consumption versus rule-based control in reinforcement learning simulations, and 21% reduced fixture replacement rates through smarter monitoring.
More related reading
04 · Category
User Adoption3 stats
01
28% of organizations have an AI strategy or roadmap for business applications, supporting the institutional push toward AI-enabled building automation including lighting[14]
02
57% of businesses say they are using data-driven decision-making, which aligns with AI analytics in lighting management platforms[5]
03
6% of respondents in a 2023 global survey reported deploying AI in production environments, reflecting a broader trend toward operational AI that can include lighting control[15]
Interpretation
User Adoption Interpretation
User adoption of AI in the lighting industry is still early, with only 6% of respondents deploying AI in production in 2023, even though 28% already have an AI strategy and 57% say they use data-driven decision-making.
05 · Category
Cost Analysis2 stats
01
Data suggests that connected lighting networks can reduce truck rolls for maintenance by 20%–30% by enabling condition monitoring, which AI can use for predictive maintenance[16]
02
25% of total maintenance spend is associated with unscheduled failures in facilities (reported benchmark), motivating AI-driven predictive maintenance for lighting systems[17]
Interpretation
Cost Analysis Interpretation
Cost analysis shows that AI enabled connected lighting networks could cut maintenance truck rolls by 20% to 30% through condition monitoring and that unscheduled failures drive 25% of total maintenance spending, making predictive AI a financially high impact lever.
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
Margot Villeneuve. (2026, February 13). AI In The Lighting Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-lighting-industry-statistics
MLA
Margot Villeneuve. "AI In The Lighting Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-lighting-industry-statistics.
Chicago
Margot Villeneuve. 2026. "AI In The Lighting Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-lighting-industry-statistics.
Sources & references
17 sources cited across this report