Close Menu
  • Home
  • AI Models
    • DeepSeek
    • xAI
    • OpenAI
    • Meta AI Llama
    • Google DeepMind
    • Amazon AWS AI
    • Microsoft AI
    • Anthropic (Claude)
    • NVIDIA AI
    • IBM WatsonX Granite 3.1
    • Adobe Sensi
    • Hugging Face
    • Alibaba Cloud (Qwen)
    • Baidu (ERNIE)
    • C3 AI
    • DataRobot
    • Mistral AI
    • Moonshot AI (Kimi)
    • Google Gemma
    • xAI
    • Stability AI
    • H20.ai
  • AI Research
    • Allen Institue for AI
    • arXiv AI
    • Berkeley AI Research
    • CMU AI
    • Google Research
    • Microsoft Research
    • Meta AI Research
    • OpenAI Research
    • Stanford HAI
    • MIT CSAIL
    • Harvard AI
  • AI Funding & Startups
    • AI Funding Database
    • CBInsights AI
    • Crunchbase AI
    • Data Robot Blog
    • TechCrunch AI
    • VentureBeat AI
    • The Information AI
    • Sifted AI
    • WIRED AI
    • Fortune AI
    • PitchBook
    • TechRepublic
    • SiliconANGLE – Big Data
    • MIT News
    • Data Robot Blog
  • Expert Insights & Videos
    • Google DeepMind
    • Lex Fridman
    • Matt Wolfe AI
    • Yannic Kilcher
    • Two Minute Papers
    • AI Explained
    • TheAIEdge
    • Matt Wolfe AI
    • The TechLead
    • Andrew Ng
    • OpenAI
  • Expert Blogs
    • François Chollet
    • Gary Marcus
    • IBM
    • Jack Clark
    • Jeremy Howard
    • Melanie Mitchell
    • Andrew Ng
    • Andrej Karpathy
    • Sebastian Ruder
    • Rachel Thomas
    • IBM
  • AI Policy & Ethics
    • ACLU AI
    • AI Now Institute
    • Center for AI Safety
    • EFF AI
    • European Commission AI
    • Partnership on AI
    • Stanford HAI Policy
    • Mozilla Foundation AI
    • Future of Life Institute
    • Center for AI Safety
    • World Economic Forum AI
  • AI Tools & Product Releases
    • AI Assistants
    • AI for Recruitment
    • AI Search
    • Coding Assistants
    • Customer Service AI
    • Image Generation
    • Video Generation
    • Writing Tools
    • AI for Recruitment
    • Voice/Audio Generation
  • Industry Applications
    • Finance AI
    • Healthcare AI
    • Legal AI
    • Manufacturing AI
    • Media & Entertainment
    • Transportation AI
    • Education AI
    • Retail AI
    • Agriculture AI
    • Energy AI
  • AI Art & Entertainment
    • AI Art News Blog
    • Artvy Blog » AI Art Blog
    • Weird Wonderful AI Art Blog
    • The Chainsaw » AI Art
    • Artvy Blog » AI Art Blog
What's Hot

Paper page – Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs

Deepseek R1-0528: German Firm Releases Version of DeepSeek’s AI Model That Runs Twice as Fast

