Implementing AI into your sales process? It only works when you have a playbook - with Deepak Shukla

Speaker A: This podcast is brought to you by Unmount's Go to Market Solutions, which includes Insightly CRM. Insightly is the modern CRM teams love. Unlike legacy platforms, a, uh, modern CRM is easy to use, flexible, affordable, and scales with you as you grow. With a proven faster go live and shorter time to ROI, Insightly delivers more for your CRM investment, helping you build lasting customer relationships. Visit insightly.com to learn more. That's insightly.com now let's get to closing time. Welcome to Closing Time, the show for Go to Market leaders. I'm, um, Val Riley, head of marketing for Insightly and Unbounce. Today I am joined by Deepak Shukla. He is an entrepreneur and AI strategist who has been helping companies rethink their sales workflows and operate with AI. Deepak, welcome to the show.

Speaker B: Hey, Val. Really happy to be here. Excited to talk about AI and thank you for the introduction.

Speaker A: All right, well, let's get started. And I want to start with inbound sales, because that's where most companies are experimenting with AI first. You've said that before, companies can really deploy AI agents for inbound. They need to build an operating playbook, essentially documenting their processes and use cases. So AI has something to go on. What does that playbook look like? And why is it so important when you're introducing AI to have one?

Speaker B: Sure. So I think that people hear Play Playbook first of all, and their, their mind melts a little bit because they think, okay, I've already got a playbook. My guys have got the pricing, they've got the framework and the layout. But in practice, what tends to happen is there are, uh, almost an infinite amount of edge cases, more especially so with service based businesses. But I've also trained it, so seen it through SaaS. So as much as anything is productized where it actually is, let's call it software as a service or a physical service or digital service, there's always edges. I mean, I've renegotiated contracts with zoom info, with HubSpot and many, many other companies, and I'm sure there's a lot of people out there doing the same. So what happens then in practice is that people implement AI too soon because you've not thought about all of the different types of scenarios that come up. So. So how do you, for example, adjust dollar pricing when you're talking to a Canadian versus talking to an American? Because of the currency conversion, everyone's still talking in dollars, but the value is totally different. So what tends to happen that's what happened in our case was, uh, that we launched AI too quickly and it was just annoying people frankly, because it couldn't understand things like this. That became really important. So when we talk about Playbook, it's about understanding what all of the nuances that exist across the business. And you'll often find that, uh, you've got mental models, but you need to build them into actually documented models to really then use AI in the most effective way.

Speaker A: Yeah, sometimes it feels like the exception is the rule. Right. Because, um, when you're trying to negotiate, you're definitely willing to make adjustments or changes that don't follow, like the exact pricing that maybe is on your website, um, just to make a deal go through. So it's all of those edge cases that really cause AI to stutter a little bit.

Speaker B: Yeah, yeah, for us it's been like that. You know, an example that we had recently, a startup that's funded with personal money versus a startup that's funded with an entrepreneur who's got exit money versus a startup that's funded with government money. Even that word is so nuanced. And you apply different filtering based on each of those scenarios. And therefore it's painful. But you really need to think about building that playbook to incorporate all of that. Otherwise you'll have what you see that's popularized now. All of the big corporate companies having what you call failed AI implementation because actually in the end they pulled it right back. Because I think that even at my level I've seen that, wow, there's innumerable variations. So. Yeah, no, I couldn't agree more. The Playbook, it's a bit of a cliche term, but it exists for a reason.

Speaker A: All right, so let's say you've convinced, uh, folks, hey, get that playbook documented, get all those edge cases in there. Um, so now we're going to start applying AI to inbound leads. In a traditional sales model, like a human person qualifies leads, decides whether to book an appointment or not. AI can handle that step. How do you see AI qualifying inbound leads and booking meetings even before a salesperson is even aware of the prospect?

