Speaker 0:  so let's talk ai. i've been on several sessions with marketers and salespeople, and every time i bring up the role of ai and marketing going forward, everyone kinda gets quiet. however, you or linkedin mentions an obsession with prompt engineering and chat gtp. what role do you think ai will play in sales and revenue enablement in the near future if it's not already doing it?

Speaker 1:  yeah. i think the important thing, i guess, to in my opinion, craig, to note is that we're still in early, early days. right? and we're still trying to figure out the value that ai provides. right? and i think what the kind of the key is to try and pinpoint a couple use cases. and, i guess, kinda how we've done it previously is, you know, to be quite crude, you know, what sucks as a sales rep. right? so they're trying to pinpoint what sucks and how can you automate that with ai. and i think it's something that initially is intimidating both for enablement as well as the sales and revenue teams that you're enabling. so, again, i think we're still in kind of that experimentation phase. it's been interesting to kinda see some of the stuff come out, but i still think it's very specific to your specific company or your scenario and how can you kinda streamline stuff that really is tedious, especially for someone who is customer facing.

Speaker 0:  i absolutely believe it's, you know, a hundred percent specific to you and your business and your offering and your clients and stuff. are you using automation tools outside of, say, chatgtp, like make, zapier, and eight n, any of those?

Speaker 1:  yeah. so what we were actually able, able to do at, at my previous company was, again, kinda getting back to that what sucks theme, is, you know, certainly as a sales rep, when i kinda obviously speak in personal experience here is that what is hard is call prep. right? you just wanna be able to jump into a call and, you know, sort of shoot the breeze and do your demo and do your discovery. but the prep is tedious. so what we were actually able to do was, create a custom version of chat gpt, which is called a custom gpt. but, essentially, all of our reps, all they needed to do is type in the customer's website, you know, in preparation for the upcoming call, and it would spit out a list of curated questions that took into context the customer's business, took into context the value that, you know, that our product or service was providing, and then kind of the sales framework they were asking our reps to try to adopt. so we're able to do that. and so, again, they were able to show up to calls, and this took, you know, thirty seconds to get. it'll just show up to calls with a list of these curated questions, and then we actually developed a prompt, that we chucked into our call listening tool, which we use was, was gong. and then any of those questions that they were asked, the answers, we were able actually to to get those and then push those using zapier into hubspot as a note. right? so what that did, that eliminated the need for our reps. again, getting back to what's tedious is after the call, going through it, writing down the notes. right? so it was automatically pushed into hubspot. and then when the customer signed up, our implementation team, all the notes were already there. right? so, again, it kind of this framework they were trying to get everyone to adopt, and it was automated using chat g p t, using gong, and using zapier.

Speaker 0:  and that's that sounds amazing. i'm thinking along the lines with the tools we have now, the one addition i would add potential to that workflow is real time analysis of the transcript so that you can actually surface answers to objections based on your, you know, objection sheet, handling sheet, whatever you database you use because it's analyzing that. your salesperson gets a chance to talk, and within a few seconds, you know, the three or four answers or ten that you to handle this objection pop up so they can pick the one they want and off they go.

Speaker 1:  yeah. yeah. so there's kind of that real time feedback. i know there definitely are some tools out there that are trying to solve that, solve that problem. but just to take it a step further, craig, what we're able to do as well too was we're able to take that output, again, the answers to those questions, and we're able to push those into a slack channel. right? so then the head of sales now had kind of, like, a time delayed feed of all of the demos. perfect. other departments found out about this channel. so what started with one or two people on end up being kinda thirty because marketing wanted to see it. if there's any nuggets in there, the implementation team wanted to see it, if there's any intel in there. right? the partnerships team wanted to see it as well too. so, again, i think it's take kind of an experimental mindset, you know, going with a single use case, but then, yeah, be open to kind of expanding that. and, again, just asking that, hey. you know, who else could this be useful for? we have this automation here. we're extracting stuff from our calls. what other departments can utilize that information?

Speaker 0:  and that's a great way to go about it. i'm building new automations every day with some of these tools, and they're changing all the time. but, like, if you're not thinking about automation in your business or have someone who's doing that, you know, you're gonna be falling behind within a year, if not sooner, just from a you can be so much more productive, and scale faster with some of the tools that you're implementing and the tools we're implementing to make these things, one, follow the process, but also, you know, draft up. you've got your questions and everything ready to go. you know, you can draft up sales letters. you can draft up a thousand different things that all the salesperson just has to go in and edit and all that kind of stuff. and this stuff happens automatically based on your sales calls, based on because you're you can take that transcript and chat gtp and some of these automation tools and say, okay. based on here's our offerings, and here's what was discussed, and here's this, and here's what we'd recommend as and build that out and push it out and be ready. like, things you can get off the phone and have these proposals ready in a minute versus hours. right?

Speaker 1:  yeah. yeah. that's the thing. i think too another use case that we did use was that because we had the slack channel with all these summaries, we were curious around, like, who was actually showing up to our calls, what type of personas were showing up to our calls. so what i actually end up doing was cutting and pasting. it was maybe, like, five minutes scrolling up and copying, which i had to do twice because i messed up, you know, as you do when you spend that amount of time. right? and then i took all that and then chuck that into chat, gpt. so give me the top ten roles or top ten personas of people that showed up to our calls. right? then we're able to use that. right? whereas if i was having to go through all those calls and listen or you'll get someone's opinions, like, hey. listen. you know, the executives, most of the time, they're showing up to our calls. right? so kinda be able to take that different data driven approach. and i think that's very, very important. and, again, it's kind of interesting how you need to experiment with these things, but they do kinda morph into these multiple use cases. so i guess kinda my advice to people is at least try experiment. you're gonna dedicate some time to it, but give it a shot and you'll be amazed what, what can come out the other side.

Speaker 0:  yeah. and you can do so much with these things now even if you just start with a tool like, claude or chatgtp or one of these just to help you through things. but once you actually start getting efficient at it, you can really build these workflows that save you hours and hours.