Google faces EU antitrust complaint over AI Overviews

Facebook X (Twitter) Instagram
Advanced AI News
  • Home
  • AI Models
    • Amazon (Titan)
    • Anthropic (Claude 3)
    • Cohere (Command R)
    • Google DeepMind (Gemini)
    • IBM (Watsonx)
    • Inflection AI (Pi)
    • Meta (LLaMA)
    • OpenAI (GPT-4 / GPT-4o)
    • Reka AI
    • xAI (Grok)
    • Adobe Sensi
    • Aleph Alpha
    • Alibaba Cloud (Qwen)
    • Apple Core ML
    • Baidu (ERNIE)
    • ByteDance Doubao
    • C3 AI
    • DataRobot
    • DeepSeek
  • AI Research & Breakthroughs
    • Allen Institue for AI
    • arXiv AI
    • Berkeley AI Research
    • CMU AI
    • Google Research
    • Meta AI Research
    • Microsoft Research
    • OpenAI Research
    • Stanford HAI
    • MIT CSAIL
    • Harvard AI
  • AI Funding & Startups
    • AI Funding Database
    • CBInsights AI
    • Crunchbase AI
    • Data Robot Blog
    • TechCrunch AI
    • VentureBeat AI
    • The Information AI
    • Sifted AI
    • WIRED AI
    • Fortune AI
    • PitchBook
    • TechRepublic
    • SiliconANGLE – Big Data
    • MIT News
    • Data Robot Blog
  • Expert Insights & Videos
    • Google DeepMind
    • Lex Fridman
    • Meta AI Llama
    • Yannic Kilcher
    • Two Minute Papers
    • AI Explained
    • TheAIEdge
    • Matt Wolfe AI
    • The TechLead
    • Andrew Ng
    • OpenAI
  • Expert Blogs
    • François Chollet
    • Gary Marcus
    • IBM
    • Jack Clark
    • Jeremy Howard
    • Melanie Mitchell
    • Andrew Ng
    • Andrej Karpathy
    • Sebastian Ruder
    • Rachel Thomas
    • IBM
  • AI Policy & Ethics
    • ACLU AI
    • AI Now Institute
    • Center for AI Safety
    • EFF AI
    • European Commission AI
    • Partnership on AI
    • Stanford HAI Policy
    • Mozilla Foundation AI
    • Future of Life Institute
    • Center for AI Safety
    • World Economic Forum AI
  • AI Tools & Product Releases
    • AI Assistants
    • AI for Recruitment
    • AI Search
    • Coding Assistants
    • Customer Service AI
    • Image Generation
    • Video Generation
    • Writing Tools
    • AI for Recruitment
    • Voice/Audio Generation
  • Industry Applications
    • Education AI
    • Energy AI
    • Finance AI
    • Healthcare AI
    • Legal AI
    • Media & Entertainment
    • Transportation AI
    • Manufacturing AI
    • Retail AI
    • Agriculture AI
  • AI Art & Entertainment
    • AI Art News Blog
    • Artvy Blog » AI Art Blog
    • Weird Wonderful AI Art Blog
    • The Chainsaw » AI Art
    • Artvy Blog » AI Art Blog
Facebook X (Twitter) Instagram
Advanced AI News
Customer Service AI

Study Finds AI in Customer Service Creates More Problems Than It Solves

Advanced AI EditorBy Advanced AI EditorJuly 3, 2025No Comments5 Mins Read
Share Facebook Twitter Pinterest Copy Link Telegram LinkedIn Tumblr Email
Share
Facebook Twitter LinkedIn Pinterest Email


The promise of hyper-efficient, AI-powered customer service is facing a significant reality check. Despite heavy investment from tech giants like Amazon and Salesforce, new research from July 2025 reveals that AI assistants in call centers often create more work for their human counterparts.

This finding is bolstered by a recent Gartner forecast scaling back predictions of AI replacing human agents. High-profile failures, such as a support bot inventing company policy, underscore the growing gap between the technology’s marketing and its real-world performance.

These flawed systems can burden employees, damage trust, and alienate the very customers they are meant to serve. The core issue is the disconnect between promised automation and the reality of human oversight still being essential.

AI’s Efficiency Promise vs. The Messy Reality

A recent academic study highlights this gap. Research accepted to the ACM CSCW 2025 conference examined AI assistants at a Chinese utility’s call center. The findings were stark. The AI struggled with basic tasks like transcribing accents and numbers accurately.

Specific failures included misinterpreting homophones, breaking up phone numbers into useless fragments, and failing to understand callers with strong regional accents. The AI’s emotion-recognition system was also found to be unreliable, often misclassifying normal speech as negative.

This forced human agents into a new role: constant fact-checkers. They had to manually correct inaccurate summaries and delete redundant text. The study’s authors noted this created new “learning” and “compliance” burdens for the customer service representatives (CSRs).

The researchers concluded that “The mismatch between technological expectations and actual implementation reflects a common oversight among technology designers, who overestimate efficiency gains while underestimating the implicit learning burdens of adapting to new systems.” This hidden labor directly contradicts the efficiency gains that are often the primary justification for adopting such expensive AI systems in the first place.

When Good Bots Go Bad: The High Cost of Hallucinations

The risks extend beyond inefficiency and into brand damage. In April 2025, AI code editor company Cursor experienced this firsthand when its own support bot “hallucinated” a fake policy. The bot incorrectly told users their subscriptions were limited to a single device.

The false information spread rapidly, causing user backlash. Cursor co-founder Michael Truell had to issue a public apology on platforms like Hacker News, confirming, “We have no such policy.” He attributed the error to their “front-line AI support bot.”

To compound the issue, a genuine technical bug related to session security was causing separate login problems, creating a perfect storm of user frustration. The bot’s confident but false answers only amplified the chaos.

The incident serves as a cautionary tale. Simply labeling AI responses is not enough to let users know that a response was generated by AI is an inadequate measure to recover user loyalty.

