AI Anti-Glossary for Customer Experience, Support & User-Centric Automations

    Anti-Glossary of AI in CX, Support & Automations

    Published by Insightarc

    This glossary is for CX leaders, product managers, and AI practitioners who’ve had enough of vague buzzwords and overhyped solutions. Behind every pitch deck and demo lies a reality often more frustrating than futuristic. This Anti-Glossary breaks down the industry's favorite AI terms — not by the book, but by how they actually show up in the wild.

    A

    1. Abandoned Cart Flow — A pretext for spamming users with “Hey, forgot something?” emails.
    2. A/B Testing — Making design tweaks and calling it a breakthrough.
    3. Active On Site — A user opened the site. Probably just to close the popup.
    4. API — That magical “should only take 5 minutes” black box.
    5. AI Agent — A chatbot with a superiority complex and no supervision.
    6. AI Email Automation — Bulk emails with “Hi [FirstName]” to fake personalization.
    7. AI Orchestration — Multiple confused models making noise in sync.
    8. AI Routing — Machine-learning-powered misdirection.
    9. Alphanumeric Sender ID — Your SMS is now from “BRNDTXT.” So personal.
    10. Anonymized Data — Allegedly private data until SQL joins say otherwise.
    11. Average Order Value (AOV) — A KPI we quote when we have nothing else to say.

    B

    1. Back-populate — Retroactively annoying people with emails they never asked for.
    2. Behavioral Analytics — Turning rage clicks into “valuable insights.”
    3. Behavioral Graph — A chaotic mess of actions mislabeled as "journey."
    4. Best People — What we call anyone who clicked twice.
    5. Browse Abandonment Flow — Our way of saying “We saw you looking. 👀”

    C

    1. Campaign — A glorified one-time email blast.
    2. Call to Action (CTA) — The button we pray fixes your funnel.
    3. Capture Rate — How many users we pestered into giving up their email.
    4. Cart Abandonment Rate — Guilt-tripping users with percentages.
    5. Chat Personalization — Using your name, ignoring your problem.
    6. Click Through Rate (CTR) — We keep refreshing, hoping it moves.
    7. Context Injection — Dumping stale session data into prompts.
    8. Contextual Targeting — Guessing what you want from a single blog visit.
    9. Conversational AI — A chatbot that still can’t cancel your subscription.
    10. Customer Digital Twin — A clone of you that gets everything wrong.
    11. Customer Experience (CX) — Ruined one bot at a time.
    12. Customer Intent Mining — Predicting what you’ll do next, badly.
    13. Customer Journey Graph — A maze map showing how lost users get.
    14. Customer Lifetime Value (CLV) — How much you’ll spend before rage-quitting.
    15. Customer Success — The team explaining why nothing works.

    D

    1. Decisioning — AI logic that insists you need another upsell.
    2. Deep Learning — An expensive way to misunderstand your users.

    E

    1. Embeddings — Mathifying your words into vector soup.

    F

    1. Fine-tuning — Teaching AI niche content so it can hallucinate with confidence.

    G

    1. Generative AI — Plausible nonsense, on demand.

    H

    1. Hallucination — Confidently wrong AI outputs that sound just right.
    2. Hyper-Personalization — Seeing the same ad everywhere, forever.

    I

    1. Inference — When the model “thinks” based on vibes.
    2. Intent Recognition — Guessing what “cancel” means in 5 tries or less.
    3. Intent Resolution — Linking your problem to the least helpful FAQ.
    4. Intents — Categories AI made up to feel like it understands you.

    L

    1. Large Language Models (LLMs) — Predictive text engines with a God complex.
    2. LLM Context — Whatever random text we shove into the prompt to sound smarter.

    M

    1. Machine Learning (ML) — Guesswork, automated.
    2. Multimodal AI — Misunderstanding your texts and your drawings.

    N

    1. Natural Language Processing (NLP) — Automating miscommunication at scale.
    2. Natural Language Understanding (NLU) — Slightly less confusion, slightly more cost.
    3. Natural Language Generation (NLG) — Auto-generated content that collapses on close reading.

    P

    1. Personalization Layer — Your name + “You might like this.”
    2. Predictive Analytics — The art of pretending we saw it coming.
    3. Privacy-first AI — We respect your privacy. While tracking everything anonymously.
    4. Prompt Engineering — Politely begging the AI to behave.

    R

    1. RAG (Retrieval-Augmented Generation) — Letting AI Google before it hallucinates.
    2. Real-Time User Context — Your last 30 seconds, repackaged as “insight.”
    3. Reasoning — When AI adds extra steps to be wrong.
    4. Retrospective User Context — Yesterday’s behavior, solving tomorrow’s churn.
    5. Retrieval — Confidently pulling the least relevant document.

    T

    1. Tokens — Text shards that let AI and billing systems talk.
    2. Transformer — The tech behind LLMs. Not a robot. Sadly.

    U

    1. User Behavioral Context — Every click, forever logged, to maybe show a banner.
    2. User Context — Anything we can scrape and pretend is meaningful.

    V

    1. Vector Databases — Where ideas go to get fuzzy-matched with wrong ones.
    2. Virtual Agent — Infinite confidence, zero real-world awareness.
    3. Voicebot — Alexa’s confused cousin in customer support.

    W

    1. Webhooks — 3 a.m. alerts to devs when someone sneezes on the site.

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