AI agents are reshaping e-commerce as AI spending grows—generative AI software is projected to bring $8.0B in 2024. Across customer support, product discovery, and on-site conversion, retailers use chatbots, automated ticket handling, and recommendation engines to lower costs and increase revenue. You’ll also see how mobile-first shopping behavior and consumer chatbot preferences shape adoption, plus the metrics behind these results.
Key Takeaways
- $8.0 billion is projected global revenue for generative AI software in 2024 (and $107.5 billion by 2030)
- Global generative AI services spending is forecast to total $62.5 billion in 2024
- Worldwide AI software revenue is projected to reach $188 billion in 2023 (with continued growth forecast)
- Companies that use AI in customer service report average cost savings of 30% (compared with baseline operations)
- Chatbots can reduce customer support costs by 30% to 70% (range reported by industry research)
- In a large-scale observational study, automated customer support handling led to 9% lower cost per ticket compared with manual routing (study reported by a research summary)
- Recommendation engines can increase revenue by 10%+ (reported typical uplift in industry case studies)
- A/B tests in leading retail contexts frequently show 5%–20% lift in conversion using product recommendations
- 1.6x average increase in average order value when using product recommendations in e-commerce personalization campaigns (benchmark from industry research)
- 56% of consumers said they prefer a chatbot that can answer basic questions instantly (2022 survey result)
- 45% of customer service organizations reported using chatbots in 2023 (survey finding)
- In 2024, 61% of consumers used mobile devices to shop online at least weekly (survey result, 2024)
Ecommerce AI from chatbots to recommendations is boosting revenue and conversions while cutting customer support costs.
Related reading
01 · Category
Market Size7 stats
01
$8.0 billion is projected global revenue for generative AI software in 2024 (and $107.5 billion by 2030)
02
Global generative AI services spending is forecast to total $62.5 billion in 2024
03
Worldwide AI software revenue is projected to reach $188 billion in 2023 (with continued growth forecast)
04
$26.6 billion is the estimated global market size for conversational AI in 2023
05
$3.1 billion is the global market size for chatbots in 2024
06
$6.6 billion global intelligent virtual assistant market size in 2024 is projected to reach $18.9 billion by 2030
07
$23.6 billion global recommendation engine market size in 2023 is projected to reach $75.2 billion by 2030
Interpretation
Market Size Interpretation
From a market size perspective, AI agent related offerings are scaling quickly, with global generative AI services projected to reach $62.5 billion in 2024 and conversational AI estimated at $26.6 billion in 2023.
02 · Category
Cost Analysis3 stats
01
Companies that use AI in customer service report average cost savings of 30% (compared with baseline operations)
02
Chatbots can reduce customer support costs by 30% to 70% (range reported by industry research)
03
In a large-scale observational study, automated customer support handling led to 9% lower cost per ticket compared with manual routing (study reported by a research summary)
Interpretation
Cost Analysis Interpretation
For the cost analysis angle, AI agents are consistently cutting ecommerce customer service expenses by roughly 30% on average, with chatbots sometimes delivering 30% to 70% reductions and automated support lowering cost per ticket by 9% versus manual routing.
More related reading
03 · Category
Performance Metrics8 stats
01
Recommendation engines can increase revenue by 10%+ (reported typical uplift in industry case studies)
02
A/B tests in leading retail contexts frequently show 5%–20% lift in conversion using product recommendations
03
1.6x average increase in average order value when using product recommendations in e-commerce personalization campaigns (benchmark from industry research)
04
A 2024 experiment found that adding a chatbot to a retail website increased conversion by 11% (study result reported by the publisher)
05
In a field study, AI-generated product recommendations increased click-through rate (CTR) by 16% versus a non-personalized baseline
06
In a peer-reviewed study of conversational recommender systems, the proposed approach improved ranking metrics (NDCG) by 0.07 absolute points over the baseline model
07
Retailers using AI-driven pricing and promotions reported average revenue lift of 3% in 2023 (survey result by a retail analytics publication)
08
In 2024, the average retail chatbot resolution rate target is 30–50% for Tier-1 intents (range reported in customer support AI implementation benchmarks)
Interpretation
Performance Metrics Interpretation
Across performance metrics for e-commerce AI agents, personalization is consistently driving measurable gains, such as 10% or more revenue uplift from recommendation engines and frequent conversion lifts ranging from 5% to 20%, with additional wins like an 11% boost from chatbots and a 16% increase in click-through rate from AI-generated recommendations.
04 · Category
User Adoption4 stats
01
56% of consumers said they prefer a chatbot that can answer basic questions instantly (2022 survey result)
02
45% of customer service organizations reported using chatbots in 2023 (survey finding)
03
In 2024, 61% of consumers used mobile devices to shop online at least weekly (survey result, 2024)
04
In 2023, the EU had 92% smartphone penetration among individuals aged 16–74 (Eurostat ICT usage indicator)
Interpretation
User Adoption Interpretation
User adoption of AI agents in ecommerce is being driven by the expectation of instant, conversational help and by the shift to mobile shopping, since 56% of consumers prefer chatbots for basic questions, 45% of customer service organizations already use chatbots in 2023, and 61% of consumers shop on mobile at least weekly in 2024.
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
Thomas Lindqvist. (2026, February 13). AI Agents Ecommerce Industry Statistics. Gitnux. https://gitnux.org/ai-agents-ecommerce-industry-statistics
MLA
Thomas Lindqvist. "AI Agents Ecommerce Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-agents-ecommerce-industry-statistics.
Chicago
Thomas Lindqvist. 2026. "AI Agents Ecommerce Industry Statistics." Gitnux. https://gitnux.org/ai-agents-ecommerce-industry-statistics.
Sources & references
22 datasets cited across this report · attribution is report-level
+5 additional datasets cited (not shown individually)