From Replacement to Reinforcement: A Shifting Industry Consensus

This blend of academic findings and public failures is fueling a broader shift in industry thinking. In June 2025, analyst firm Gartner made a significant revision to a previous forecast. It now predicts that by 2027, half of all organizations that expected to replace support staff with AI will abandon those plans.

The new consensus points toward a hybrid model. Kathy Ross, a senior director analyst at Gartner, argues that “A hybrid approach, where AI and human agents work in tandem, is the most effective strategy for delivering exceptional customer experiences.” This approach leverages AI for what it does well—data retrieval and routine tasks—while preserving the essential human element for complex or sensitive interactions.

This pivot acknowledges that the initial vision of fully autonomous, human-free customer service was premature. The focus is now on creating tools that augment human agents, making them more effective rather than making them obsolete.

Big Tech’s Unabated Push into AI Agents

Despite these challenges, major technology firms are not slowing down. They continue to market and develop sophisticated AI agent platforms, framing them as the future of digital labor. Salesforce has been particularly aggressive with its Agentforce platform.

In May 2025, the company launched Agentforce for HR Service, designed to automate internal employee support. This followed the general availability of the core Agentforce platform in late 2024.

Salesforce’s Kishan Chetan, EVP & GM of Service Cloud, claimed, “Salesforce is leading this digital labor revolution across industries, and now, we’re bringing Agentforce skills and actions to HR Service so companies can expedite requests and scale employee support when resources are limited.” The company even touts a 96% self-service resolution rate from its own internal deployment, positioning itself as a primary user of its own technology.

Amazon is also a key player, having enhanced its Amazon Connect platform with new generative AI features. Meanwhile, well-funded startups like Decagon, which raised $35 million in June 2024, are also vying for a piece of the growing market. The central conflict remains unresolved: a market flush with investment and ambition, yet a technology still struggling with fundamental reliability.



Source link

Follow on Google News Follow on Flipboard
Share. Facebook Twitter Pinterest LinkedIn Tumblr Email Copy Link
Previous ArticlexAI data center gets air permit to run 15 turbines, but imaging shows 24 on site
Next Article Deep Learning Program Learns to Paint | Two Minute Papers #49
Advanced AI Editor
  • Website

Related Posts

Customer support automation startup Wonderful raises $34M

July 4, 2025

AI assistants create more work, frustrate customer service reps: Study

July 4, 2025

Call center workers say their AI assistants create more problems than they solve

July 3, 2025
Leave A Reply Cancel Reply

Latest Posts

Albright College is Selling Its Art Collection to Balance Its Books

Big Three Auction Houses Hold Old Masters Sales in London This Week

MFA Boston Returns Two Works to Kingdom of Benin

Tate’s £150M Endowment Campaign May Include Turbine Hall Naming Rights

Latest Posts

Paper page – Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs

July 5, 2025

Deepseek R1-0528: German Firm Releases Version of DeepSeek’s AI Model That Runs Twice as Fast

July 5, 2025

Google faces EU antitrust complaint over AI Overviews

July 5, 2025

Subscribe to News

Subscribe to our newsletter and never miss our latest news

Subscribe my Newsletter for New Posts & tips Let's stay updated!

Recent Posts

  • Paper page – Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs
  • Deepseek R1-0528: German Firm Releases Version of DeepSeek’s AI Model That Runs Twice as Fast
  • Google faces EU antitrust complaint over AI Overviews
  • Automatic Parameter Control for Metropolis Light Transport | Two Minute Papers #30
  • Paper page – Energy-Based Transformers are Scalable Learners and Thinkers

Recent Comments

No comments to show.

Welcome to Advanced AI News—your ultimate destination for the latest advancements, insights, and breakthroughs in artificial intelligence.

At Advanced AI News, we are passionate about keeping you informed on the cutting edge of AI technology, from groundbreaking research to emerging startups, expert insights, and real-world applications. Our mission is to deliver high-quality, up-to-date, and insightful content that empowers AI enthusiasts, professionals, and businesses to stay ahead in this fast-evolving field.

Subscribe to Updates

Subscribe to our newsletter and never miss our latest news

Subscribe my Newsletter for New Posts & tips Let's stay updated!

YouTube LinkedIn
  • Home
  • About Us
  • Advertise With Us
  • Contact Us
  • DMCA
  • Privacy Policy
  • Terms & Conditions
© 2025 advancedainews. Designed by advancedainews.

Type above and press Enter to search. Press Esc to cancel.