A Proven Approach to AI for Revenue Teams

03:56:04.130 –> 03:56:09.280
Julia Nimchinski: And we are transitioning to our demo part in a second.

1281
03:56:11.060 –> 03:56:15.480
Julia Nimchinski: Yeah, welcome, Josh.

1282
03:56:16.150 –> 03:56:16.870
Josh Solomon: Hey! There!

1283
03:56:17.600 –> 03:56:18.750
Julia Nimchinski: How are you doing.

1284
03:56:18.750 –> 03:56:20.589
Josh Solomon: I’m good. How are you? How’s the day been.

1285
03:56:21.650 –> 03:56:33.439
Julia Nimchinski: It’s been really fascinating. Hitting a lot of topics, every topic. You can imagine anything and everything agentic and excited to see some demo in action.

1286
03:56:33.800 –> 03:56:36.490
Josh Solomon: What’s the latest and greatest? Ask AI.

1287
03:56:37.180 –> 03:56:39.859
Josh Solomon: Yeah, for sure. Am I? Am I good to pull my screen up.

1288
03:56:40.050 –> 03:56:41.080
Julia Nimchinski: Absolutely.

1289
03:56:41.480 –> 03:56:43.019
Josh Solomon: Great. Let’s go ahead and do it.

1290
03:56:46.450 –> 03:56:47.790
Josh Solomon: So

1291
03:56:48.000 –> 03:57:14.029
Josh Solomon: alright. So let’s go ahead and jump into things here. So just by way of introduction, everyone. I’m Josh and the Vp. Of revenue at a company called, Ask AI. We’re a post AI company that’s focused on b 2 b go to market and customer experience. And we really think it matters that our product is built from with AI from the ground up. So about a quarter of our employee population have advanced degrees in AI. And it’s really at the heart of everything that we do.

1292
03:57:14.140 –> 03:57:22.309
Josh Solomon: So I thought what I would do is just maybe start by framing up how we see the problem inside of your b 2 b inside of your revenue organizations today.

1293
03:57:22.380 –> 03:57:51.080
Josh Solomon: And so as you think about sort of our sales, cycles, or account structures. What we plan for was a relatively simple take on how our account teams would manage our customer. Our customers. Either our champions in a cycle or advocates post sales along with the executive buyers, and the people who have to touch or feel our product on a day to day basis. But as our cycles, our businesses, and the competitive landscape of Sas have gotten much more competitive

1294
03:57:51.634 –> 03:58:19.990
Josh Solomon: how we actually do business is much different, right? We rely on a much broader set of people across our organization. Whether that’s our delivery teams, our legal teams, our product and engineering teams, and we’re consistently selling to more people across our customers organization right out to their it teams, their legal teams, their executive teams, their users across their their business, and a wider variety of departments or personas than we’re probably used to in the past.

1295
03:58:20.060 –> 03:58:46.300
Josh Solomon: and what this creates is quite a messy mode of communication across your organization and the customers. Space right? It’s sort of starts to look something like this. And as an Ae or or a Csm, we’re the per people who are actually responsible for managing this mess on a daily basis. Right? We think. And we hear all the time, you know, from Gartner. You know, buyers are spending less and less time with sellers.

1296
03:58:46.300 –> 03:59:05.460
Josh Solomon: but on the flip of that we hear sales. Cycles are getting longer and longer. So what are reps spending their time on? Well, what reps are spending their time on is managing the internal mess that we have to manage, and it actually results in about set with 70% of our time not being spent with our customers not focused on selling, managing internal process.

1297
03:59:05.640 –> 03:59:18.409
Josh Solomon: and that is largely driven because every organization, every department, every persona that you have inside of your own business, is living in a different source of truth, right product, and engineering are working out a jira and confluence

1298
03:59:18.958 –> 03:59:38.099
Josh Solomon: solution. Engineering is got everything stored in in notion. Our executives are spread thin, our sales teams are operating out of gong. So every team has a very different source of reality. Right? And so this mess of the knowledge, and how your teams actually operate can be completely transformed with AI.

1299
03:59:38.380 –> 03:59:42.950
Josh Solomon: And so there’s lots of promise out there around how this actually can get done.

