I’m an AI optimist, but I tread lightly when making grand claims about AI’s current and potential future value.
Why?
Just a few months ago, everyone was talking about how AI was going replace your entire SDR team with super efficient bots that never miss.
That kind of overpromising kicks off a frenzy that eventually settles into what’s truly possible.
This exact cycle has happened with the rise of fully AI-automated outbound systems aka “Auto SDRs.”
Unfortunately, so many of those promises haven’t panned out. Instead, we’re seeing an acceleration of the problem. More emails, more spam disguised as signal-based outreach.
When I posted recently about how AI is often just accelerating the spamming of bad SDRs, my comments section lit up.
Over and over, leaders reaffirmed what I’ve been saying from the beginning: AI can supercharge your best reps, but it can also be “spray and pray with lipstick on” as Shaun Crimmins put it.
Last week I talked about the real use cases in sales and RevOps workflows that AI can automate or augment.
This week, I’m diving into how to know when a potential solution for these use cases is a tool or toy.
The AI recipe for success
We know ignoring AI is not a winning long-term strategy for GTM teams. But we also know that trying to fully automate the sales process with AI “reps” isn’t working either. So, how do you find the middle ground and figure out which AI tools will actually help your team?
The way I’ve been thinking about it is whether the task you want to automate with AI fits the “recipe for success” - tedious + time-consuming + data-heavy + low creativity.
Let’s break it down by ingredient:
- Tedious: Is the task you’re trying to use AI for repetitive or slow? AI does well with repeating patterns and following simple instructions.
- Time-consuming: Do you spend a significant amount of time on this task each day or week? AI can give you hours back by cutting high-volume work down to a few clicks.
- Data-heavy: Are you trying to synthesize big quantities of data into easy-to-understand bullets or actionable tasks? AI can identify patterns, crunch numbers, and pull out key concepts in seconds.
- Low creativity: Is the task something that doesn’t require critical thinking or thoughtful wordsmithing? AI still produces results that read a little robotically, so if your task doesn’t require too much human nuance it’s a good one to automate.
I’m optimistic and betting AI models continue to get better and soon this criteria will change, but for now, it’s helpful to be realistic about AI’s capabilities.
People want to buy from people, but GTM teams are distracted by the hours of tasks in their day that fit this recipe perfectly. The tasks that keep them from actually talking to customers.
So while you can’t build a cutting-edge team of robotic sellers with a 100% closed-won rate, you can use AI as a force multiplier for your best employees and as a way to copy/paste winning strategies across your whole team.
Curious what that looks like in practice? Here are 3 examples of tasks that are a perfect fit for AI as it is today. (No jetpacks required. 😉)
#1 - SEO at scale
SEO is one of the best ways to build brand awareness and show up when your target audience is actively searching for a solution. The downside? Getting SEO must-haves like page metadata tags set up is high effort with slowly compounding reward.
These sorts of programmatic SEO tasks are perfect for AI, though. They’re the definition of tedious and time-consuming, and you don’t want to be too creative – you want to use the actual terms people will search for. Plus, using AI tools to quickly generate this formulaic content also lets you tap into data like top searches done in your industry without having to spend hours combing through search engine data and reports.
#2 - Deep account research
The best reps spend hours getting to know their target accounts. Historically this has meant combing through Google, reading news articles, searching for recent social media content from the brand and decision makers, and reviewing financial documents. Once that’s complete, reps dive into any existing internal documentation, like past outreach sent and old call notes.
This work is time-consuming and data-heavy, making it the ideal task for AI. (This is exactly why we built our new AI Strategy tool!) The right AI tool can quickly synthesize all of your internal data and publicly available information into need-to-know highlights. Give it additional information about your product and POV, and it can even help you identify some of your strongest business cases so your pitch is strategically customized to each buyer.
#3 - Alerting on customer sentiment
GTM teams need to constantly be monitoring customer interactions for indicators that an account is having a great experience and could be an expansion opportunity, or that they’re experiencing pains and might be at risk for churn. The old way of doing this was keeping meticulous notes by hand, citing anything important that came up on calls, in emails, or over Slack. The risk of something important being missed or overlooked was high.
AI can do consistent sentiment analysis and monitoring by looking for positive and negative keywords, auto-updating account health scores, and flagging any urgent items for immediate intervention. By handling the data, CS teams can focus on owning the actual human interactions to upsell or retain customers. Again, as with account research, AI should only be used for internal data aggregation, not for handling sensitive communications with customers directly.
AI evaluation matrix
Are you actively evaluating or deploying real AI tools? I’d love to hear from you! We’re building a helpful matrix that teams can use to separate tools from toys and looking for examples.
Here’s my ask: if you’re currently using an AI process or tool to streamline your GTM workflow, tell me about it.
I want to hear where you’re seeing success. I’ll share the feedback in an upcoming newsletter so we can all learn together.