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AI strategy & operationsJune 22, 2023

AI Analytics for Support: From Fast Answers to Fewer Questions

AI shouldn’t stop at answering tickets. The same question-and-answer logs can surface trends, training gaps, and usability issues—if you analyze them the right way.

Originally posted on YouTube on June 22, 2023

Key takeaways

  • AI’s first value is fast, usable answers, but its second value is analytics that reveal trends across interactions.
  • Analyzing Q&A logs can expose common topics, training gaps, and usability issues without complex prompts.
  • The operational goal is fewer repeated questions through upstream fixes, not just faster ticket responses.

Most teams first meet AI as a question-and-answer machine: type a prompt, get a response. That’s useful, and it’s where a lot of the conversation is stuck.

But inside an organization, the bigger payoff often comes after the answer is delivered—when you treat AI interactions as operational data.

The two jobs AI should be doing

James Moelk frames AI’s value as “twofold.” First, it helps individuals get unstuck and move faster.

“People will ask a question and get a response. Super powerful. It’s great for giving you a list of ideas.”

That applies to personal productivity (drafting, brainstorming, outlining) and it applies at work: a support tech, salesperson, or new hire asks a technical question and gets a usable response quickly.

The second job is the one most teams ignore: AI can summarize what’s happening across hundreds or thousands of those interactions and turn it into management-grade signal.

Secure, internal Q&A is table stakes

If you’re serious about using AI with real customer and operational data, you need to start with containment. IntelliChat’s model is simple: ingest a company’s data and let users ask questions about that data specifically—without sending it out into the open.

That matters in multi-tenant environments where the wrong configuration becomes a data leak, and it matters for regulated industries where “we didn’t mean to share it” doesn’t go far.

You can’t build trustworthy analytics on top of an untrustworthy data flow.

Turn Q&A logs into operational analytics

Once you have secure internal Q&A, your question-and-answer history becomes a high-quality dataset about what people can’t find, don’t understand, or keep getting wrong.

Moelk describes taking a set of questions and responses and feeding it back into the AI with a straightforward prompt: what insights would help improve support in the future?

“I really didn’t feed it that much… The prompt was… simple.”

The output he got was the kind of analysis support and ops leaders usually assemble manually:

  • Common topics: the most frequent queries, grouped into themes rather than one-off keywords.
  • Training needs: repeated confusion that suggests onboarding gaps, missing SOPs, or docs that are present but unusable.
  • System usability issues: patterns that point to friction in the product or tooling—places where users are forced to ask for help because the system doesn’t guide them.

This is where “AI for support” moves from reactive help to proactive improvement.

The goal isn’t faster answers—it’s fewer questions

Fast answers are good. Fewer questions is better.

When you can identify bottlenecks and pain points, you can fix the upstream causes: unclear workflows, incomplete documentation, broken UX paths, or training that assumes too much context.

That reduces ticket volume, improves first-contact resolution, and makes your team less dependent on heroics. It also gives execs something they can act on: trends, not anecdotes.

What this means for you

If you’re evaluating AI, don’t stop at “does it answer correctly?” Ask what it can tell you about your operation over time. The most practical systems will support secure internal Q&A and analytics that surface what to fix next—so your team spends less time answering the same questions and more time removing the reasons they get asked.

Full transcript
So I really think that the power of AI is kind of twofold. The first is how we're kind of seeing it being used today, which is where people will ask a question and get a response. Super powerful. It's great for giving you a list of ideas. I, in fact, I just used it this morning to get some video topic ideas and it was pretty great. I asked for 20. I think I had to throw five out and tweak another five, but it really kind of gets you going in the right direction and kind of gets the creative juices flowing. So it's powerful for that. Obviously, from a business standpoint, with IntelliChat, which is the AI sales and support company that I own, what we do is we ingest a company's data and allow a user to ask questions about that data specifically. We do it very securely and, you know, it doesn't get out there in the open. And that's great for an end user or a support tech or a salesman to be able to ask a question that's highly technical and very quickly get a human response back is super powerful. But what's next, right? You know, what's the other side of that? The other side is, you know, the ability for the AI from kind of a management standpoint, you as a business owner, not necessarily the support tech or the salesman or the trainee, but, you know, the business owner being able to see what's happening with the salesman, the support tech and the trainee and kind of start to get some trends. So what I did, and we're integrating this into our dashboard for IntelliChat actually, but I think this is important, is I took some of the responses, the questions that were asked and the responses, and I fed it into the AI. And I said, okay, I want you to give me some like, you know, some stats on this. What's going on? You know, give me some analytics that would be meaningful. And it did. It gave me common topics. You know, it's like the most frequently asked queries can be identified and then it expanded on them. It said, here are some training needs. So, you know, there might be some end users that need to know a handful of things, again, we're identifying that these things just don't seem to be covered. And then also talked about system usability. And I really didn't feed it that much. You know, the prompt was, what data can you give me from the data that would be helpful for improving support in the future? That was it. It was, it was simple. And I fed it data in the previous response. And then I just asked, you know, give me something that would be helpful. And it got into system usability. It understood that there was a system behind this that was going to need some work. So that's really what's, what's powerful. Yes, at the end, at the end user level, it's great to get questions answered quickly, but what's really powerful is when you can start to get to the point that those questions don't need to be asked because you've identified potential bottlenecks, pain points within the pieces of software, within your training method, whatever it is. So the more we can start to use AI to analyze our company data internally, the more powerful that's going to become either as an AI tool or just the smarter the company is going to become in general.
Originally published on YouTube
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