1300
03:59:43.445 –> 04:00:06.380
Josh Solomon: But the current approach is not working for anyone. I talk to. Almost everyone I talk to is inundated with AI stuff right? Every vendor that’s in their that’s in their existing tech stack is banging on their door with a seed upgrade to add an AI feature. Skew some sort of AI summary into their into their business, and there’s a ton of overlap and not much clear value. That’s out there.

1301
04:00:06.430 –> 04:00:32.799
Josh Solomon: So the way that we’ve approached this is by building a single platform for go to market and Cx teams. It starts by looking at all of the knowledge and data that’s inside of your organization across your field teams, your engineering teams, your G and a teams and bringing it into one what we call AI operating system, and allowing you to have the infrastructure, the tools, and the applications to build use cases on top of your own knowledge.

1302
04:00:33.000 –> 04:00:56.570
Josh Solomon: And so there’s 3 primary ways that people use. Ask AI. The 1st is what we call a rep assistant. This is an AI agent that allows that actually works alongside your reps to provide them. AI answers and AI automations. The second is from a customer self service perspective. How do you, either on your website, your help center or in your product help serve your customers with AI powered resolutions.

1303
04:00:56.650 –> 04:01:11.889
Josh Solomon: And the last is around intelligence. How do you look at the customer conversations that you’re having across a variety of different objects inside of your business and use that to power different programs like voice of customer quality assurance, risk, identification, etc.

04:01:12.020 –> 04:01:37.249
Josh Solomon: So I’m gonna jump in now to a couple of examples and show you primarily, we’re gonna focus on from things from a rep assistant perspective. So the 1st thing that I want to do is just introduce the ski assistant to you. So I’m gonna go ahead and open up the ask. I assistant. And one thing that’s fundamentally different about the approach that we’ve taken to deploying. Our product is that we deploy as a browser based assistant.

1305
04:01:37.310 –> 04:01:49.680
Josh Solomon: So that means no matter where reps work, whether they’re in slides like I am here, or they’re in salesforce or in gong or in your product. They’re always one click away from being able to access their AI assistant.

1306
04:01:49.900 –> 04:02:04.639
Josh Solomon: So this is the interface here, and you’ll see up at the top. All of the connected knowledge that I have access to this allows reps to get AI powered access to knowledge from anywhere in your organization had based on what you’ve connected in task. AI,

1307
04:02:04.830 –> 04:02:16.190
Josh Solomon: so let’s jump into a couple of use cases. So we’re gonna jump into my my inbox here. So assume I’m an Sdr. I’ve been prospecting Scott at our terrex, and I finally get a bite

1308
04:02:16.310 –> 04:02:24.400
Josh Solomon: right. I get a response and I get an ask for hey, do you have any case studies that show how you improve response times and impact csat scores?

1309
04:02:24.740 –> 04:02:50.610
Josh Solomon: Scott also has a question about how we differentiate from salesforce right in the Old World. I would probably go out to slack. I would post these questions. I would wait till someone responds, or I’d search through high spot, and then eventually I’d get back to Scott right? What I’m gonna do instead is just jump into. Ask AI, and I’m gonna ask the agent for a little bit of help to respond to the email highlighting 2 case studies and including a couple of bullets on salesforce comparison.

1310
04:02:50.840 –> 04:02:53.700
Josh Solomon: And I’ll just paste the email in here as well.

1311
04:02:54.100 –> 04:02:57.900
Josh Solomon: And so this is, gonna go ahead and the agent’s gonna do a little bit of thinking.

1312
04:02:57.940 –> 04:03:20.079
Josh Solomon: And it’s gonna go ahead. And it’s actually gonna generate for me a draft response that allows for me to see. Okay, here is a response to Scott’s email. Here’s the conductor case study. Here’s the rapid 7 case study. And here’s how they specifically address the 2 things that they were looking for in the email. And here’s some information about how I actually, or how we differentiate from salesforce.