Speaker B: Yeah, sure. So I think that uh, in an ideal world, first of all, you think about it from a salesperson's perspective. Every salesperson wants a sales qualified lead who's got decision making authority to come to a call. Anything less than that is non ideal. So when you think about it through that framework, what are all of the things that we typically ask of people to qualify over mechanisms Prior to calls, which is typically, typically for example email chats, WhatsApp, Telegram and the like. So when you look at all of these platforms, first of all, a lot of them have API access that allow you of course to then pass through messages. Second of all, we even mentally and again it almost deviates back to the playbook. We have mental models for, let's call them keywords that we look for. You know, pre AI, we were kind of doing SATA AI, uh, with what we call deterministic logic, which is ultimately if this, then that scenarios and formulas that we were building. So, so it's really making sure that you've got that put together uh, in a framework and what you'll actually find, and this is the interesting part that AI then becomes the sliver for managing edge cases. But if you've built a really strong rule book, deterministic logic can carry you a lot of the way. And fundamentally that is a case of giving AI rules. So then what happened with us practically is we began with human based rules in a document. We, we then fed it to a GPT. The GPT was then still being used by a human. The GPT was making errors based on edge cases that were coming up. After three months of edge case handling, we began to get to a place where okay, maybe we can let the rat out of the mousetrap or let the mouse out of the mousetrap and see kind of what happens out in the wild. Again there came up more problems. But then eventually we got to a place where hey, 80% of emails now can actually be handled. We're still learning because there's still some edge cases that come up once every so and so broadly, however, I really do believe that if you've got a mature lead flow, it does take that amount of time to actually build it out. And even where you don't you think, right, I can have AI start from day one. You've still got all of those edge cases to come, so you're still going to have to learn things down the line as you develop ultimately your sales process and flow. So you email I think is a great place to start. However, it does begin with that framework. Human first, ah, then rule book, then rulebook into a GPT, then GPT with human GPT and human analyzes GPT, more edge cases built, then we try and pull out the human. But then we still really monitor and do check ins more edge cases occur because also it's a consequence of how well are you building the rulebook and how much is it Subject to interpretation. So this is the framework that we followed where now, if you were to come into our organization, we'd say, look, we've improved the quality of leads coming to your calendar by probably 40, 45% through using AI and deterministic logic. And now a human steps in intermittently, but whereas before it was 24, seven, now it's probably, you know, four seven. So four hours a day, seven days a week to just monitor. And that's kind of the process that we follow now.

Speaker A: Gosh, I almost wish there was a magic, uh, wand where we could, everybody could transform themselves and get past all of that, that hard stuff, right? The, the, the human plus AI time frame. Because I imagine the first time a rep gets a qualified, uh, appointment on the calendar and it works flawlessly. It must feel like a million bucks.

Speaker B: Yeah, yeah, yeah, no, exactly. It's a wonderful feeling. And the, the challenge I think that we have, I have as a, you know, we're what, maybe I think about 134 people, eight figure business. We are still small by many means. And the upside, slash downside is that you can be agile, but it also leads to impatience. And this is a place where there is a need for proper process. And what happens is you're like, right, I can install the API or the integration. I've got my key. I can let it run. And you quickly discover that, oh, wow, it's causing more bad than good in the end. So we've had to go through the learning curve, and that's inside out what our learning curve has looked like.

Speaker A: Um, okay, so what we've described now to me is like the Holy Grail. Like, that's where we all want to be. But in practicality today, um, most organizations aren't there, right? But they are having sales reps do things like use AI to draft email replies, maybe generate proposals, maybe schedule meetings or prepare for sales calls. Are, uh, those use cases actually delivering value right now? And is that a way, like almost a stepping stone to get folks to invest in that next level that we were just talking about?

Speaker B: I think that there's definitely a framework for adding AI assisted intervention across the spectrum. Again, however, annoyingly, it is a matter of how intelligent you can prompt engineer. Because two people can say, great, I did. So I'll give you a practical example of what this looks like. I observed language is a funny thing. I have two sales reps that will both say that they've done sales prep and they'll produce completely different materials across the spectrum in terms of what they have. That's useful for the call because you can ask an LLM to prepare you for a call, but it's still relative to how the LLM is primed and what's also important to the company in terms of outcome. So that might mean, hey, if I'm preparing for a call, I want to uh, also have the rep know about what's happening in their industry and not just about that service. So you can sound like an insider and I want you to be able to use industry jargon. So if you're talking to chief, uh, Revenue Officer, if you're not using ABM, SQL, MQLs, AOV, LTV, and you're going to talk about, so then we close business and then we get the next deal. There's a real distinguishment of quality. So again who's prompting and therefore what is the output? So all of that is possible. The truth is that it's the whole kind of monkey with the machine gun scenario that you need to still, I think design frameworks to ensure that there's a, uh, there's a kind of a ringer that every prompt is going through to ensure that there's kind of consistency of output, if that's what you're looking for. Like a bit of a standardized knowledge, knowledge base, a dossier if you will. So that meant that with the GPT that we ultimately built for sales reps to use, we had to put really strict guardrails, build that in. And now what happens in our team is that uh, if you for example have a booking that comes into your calendar, then we'll run that through a uh, knowledge base that has strict instructions as to what it should produce. So that in that scenario it works really well. I have, I know still the reps do use it. But the problem also with different reps using AI is that again that problem of producing wildly different results. Some people get lazy and you can tell they've got the double dash, which is quite classic within AI. But not everyone apparently is aware of that because I still see an email, it's coming up. And then also there's just funny, that's because people get lazy, they just say improve this. There's no avoiding the hard work. So it's again that uh, 20% of people will produce, I think, quote, unquote exponential results. And then everyone else, you'll just be able to tell from looking at it, VAL didn't write this email. Val's GPT wrote this email. And that's again another challenge that we have. And that's why we had to create GPTs. Strict rules around how people use the LLMs. Because. Because when people deviate from it, they produce all kinds of wild stuff in email size.

