Mitchell Edwards: Today we’re going to be talking about artificial intelligence. And no, we’re not talking about robots in a sci-fi movie that are trying to take over the world. We’re talking about a real-life tool that can actually drive results for your commercial teams today. So let’s talk about some numbers. When we look at those that have some form of budget item allocated towards artificial intelligence for 2024, there are about 52% to 62% of companies that are making that form of investment. Now, how do we translate that into who’s actually using the technology today. And so that’s where the numbers start to change quite a bit. And so from our research what we found is between 18% and 20% are actively using AI-based tools for their commercial teams today. You might be one of those companies that has some form of budget allocated, but you don’t know what to do with it. Let’s talk about some potential use cases for how you could use artificial intelligence for your commercial teams. The most important step here is to link this back to the business challenge that you’re trying to solve. Now, you might be coming from an environment where you have high-performing sales teams, but you have a real challenge with customer success or customer satisfaction on the back end. I’d go down the path of looking at tools that might help support improving that area of your business. Contrast that with another company that might have big problems with rep productivity as an example. That might lead you into a very different direction when it comes to the type of AI or technology that you implement to help support that.
I’d also push you to think a little bit about, what if it’s not an AI technology that you need to support those teams? What if it’s we need to get better at how we segment our customers? We need to formalize more of that segmentation process, or we need to change the roles and the roles that are aligned to those segments. I’d go down one of those paths before I start exploring artificial intelligence. But let’s say you’ve solved all the things that are not tool and technology-driven, but you really want to implement some artificial intelligence. What I’m going to run through today are four different use cases for how you could be using artificial intelligence to help support your commercial teams. We’ll start out with maybe the more basic end and then move through to some more advanced ones that might be a benefit to your business.
So the first one is really around inducing some form of an AI assistant or copilot. Right? Agnostic of tools here, it could be your copilot that’s part of Microsoft. It could be taking a tool like ChatGPT. It could be using the Claude model from anthropic. Name the tool. The idea here is you just launch some kind of copilot or assistance to help level up your teams. And so that could be something as simple as using one of these large language models to help drive more tailored content as you work on your marketing outreach or as you craft sales messages, it could be using that tool to help speed up a pretty lengthy account planning process for your major account teams. You can use these tools to help research potential company targets, any news that’s happening with those companies, strategic priorities, recent public events that people have spoken at that you might want to talk with as well and use that to create relevant account plans or messages that you work with those groups.
You can also go down the path of using these tools for coaching and development. We can record a sales call like this, load the transcript into the tool and say, hey, give me some feedback around how I navigated the negotiation process with that company as an example. And so use case number one is really around adopt some kind of an artificial intelligence tool based on generative AI so you can begin using it for a number of different use cases that might exist.
Use case number two. This would really be around kind of optimizing or automating a lot of the post sales call or CRM process. And you can start out more basic here, and then with integration, take it to a further level. But it would be in a meeting functionality or a meeting tool where you have the ability to record a session. Get that recording. Now translate that into a transcript. So now you’ve got a version of the meeting. Take that transcript, drop it into one of those tools and pull out the key takeaways. Those key takeaways can then be loaded into your CRM as an example, to speed up what would have been a manual process of taking notes in the past, logging into the CRM, loading that information in. Now working out what are the next steps. Take that same transcript and work out what are the follow up or action items that we have coming out of that particular meeting? The seller can then use that information for their outreach back to the person to make sure that that sales cycle keeps moving. We’ve got a number of clients that have been using this kind of functionality. One interesting piece that you might think of as an impact of using this tool would be, hey, we’re going to save our team so much time when it comes to we now don’t have to take the notes, load them into the CRM and do those follow ups independently. But what we’ve actually found is that in some cases, you might not have very good hygiene around a lot of those activities today. And so one of the real benefits that comes in here is really thinking about, now you’ve got a lot more documentation and rigor around that approach. Whereas you might have some scrappy notes in the past, now you’ve got a fully documented call that can be loaded where it’s going to be helpful for other teams as part of the planning process.
Item number three would be looking at tools that potentially help with product recommender systems. And so you might have a pretty broad product portfolio within your organization. And so you’ve successfully sold one, maybe two products to a given company. But what’s the next product that that given customer should buy? And certainly you can start discovering that process more naturally through sales calls, where they might be downloading information from your website, as an example. But where you can get pretty sophisticated is with these product recommender systems that then take in many more attributes about a given customer. So it might be what they have bought from you in the past. It might be how long they’ve been a customer. It might be how they’re using the given products that you’ve sold to them. It might also be how they interact with your website or other websites where you can understand a little bit more around the intent of what they might be looking to do. And so with all this information, this can be loaded into a tool that now comes up with recommendations based on that activity that that buyer has had in the past to now serving up a recommendation to a seller with which product should be positioned next. So certainly a lot of value when it comes to supporting a cross-sell strategy or driving growth within your existing account base. You can see a lot of uplift with some of these product recommender systems being leveraged.
The fourth piece that I’ll talk through here is really tackling a broader topic, which is an element of artificial intelligence called machine learning. And so you’ve heard a lot more recently around generative AI, which is the newer kind of more creative form of artificial intelligence. But machine learning has been around for decades. And really what that takes is a lot of clean, structured data with a series of inputs and outputs. And then the algorithm is essentially trained to start informing what an appropriate output might be from the information that you load in. And there’s a really broad array of applications that you can use for a machine learning algorithm. It could be something up front about how you forecast the business. It could be around how you prioritize different accounts. It could be on the back end for how you evaluate the health of a customer or come up with customer health scores as an example. Really, the whole idea of machine learning here is that you take a number of structured inputs and outputs, train that algorithm, so now you can get much better at predicting what the outputs might be in the future. Now, where this one becomes a little bit tricky is it does require data, generally a lot of data and pretty clean too. And so this might be one where you do need to make some investments around your tools, your data lakes, how you cleanse data so that you can start building this. But there’s a lot of very beneficial applications of machine learning that there’s certainly something that can provide a lift for your business as well. So that’s four different use cases, four real ways that you can start using artificial intelligence today to drive growth within your commercial organization.