1313
04:03:20.080 –> 04:03:45.019
Josh Solomon: So from here as a rep, I can interact with this. I can just copy this answer, and I can go ahead and draft a response. So I can just go ahead and copy paste this into my email retains all the formatting, etc. And if I wanted to, I could also review some of the knowledge that’s attached here, so I can actually dive directly into the documentation and start to review any of the battle cards. Any of the source documentation that the team has built for me to help me actually do my work

1314
04:03:45.190 –> 04:03:54.010
Josh Solomon: right from here. If I have follow up questions I can ask. So let’s say, do we have any notable wins

1315
04:03:54.590 –> 04:03:56.660
Josh Solomon: over salesforce

1316
04:03:57.060 –> 04:04:20.420
Josh Solomon: right? And so again, this will go ahead, and it will query, you’ll see that uses a different set of tools this time in order to actually generate the answer, and it will actually pass me back a couple of examples where we have a a couple of notable wins over salesforce. If from here, let’s say I still haven’t found what I’m looking for. What I can do is actually now route this question to a dedicated slack channel. So let’s say something like our ask, go to market team.

1317
04:04:20.430 –> 04:04:46.769
Josh Solomon: And I can actually tag a specific expert. Maybe Adam, to actually get help. And why this really matters is this gives our leaders a way to automate the QA. That’s happening inside of their organization today and actually build the knowledge repository that you have from the bottom up. Because now, when Adam actually answers this question, we’re going to go ahead and we’re going to index it. And then we’re going to allow that the next time an Sdr. Or anyone else asks this question, they’re going to get a direct answer from. Ask AI.

1318
04:04:47.180 –> 04:05:14.159
Josh Solomon: So let’s look at a couple of other examples here. So this same concept can now extend out to basically any other moment in your playbook where reps are working to prepare for customer activity. Right? So here’s an example where I might be prepping for a big demo. Right? I have a demo with apex wave for our secure AI product. I can come and task AI, and I can actually use this agent to help me prep. So help me prep for my call with apex wave, help me outline the following.

1319
04:05:14.250 –> 04:05:21.699
Josh Solomon: and this will search through all of the knowledge that we know about our customer in order to give us a real time status of what’s going on in the account.

1320
04:05:21.860 –> 04:05:42.810
Josh Solomon: In a second, I’m able to actually understand how they use our product, what their usage looks like what competitors have been mentioned in a cycle, what the next steps are. If there’s any open cases that are attached to that, to that this account, and allow for me to have much better visibility and a level of sort of confidence that I actually can go execute in this demo as I go forward.

1321
04:05:42.940 –> 04:06:10.609
Josh Solomon: And one thing that’s really helpful with that is actually bringing knowledge that you know most teams wouldn’t see. So for our support team. Obviously they live in salesforce cases every day, but as a seller I almost never look at salesforce cases, so this gives me an opportunity to drive directly into the case. I get an AI powered summary of the case, and I’m actually able to review it directly in line here as well. So when I jump on the call. I’m not surprised by the fact that there’s an open case, and maybe the customer is actually a little bit frustrated.

1322
04:06:11.610 –> 04:06:41.490
Josh Solomon: So now let’s make things a little bit more complex. So I’m gonna jump into Hubspot. And let’s say I’m working on some of my target accounts. And one of my target accounts is beyond trust. I’m gonna go ahead and just highlight the text here, and you’ll see that. Ask AI starts to promote a few different options for me. This is what we call AI agents and apps. So I’m gonna run this account research app, and this will allow me to actually generate. Pardon me, wrong button this is, gonna allow me to actually generate

1323
04:06:41.600 –> 04:06:44.850
Josh Solomon: a specifically tailored

1324
04:06:45.459 –> 04:07:10.240
Josh Solomon: a specifically tailor tailored research site summary of this account. So this will go through, and it’ll provide me a high level summary of the business. Any recent news on the account key executives key executives and decision makers in the account, as well as quotes from their executives. In particular, something very specific to what we think about how complex is the documentation on their website.

1325
04:07:10.420 –> 04:07:14.760
Josh Solomon: and any references in our customer base that might actually be useful

1326
04:07:14.790 –> 04:07:37.769
Josh Solomon: right? And so now, as I find, let’s say, an executive from this research inside of inside of my research. Here I can go ahead and open up their Linkedin profile. And I might run a new agent right now I want to research the prospect. So now I can research the prospect in one button click. And I’m able to actually go ahead and see you know a a few new pieces of information.

1327
04:07:37.770 –> 04:07:48.999
Josh Solomon: What’s the overview on the prospect? What’s important to know about the persona? What are the recent trends and professions that trends that in this profession, pardon me that are taking place.

