Customer Trust | ICX Blog

Illustration of a glasswing butterfly with transparent wings over a dark grid, evoking the Project Glasswing security theme

Industry NewsMay 20, 20267 min read

Project Glasswing Is a Cybersecurity Story. A Conversation Designer Reads It Differently.

Project Glasswing is a new Anthropic-led effort, with partners like Amazon, Google, Microsoft, and JPMorganChase, to secure critical software using Claude Mythos Preview, a model that finds software flaws better than almost any human. Most coverage will focus on the hacking. This opinion piece reads it through a conversation designer's eyes: the names, the framing, and what it means for any business whose AI talks to customers.

Designing an AI persona that builds customer trust in a chatbot

Conversational AIApril 25, 202610 min read

How to Design an AI Persona That Builds Customer Trust

AI persona design is the practice of giving a chatbot a coherent character (name, voice, behavior, emotional range) that builds customer trust. A real persona is more than an avatar and a name. It is four elements working together: identity, voice, behavior, and emotional range, encoded into the system prompt and tested before launch.

Why AI chatbots often get politeness and tone wrong

Conversational AIApril 23, 20269 min read

The Science of Why AI Gets Politeness Wrong in Chatbots

AI chatbots routinely get politeness wrong. Too polite feels fake and slow. Too direct feels rude and untrustworthy. The fix comes from Brown and Levinson's politeness theory: every conversation involves face-threatening acts (asking for information, refusing, correcting), and good design calibrates politeness to the stakes of each act. Most chatbots hedge too much on low-stakes turns and not enough on high-stakes ones.

Choosing which customer interactions to automate and which to keep human

CX StrategyApril 16, 202610 min read

The "Automate Everything" Trap and Which Interactions Should Stay Human

The automate-everything trap is the assumption that the goal of AI in customer service is maximum automation. The opposite is true. Five types of interaction should stay human: high-emotion conversations, high-stakes decisions, true edge cases, complaint resolution, and loyalty-moment interactions. AI should handle volume so humans can focus on these five.

Auditing an AI customer experience in a focused 30-minute review

GuidesApril 13, 20269 min read

How to Audit Your AI Customer Experience in 30 Minutes

The 30-minute AI CX audit is a five-dimension framework that finds what is actually broken in a chatbot. Spend 5 minutes on setup. 15 minutes having real conversations as if you were a customer. 10 minutes scoring the five dimensions: language quality, error handling, escalation design, trust signals, and resolution. Dashboards miss most of what this audit catches.

Designing what an AI assistant says when it cannot help with a request

GuidesApril 12, 20269 min read

What Happens When AI Says "I Can't Help With That"

'I'm sorry, I'm not able to help with that' is the most common AI response to a limit and the most damaging to customer trust. The problem is not the limit. It is what happens next. Good failure messages acknowledge what the AI understood, name the limit clearly, and offer two next steps. The same limit can be a dead end or a door depending on the words around it.

A person pausing at a laptop, representing fallback flows that recover a stalled conversation

Conversational AIApril 9, 20267 min read

How to Design Conversational AI Fallback Flows

A fallback flow is the design layer in a chatbot or voice agent that handles everything outside the happy path: ambiguous input, out-of-scope requests, low-confidence intents, and conversation breakdowns. Three core triggers fire fallback: low intent confidence, missing required data, and explicit user frustration. Well-designed fallback flows are the difference between trust and abandonment.

The hidden cost behind good-enough AI chatbot projects that get abandoned

CX StrategyApril 9, 20268 min read

Why 60% of "Good Enough" AI Chatbot Projects Get Abandoned

Around 60 percent of chatbot projects quietly get shelved within their first year. The cost is not just the wasted build budget. The real ongoing cost shows up in repeat contacts (customers calling back the same day), CSAT damage that lingers for quarters, and the rebuild burden when leadership wants to try AI again. 'Good enough' AI is the most expensive AI.

The career shift from copywriter to AI content designer explored in this article

Industry TrendsApril 2, 20268 min read

The Skills Shift From Copywriter to AI Content Designer

Writers have most of the foundation for AI content design: voice work, audience understanding, and editorial judgment. The skills shift is from single-piece thinking to system-level thinking. AI content designers build prompts that work across thousands of interactions, write evaluation criteria, and treat language as a versioned system. Three habits help: thinking in patterns, writing for the model, and shipping evaluation.

A content design system as the foundation for reliable AI output, the subject of this article

CX StrategyApril 1, 20268 min read

Your AI Does Not Need Better Models. It Needs a Content Design System.

Most AI chatbots fail because of missing language standards, not bad models. A content design system is a documented set of language rules that the AI follows across every interaction. It has five layers: voice (who the AI is), vocabulary (which words it uses), structure (how it organizes information), behavior (what it does), and refusal (how it says no). Build the system once; reuse it across every channel.

A winding road through a landscape, representing designing for the moments when AI gets it wrong

Conversational AIApril 1, 20269 min read

A Conversation Designer's Guide to the Moment AI Gets It Wrong

Every AI chatbot will get something wrong. The real question is what happens next. Three failure types need explicit design: misunderstanding (the AI parsed the input wrong), low confidence (the AI is not sure of the answer), and out-of-scope (the AI cannot help with this kind of request at all). Each failure type has a different recovery pattern. Trust depends on getting these right.

Two people reviewing a plan on a laptop in a meeting, representing making the business case for conversation design

CX StrategyApril 1, 20267 min read

"But ChatGPT Can Already Do This." How to Make the Case for Conversation Design.

When a leader asks why you need conversation design when ChatGPT can already handle conversations, here is the answer. ChatGPT can hold a conversation. It cannot reliably represent your brand, follow your policies, escalate at the right moment, or handle the customer questions you actually get. Conversation design fills that gap. The 'works' bar and the 'works well' bar are far apart in production.

Articles tagged “Customer Trust”