惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 三生石上(FineUI控件)
月光博客
月光博客
S
SegmentFault 最新的问题
有赞技术团队
有赞技术团队
Stack Overflow Blog
Stack Overflow Blog
Engineering at Meta
Engineering at Meta
T
The Blog of Author Tim Ferriss
The GitHub Blog
The GitHub Blog
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
IT之家
IT之家
宝玉的分享
宝玉的分享
A
About on SuperTechFans
Vercel News
Vercel News
P
Proofpoint News Feed
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 【当耐特】
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
V
Visual Studio Blog
Jina AI
Jina AI
Y
Y Combinator Blog
T
Tailwind CSS Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Last Week in AI
Last Week in AI

Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
AI customer service bots get rolled back at 74% of firms
2026-05-14 · via Hacker News - Newest: "AI"

REG AD

AI + ML

Dissatisfied: Three-fourths of AI customer service rollouts are a letdown

AI rollback rates hit 81% at firms with mature guardrails, suggesting enterprises are struggling to manage the systems in production, says Sinch

If you're thinking you can replace your human call center staff with a server farm of bots, think again. Nearly three-quarters of enterprises that deploy AI customer communications agents later roll them back or shut them down, according to new research suggesting the systems are far harder to manage reliably in production than the AI hype implied.

Swedish comms-as-a-service firm Sinch surveyed more than 2,500 AI decision makers from various countries and industries for its AI Production Paradox study. The starkest finding is undoubtedly the 74 percent rollback or shutdown rate for deployed AI customer communications agents tied to governance failures, but that’s not the only sign enterprise AI deployments are falling short of expectations. 

AI rollback rates, which Sinch told us specifically refer to AI projects that were deployed and pulled from live service rather than projects that failed before launch, actually rise to 81 percent among organizations that it describes as having “fully mature guardrails.” That, says Sinch Chief Product Officer Daniel Morris, suggests governance alone is not fixing the problem. 

REG AD

"The most advanced organizations aren't failing less; they're seeing failures sooner. Higher rollback rates reflect better monitoring and control, not weaker performance," Morris said in a press release. “If governance was the fix, the most mature teams would roll back less, not more. Our data points to a deeper issue.”

REG AD

According to the findings, 84 percent of AI engineering teams are spending at least half their time on safety infrastructure, leaving little time to develop AI. This is exacerbated by the fact that most firms said spending on AI trust, security, and compliance ranks ahead of AI development itself.

“When 75% put trust, security, and compliance in that top three — ahead of AI development itself at 63% — that’s a finding about where the priority sits within their AI customer communications programs,” a Sinch spokesperson told us in an email. In other words, it seems like most organizations realize that their biggest issue with AI isn’t getting it working properly - it’s getting it to just work safely in the first place. 

“The operational cost of running AI safely at scale is much larger than most organizations expect,” the Sinch representative explained.

The numbers don’t change based on organizational size or budget, either, Sinch told us. 

“The rollback rate holds consistently across every region and every industry in the study, which suggests size isn’t a meaningful protective factor,” the company said. “Rollback isn’t a symptom of under-investment or being too small to afford proper guardrails.” 

Of course, as a business communications service provider, Sinch linked its results back to AI customer service agents not being properly deployed on comms infrastructure designed for AI agents, a problem it’s naturally positioned to offer a fix for. 

Regardless, that three-quarter rollback figure doesn’t seem too out of place when you consider recent customer service automation news. 

As we’ve reported on multiple occasions, replacing customer service staff with AI hasn’t gone to plan for many businesses. Gartner said in June 2025 that half of organizations expecting AI to significantly reduce customer service headcount would abandon those plans by 2027. Sinch’s numbers suggest the problem may extend beyond staffing cuts to the AI agents themselves. Not that far-fetched when Gartner was already warning last year that fully agentless contact centers were not practical in the real world.

REG AD

"Our vendor evaluations reveal that a agentless contact center is not yet technically feasible, nor is it operationally desirable," Brian Weber, VP analyst in the Gartner Customer Service & Support practice, told The Register, adding that unexpected costs and unintended results were contributing to abandonment plans - just like what Sinch is reporting now. ®