1328
04:07:49.000 –> 04:08:17.270
Josh Solomon: How? Asks value and any relevant content that maybe I should share directly with this prospect as I’m I’m going out to actually send a message here on Sean’s Linkedin, right? And so what’s really great about each of these agent or app use cases, and I’ll show you this in a second. Each one of the components that you’re seeing in terms of output is completely customized, based on your use case right? For me. It’s really relevant to understand what the trends in the profession are today, because a lot of it’s changing around AI.

1329
04:08:17.270 –> 04:08:34.939
Josh Solomon: You may have a very specific persona feature that you want to actually get out in your research for your team, or something very specific that you need to identify on the web about accounts as you do research our agents able to actually allow you to go and do that. And you’re in full control of what that output looks like. I’ll show you that in just a second here.

1330
04:08:35.389 –> 04:08:45.369
Josh Solomon: And so maybe just one last use case. Here is, you know you can extend this to just about anything that your reps do on a daily basis. This is one that I use almost every day.

1331
04:08:45.370 –> 04:09:08.180
Josh Solomon: which is, how do I actually handle legal red lines and do some lightweight cleanup before I push this back to deal desk or back to my customers? This allows me to just go ahead. Highlight the text in an agreement. Run the review red lines application here. And this will actually query all of the the a approved contracts in our database, and give me guidance about, can I accept this term or not?

1332
04:09:08.310 –> 04:09:18.129
Josh Solomon: Right? And so, for something that often looks like a different language to Josh. I’m now able to actually participate. Turn these around much faster and really reduce the customer. Wait time.

1333
04:09:18.520 –> 04:09:38.100
Josh Solomon: So the last thing that I wanted to show you here is just how these agents and apps come together. And this comes to through what we call our no code builder. So basically here, you have an ability to build an array of different apps. And so I showed you the account research app at the beginning here and I’ll just maybe dive into how this comes together.

1334
04:09:38.260 –> 04:10:06.910
Josh Solomon: So you have the opportunity to build an app yourself. There’s obviously some basic configuration. But then you can start to actually set up the configs. So I showed you a few of these right? We might want to highlight a set of text like the legal review, or we might want to actually scrape a full web page right like the Linkedin profile to actually start to gather information from here. I can plug into any AI model that I want to use. We call this an AI gateway to use the right model model for the right task.

1335
04:10:06.990 –> 04:10:19.489
Josh Solomon: And then I can start to connect to different data sources and tools. So this agent actually has access to a couple of different tools. It has access to all the knowledge. That’s an Sdi. It has access to a Google powered search, and it has access to a web crawler

1336
04:10:20.006 –> 04:10:27.960
Josh Solomon: and you can add a variety of different tools and actions here as you like, and then build out a structured prompt that uses those tools to complete the agent’s action.

1337
04:10:28.300 –> 04:10:44.090
Josh Solomon: So maybe just in closing one thing that people almost always ask me is okay. Where do I start with? You know, deploying AI across the go to Market Org or across the revenue org, and where I’d really urge you to look is in slack

1338
04:10:44.100 –> 04:11:10.559
Josh Solomon: right there’s so much internal noise that’s happening in slack on a day to day basis. Every leader I talked to knows that it doesn’t make sense to escalate things aimlessly into slack that we have channels that just bog down our Ess or Ps team, or our engineers. And what you can do with, ask AI, and you can do in a really easy way, is actually get very clear metrics on the number of questions that exist in air, slack channels. How many of those questions repeat.

1339
04:11:10.670 –> 04:11:38.200
Josh Solomon: and how many of those questions you can actually answer with the knowledge you have today? Along with a set of other metrics that will really show you where the problems are to solve in terms of your team’s operation, how they actually use knowledge internally. And for anyone that’s here today, if you’d like to do one of these analysis. We’re happy to do that for you. At no, at no cost. So that’s 15 min on us. I appreciate it. I hope everybody’s having a great day. And if you’d like to jump, jump in on something like this, my emails here, it’s Josh at ski.

1340
04:11:38.320 –> 04:11:39.249
Josh Solomon: Thanks a lot.

1341
04:11:40.620 –> 04:11:46.800
Julia Nimchinski: Super cool. Thank you so much, Josh. We let’s just address one quick. Oh.

1342
04:11:47.410 –> 04:11:53.560
Julia Nimchinski: Josh is gone. No question. It’s gonna be addressed. Hey, Benny, how are you doing.