For enterprise CX teams, AI adoption is no longer a nice-to-have; it’s a competitive necessity.
But navigating the new status quo also means wading through an overwhelming amount of information (and misinformation) about AI’s capabilities.
To ensure a successful adoption, you need to be informed. Here are five common AI support myths, and the real story behind each.
1. “Existing chatbots are good enough.”
Not all AI bots measure up.
You might assume that lower-cost options like legacy chatbot solutions or GenAI wrappers built on models like ChatGPT will do the job. The reality? They won’t work long-term.
Legacy solutions rely on decision trees, which follow a rigid “if-then” structure that only enables bots to give scripted answers to predetermined questions.
While GenAI wrappers built on LLMs are more advanced, their inability to integrate with your core systems means they can’t answer complex questions.
The result in both cases is frustrated customers and more human handoffs.
To deploy AI bots that can answer all your customers’ questions without regular intervention, find a partner who offers no-code customizations and native integrations across your stack (like Salesforce, Zendesk, Snowflake, and Stripe).
2. “AI systems provide unreliable answers.”
Gold-standard GenAI solutions rarely hallucinate.
The tendency to equate AI answers with unreliability isn’t unfounded. Weaker systems are still very hallucination-prone.
This is true of solutions created with RAG models, which retrieve information from both internal and external sources. These systems commonly misinterpret their sources and synthesize them into wildly inaccurate answers.
Commercially built models safeguard against this. They pull answers from multiple internal sources, break data into small, verified chunks of information, and feed them to their LLMs to generate concise, reliable responses.
This is how leading AI vendors like Maven AGI autonomously answer over 5 million tickets at a 93% accuracy rate.
3. “AI will eliminate the need for human support jobs.”
Human CX jobs aren’t disappearing; they’re evolving.
AI’s promise of infinite scale on limited headcount is irresistible. And enterprises across industries are already hiring fewer humans and implementing more AI.
However, even the smartest systems aren't mature enough to operate without human oversight. On its own, AI misses nuance, lacks empathy, and mishandles sensitive data.
Plus, some customers will never be satisfied until a human has jumped on the phone and made it better.
In fact, many companies are still incorporating AI more cautiously and trusting it with less than it’s capable of.
Instead of fretting about what AI could mean for future CX jobs, use it to automate repetitive tasks and make your team’s work easier today.
4. “AI can only answer simple questions.”
The best LLMs get smarter every time you use them. Today, AI agentsbots reset passwords, process refunds, initiate disputes, and resolve increasingly complex requests.
Many teams underestimate these capabilities because LLM technology is evolving at an almost incomprehensible pace.
But the best models create a flywheel effect where every data point they absorb boosts accuracy and equips them to do more complex work.
How?
The LLM reads a user’s request in natural language
It determines which backend APIs are needed to fulfill it
The orchestration layer maps the requests to API calls and executes them
Over time, the LLM learns new patterns and gets better at carrying out complex tasks
Pick a vendor whose AI is built to grow with you.
5. “AI is expensive without offering a clear ROI.”
Ask any CX leader using an enterprise-grade AI solution, and they’ll tell you: AI drives significant cost savings and rapid ROI.
We’ve all heard the horror stories about failed AI experiments. And early on, companies implemented AI too eagerly without concern for its effect on their P&Ls.
It’s not surprising, then, that a hotly debated new MIT report concludes that 95% of organizations have seen zero ROI from the billions they’ve invested in AI.
This report doesn’t reflect the strategic and thoughtful ways people use AI today. Now that we’re in the second phase of adoption, the ROI is following.
CX teams outsourcing support tasks to Maven are already seeing tangible results. Take the fintech Papaya Pay, who now automates 90% of chat inquiries while cutting its cost per ticket in half.
See Real AI in Action With Maven
It’s time to cut through the noise and see what AI can do for your CX team.
Maven optimizes every customer touchpoint and drives impactful business outcomes for its partners. Just ask industry leaders like ClickUp, Tripadvisor, and Thumbtack.
Accelerate your AI journey by booking a demo today.