If AI chatbots are the future, I hate it
AI-powered customer support is drawing criticism as many users report that chatbots add friction without resolving real problems, especially in complex cases like ISP outages or billing glitches. Commenters argue that what’s often labeled “AI” is still just rigid decision trees, and that even true LLM-based systems are frequently deployed as cost-cutting shields rather than genuine helpers. Others see potential for well-designed hybrid models, where bots handle routine queries, quickly recognize their limits, and escalate to empowered humans—provided companies are actually willing to invest in service rather than merely minimize expense.
Nature of the Problem Discussed
- The concrete case involved an ISP “chatbot” that was really a rigid decision tree, not an LLM.
- It repeatedly forced Wi‑Fi troubleshooting despite clear statements about a wired, line-level speed drop.
- After escalation, the human agent also followed a script and missed provided data.
- A later onsite visit revealed a backend billing / provisioning downgrade to a 6 Mbps plan, likely due to messy legacy systems.
Are These Actually “AI” Chatbots?
- Many argue the example is misclassified: it’s keyword + dialogue-tree logic, common for years, not modern generative AI.
- Some say calling it “AI” is misleading and clickbaity; others note that expert systems and decision trees have long been labeled “AI” in industry.
Experiences with Chatbots vs Humans
- Widespread frustration: bots waste time, block humans, repeat irrelevant scripts, and often don’t pass prior context to agents.
- Others report positive cases: e.g., broker chatbots changing investment settings, Amazon-style bots that gather context well, an ISP with a transparent scripted flow, a Carvana bot solving a complex title issue.
- Some users actively prefer machines to avoid exhausting human calls and long hold times.
Economics and Incentives
- Many see customer support at large ISPs and platforms as a pure cost center, especially where there’s little competition; the goal is to be just good enough not to lose regulators or customers.
- Debate over whether high prices imply room for better service: some cite thin margins and scale; others point to monopoly behavior and cost-cutting, not necessity.
- Several argue most traffic is basic (“what’s my balance?”, bill pay, password reset), making automation attractive.
Design Ideas and Future of Support
- Suggested improvements:
- Clear “talk to a human” escape.
- Technical “shibboleth” or quiz to fast‑track advanced users.
- Bots that honestly declare themselves, are optional, escalate quickly, and share all gathered info with agents.
- Better internal documentation and tools for support staff.
- Some foresee LLM-based agents that know account history, understand natural language well, and hand off smoothly, eventually surpassing today’s human-first model.
- Others doubt incentives will ever align to deliver that best-case hybrid, predicting further degradation before any improvement.