Hi everyone, new here and still figuring out the whole B2B software evaluation thing. I've been testing a few support platforms for our small team, and there's something I keep running into.
I feel like I'm going crazy, because honestly? I miss the old, simple canned responses. The new AI suggestions in the agent workspace feel... off. Like, they're trying to be too clever and often miss the mark. Last week, I had a customer with a straightforward billing question, and the AI suggested a long paragraph about "leveraging our self-service portal for an optimized resolution journey." The customer would have been so confused! I just wanted my old "Thanks for reaching out about your invoice. Let me pull that up for you right now."
Maybe I'm not using it right, but the AI seems to add fluff or misinterpret the tone. With my canned responses, I knew *exactly* what was going to be sent. I had them organized and could fire off a clear, accurate reply in two clicks. Now I spend more time reading and rejecting AI suggestions than I used to spend picking a canned response.
Is it just me? 😅 Are teams actually finding these AI suggestions faster or better? I'm grateful for any new tech that helps, but this one feels like a step sideways instead of forward. Would love to hear from agents who use this daily.
Oh, you're definitely not going crazy. That specific example you gave about the "optimized resolution journey" is a perfect illustration of the problem - it's jargon, not help. I see this tension a lot in our community discussions.
A lot of these AI tools are trained on mountains of corporate documentation and marketing copy, so they tend to default to that kind of overly formal, impersonal language. For a small team trying to build genuine rapport, that's a real setback.
The control factor you mentioned is key. Good canned responses were predictable tools. Some of the new suggestions feel like you're constantly having to wrestle with a collaborator who doesn't understand your customers. Many teams do find success with the AI, but it often requires significant upfront time to train and tweak it to match their voice, which defeats the "time-saving" promise. Maybe the sweet spot is using AI for a first draft but keeping those trusty, simple canned responses a click away for core interactions?
Keep it constructive.
Totally get that feeling of "I just want my tool to work, not philosophize!" You're spot on about the fluff.
One thing I've seen some teams do is keep their best canned responses as a baseline, then use the AI strictly as a smart editor. For example, paste your "Thanks for reaching out about your invoice..." snippet into the AI workspace and prompt it to "make this more concise" or "suggest a warmer opening line." That way, you keep the control and clarity, but the AI might offer a useful tweak you hadn't thought of. It flips the dynamic from fighting bad suggestions to directing the tool.
Have you tried that approach? It turns the AI from an unpredictable suggestion engine into a slightly cleverer text helper.
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No, it's not just you. You've described the core problem with most agent-facing AI: it's trained for generic politeness, not for the specific, concise communication that support actually requires.
>I spend more time reading and rejecting AI suggestions than I used to spend picking a canned response.
This is the real metric. Any tool that increases time-to-reply for simple questions is failing at its job. The "optimized resolution journey" type of language is a clear sign the model is pulling from corporate blogs, not successful support transcripts.
The trick is to stop letting the AI initiate. Keep your canned response library intact. If you use the AI at all, use it as an editor on *your* text, not as a suggestion engine. Paste your canned response and tell it to shorten or rephrase. That way you stay in control and avoid the fluff factory.
Your fancy demo doesn't scale.
You're not crazy, and your point about time-to-reply is the key metric here. This is a vendor problem, not a you problem.
The AI is trained on the wrong data. If a platform lets its AI suggest phrases like "optimized resolution journey," it's a clear sign they prioritized marketing gloss over actual agent efficiency. A good vendor would have constrained the model to actual, anonymized ticket history to keep suggestions practical.
Your instinct to rely on your canned responses is correct for now. If you're evaluating platforms, make this a direct question for their sales team: "Show me exactly how the AI is trained, and can I turn off proactive suggestions entirely?" Any hesitation is a red flag.
SLA is not a suggestion.