Speaker A: Mhm. Yeah. I mean it sounds like most other things, right? It's like you get out of it what you put into it. You know, I mean we sell CRM and you have people who have wildly successful CRM implementations and you have people who have really poor CRM implementations. And the idea is how much time did you put in upfront to make it work? So sort of the same message overall. I'm going to ask you to pull out your crystal ball, uh, because we've talked a lot about inbound sales, but not so much about outbound. Um, do you see a future where AI could really do a good job with outbound? Because most of what I have seen has not been great so far.

Speaker B: I think. Yes, I think that again, it's about understanding the constraints and how to make best use of what exists. So uh, if we talk more tactically now, because my underlying theme is that everything's tough, which isn't always ideal for someone who's like Deepak, what's the hack? I would say that when you have a novel offer, then I can work really well. So for example, if I say, hey, we're giving away a free gift card to this software, it's free for one year. That's it, that's the offer. Uh, what's the catch? There's no catch. Try it and if you like it after the one year, subscribe. That's a novel enough offer that you don't really care about the quality of the salesperson who's pitching that because the quality of the offer is strong enough of itself to convey the message. So in those types of environments where you have a truly. Have a unique offer, unique mechanism, something that, and, and this is the, this is the, this is the kicker. Most companies don't. Most companies just don't. If you're in I, I'm in digital, like almost everything is commoditized. Unless you can figure out the. Alex Hormozi talks about the hundred million dollar offers. Russell Brunson talked about it before him with ClickFunnels and all of his books and before him there were others. You need to. If you have a unique mechanism, then I do believe that AI outbound can work because it's just about the quality of what you're offering. Then even if it's fumbled and it sounds a bit robotic, you don't care. You don't care a little bit. Like in the same way that I make lesser distinctions with people that have broken English, struggle to get through a pitch, but they're selling me, for example, uh, a software product, if the quality of the features and benefits are important to me, I make more of a decision on that if there's a need there, as opposed to how well they conveyed the pitch. And that gets more complex as you go down the hierarchy with services. So yeah, when there's productized offers or something that's novel, I do think AI outbound can work.

Speaker A: Okay, well that's exciting. That's the. Maybe that's the first step. Uh, is those novel offers that really sell themselves. Um, okay, last question. Um, you're talking to a sales leader and they're curious. They are not doing anything AI related. What would be the first step? You would say, hey, it's okay, you're not that far behind. Here's something you could implement pretty easily.

Speaker B: Sure. So I think that the first thing would be, I mean, I'll go through a couple of things because it'll be different for different people. But number one would be, hey, pay for ChatGPT or pay for Claude, number one. Number two would be, hey, if you pay for it, then also start using projects and also within, within ChatGPT as an example. Because people tend to skew one way. I use ChatGPT for everything at the end. I tried other LLMs, but my. Your brain doesn't work that way. So with. But, but all of them broadly offer a similar kind of suite. You have your paid subscription, then you have GPTs. I'm not sure if Claude can do that. Probably it can. Then you have projects and I think getting familiar with the house in which many future businesses will live in is the first thing. So I think that the biggest thing for business owners is to be if, especially if you're small and agile, is you have to be familiar with what you're doing to make best use of it. And then I think from there you'll begin to kind of extrapolate out what's possible. I think, um, and I tried that. What I did try is have other people deploy it and then there was incorrect deployment because they didn't have enough of an understanding of the business and the nuances. So also if you're the business owner and you're not going to be the person doing it yourself, give it to someone who's tenured by within the business, who understands how the business works. Because the problem with people that are, uh, Avant garde AI is that they still need to learn the nuances of how your business works internally, which is problematic within itself. So you need to find where the rubber hits the road and find someone who understands the business well enough but is open enough to really embrace what's new and get through the learning curve. And that's kind of the sweet spot. And ironically, in smaller companies, that actually tends to be the founder and, and founders will often defer, uh, the burn your brain calories part to someone else. And when it's something that leads to category changes, I think that that's not, that's not, it's not sensible. With AI, it's a paradigm shift. So I think that that's where owners should come in and put in the hard yards because it can lead to massive changes. Like, it's transformational. So I think that that's where I would start. If you have yet to embrace it,

Speaker A: I don't think anyone would argue it is definitely transformational. I also want to do a quick plug. If your sales team and your marketing team can be on the same, um, uh, AI LLM, that's a plus. So, uh, I would. That's my. That's my little plug from the marketing chair. Yeah. Deepak, this has been fascinating. Thank you so much. I think a lot of companies are asking these questions right now, and I think the timing of this episode is perfect. So I appreciate your time.

Speaker B: Oh, no, thank you for the chat. I really enjoyed it.

Speaker A: And thanks to all of you for joining us as well. Remember, you can get this episode and every episode of Closing Time delivered right to your inbox. Just click the link in the show notes. We'll see you next time.