Rewiring Revenue: Carly Crittenden

Carly Crittenden, who is set to speak at Revenue Brew’s upcoming event, shared what she believes are the biggest opportunities in AI for sellers.

What’s the biggest misconception sellers have about AI?

One is that it slows them down, and the other is that it solves all their problems. I found that folks live in two camps, and I think a lot of people are actually really afraid of AI; not afraid that it’s going to take their job, but just don’t have the mental capacity to slow down and think differently to leverage it.

They’re scared to say they aren’t leaned in, and so they’re sort of faking it, or dabbling here and there, or using it in small use cases, but not using it in ways that really transform their business. I was speaking to someone this morning who said, “I don’t have time to use AI,”which is a bit ironic, but it’s a pretty consistent trend. The other is that it is going to solve all of your problems, and the application then becomes a very lazy use. The number of times I’ve been prepping for a meeting, and a seller’s like, “Hey, here’s what I’m going to present.” I’m like, “This is obviously Claude. It is not branded; it has not been pre-read.” I have [interviews] with candidates who send me something that is clearly not edited. It requires thoughtfulness and intentionality, and both of those use cases don’t really appreciate the need for the extra work required in order to make it actually work for you.

It’s not going to solve all of our problems, and it’s not actually more work in the long term, but the need to do something a little more intentionally in the short term is very real…You can’t use the same decision framework that you use when you’re working in a fast-paced tech startup to effectively leverage AI tools. You have to actually turn the problem inside out. You have to stop; you have to think about how to tell it what you want it to do.

Where do you see the most resistance when teams try to adopt AI into their revenue workflows, and how do you move past it?

Individuals experience resistance based on what I just said, which is: “It takes me more time. I don’t actually care to use AI. I don’t want to use AI. I’m not deeply curious. Can someone else build it and do it for me?” Sometimes it’s solving the enablement piece really quickly and finding your champions. There’s not one way to do it, but there’s lots of good ways to do it.

The organizational resistance is fear around data sharing…There’s a gap in education of what leaning into sharing your data with an LLM means and what the actual risks are to your business versus what aren’t. We are a startup; we move really fast; we need to be really scrappy.

We sometimes find ourselves with our head of compliance feeling like we’re in a big corporation, so I think there’s organizational friction, depending on risk tolerance, risk assessment, and just knowledge and information about what you’re actually doing that can slow organizations down. The more we can get educated—particularly the folks that are enabling the sales and commercial organization to leverage the tech—allows you to beat back the friction and be more agile.

If you had to pick one AI use case that’s delivered the most measurable impact on your team’s performance, what would it be and why?

I have a leadership use case and a team use case. I find that some of my top performers have built themselves assistants. We’re in an age where, especially at a startup, the workload is insane. The number of things that have to get done is more than the number of hours, but there’s an expectation that we figure it out because we know we have AI and we have tools and technology, so we’re not hiring at the pace that there’s a need because we want to try to lean into technology. The problem is we haven’t totally enabled that to happen, and so people just feel more without the unlock. Where I’m seeing top performers—and some of my peers and close friends in the space—get the unlock is building themselves personal assistants, integrating all of their various forms of communication, their known workflows. They get a readout in the morning: Here’s what you need to prioritize.

Sales leadership is one of the most high-touch management jobs in an organization because the coaching and the complexity, the deal, the nuance, the communication, the multithreading, it’s a lot to think through and solve. Historically, we’ve just hired people to run sales organizations who are really good at detecting and identifying that and navigating that. I don’t think that human element has gone away, but I think our ability to get to that next email to maintain deal control has accelerated tremendously with AI. The use case that is a meaningful unlock for us is we’ve partnered with a consultancy called Project Baku, and they have built an app that ingests a ton of the information we have as an organization, but layers in our sales playbooks…and basically has built a deal management and deal strategy brain specifically for Vidmob, and how we do deals that allows us to interact with this agent and produce the next email, given all the risk on the deal, the stage, what we know, what we don’t know.

My sellers that have leaned in and embraced that in a self-serve way have been able to move three times as fast as the rest of my sellers. Additionally, they’re putting the biggest numbers on the board, and it’s not necessarily because they have better instincts. It’s because they’re using tools to be able to do more, faster. Therefore, they can handle a bigger pipeline. Therefore, they can deliver more revenue. It doesn’t exist independent of coaching. I’ll still look at the email and tweak it, or we’ll workshop it together, but it’s allowing them to get the answers and the expertise much faster than they would have on their own.

How do you think about the balance between AI-driven automation and maintaining authentic, human-led relationships with prospects and customers?

At the end of the day, selling is a very human experience. People buy from people; people buy from people they love; people buy from people that are going to teach them something and solve their problems. Sometimes it’s because of the company they work for; sometimes it’s because the person is really smart and knows the industry and is really talented. I don’t think that’s going anywhere. I think that’s still always going to be true; we are such relational creatures.

When we can lean in and automate the insights and surface the most important things more quickly…or do some of the leg work that is less human and less strategic, in a more scalable way, that’s how you really lean into it.

What does a revenue team that’s truly AI ready look like?

The first is there’s a shared understanding of what AI is and isn’t for the organization. That doesn’t mean everyone uses it in the same way, but everyone agrees that it’s not here to take my job. It is actually meant to enable me, but there is still this human element, and there is still this work, and there is still this commitment I need to make in order to get the unlock for my business.

The second is really strong enablement in the organization. Different people are at different stages of that journey. There’s various levels of trepidation and fear for different reasons, and if you can unlock a few use cases or a few individuals, champion them, and then put infrastructure enablement behind them, everyone can benefit from their creativity and curiosity. You’ll move much faster than asking everyone to do the lift on their own.

Third, the devil’s in the details, in the data. The data tells you: Are you moving deals faster through the pipeline? Are you doing bigger deals? Is more of your team on the board every month, every quarter, because we’ve made it possible for them to be more successful through the leveraging of the tech and the tool? You have to measure it.