If you feel like AI is everywhere, but you don’t know how to make it valuable, you’re not alone.
I’ve seen 20+ pitches for AI sales tools that supposedly can replace your entire team, but the benefits feel murky. Tools that are supposed to fully automate the sales motion actually need a fair amount of hands-on setup. Buyers can spot an AI email quickly (and don’t love it!), so GTM teams are left with fairly empty promises.
Despite this, we know that AI is here to stay. We also know that AI can excel at making tasks & research faster and better. So, at Pocus, we’ve been focusing on building to AI’s strengths.
We’ve been in stealth mode on some very exciting new AI features in the product, which means me and some of the core team get to put our customer research hats on.
I’ve spent the last several weeks talking to sales leaders at every type of SaaS business, from our PLG customers to more traditional sales-led orgs and everything in between.
Which brings me to this week’s newsletter topic from now until the foreseeable future:
AI x GTM
Every sales leader I spoke to is understandably confused. Where is AI mature enough to apply? How should I be thinking about AI tools in our tech stack? Is AI actually good at anything yet?
I’ll be unpacking all of this over the next several weeks. This week I want to look at all the real use cases where you can operationalize AI in your stack today versus what might actually just be a toy.
Before we dive in, I wanted to share a quick update on that AI product we’ve been stealth building. We're announcing it later this week, so make sure you're following us on LinkedIn to be the first to hear the details.
AI tools vs. toys
You’ve probably heard the saying “is it a vitamin or a painkiller?” Well, for AI I’ve started to say, “is it a tool or is it a toy?” The vast majority of AI products aimed at go-to-market teams are still in the toy phase of maturity. They’re great for experimenting and have some fun features, but aren’t quite ready for primetime.
A great example is automated AI-generated email. A fully autonomous email cadence written by AI using a few data inputs sounds good in theory, but I’ve spoken to very few teams who have successfully implemented this at scale without errors.
The biggest challenges with applying AI to your go-to-market today are three-fold:
- Lack of clarity on use cases - which workflows can AI actually help automate or augment?
- Lack of clarity on maturity for those use cases - is this still a toy or can it be operationalized?
- Lack of clarity on what tools exist to help - which products actually do what they promise?
In this newsletter I'm focusing on #1 - the use cases AI can help with today.
GTM cases x AI
Sales teams do a lot. You’ve probably seen the stat that 70% of a rep’s time is spent on non-selling tasks. This is the most fertile ground to start applying AI. I looked at sales and RevOps "jobs to be done" and then assessed where AI could fit in.
Sales
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#1 Prioritize book of business: Before AI, reps had to synthesize data about accounts themselves, making a call about where to focus. Now you can use AI to analyze all of the data you have about an account's activity (1st and 3rd party data) to prioritize where reps should focus their time.
🔮 How Pocus helps: Know when, who, and how to engage the best opportunities with AI-powered prospecting, research, and optimization in Pocus.
#2 Research & plan: Historically, the best reps might have spent hours learning what matters to a prospect or account. Those in scaled teams probably skipped this step entirely because it was time-consuming and not intuitive. With AI you can easily become an expert in every account. AI can synthesize more data across both internal and external data sources to give reps a perfectly tailored account plan.
Use AI to speed up the process and find out:
- What they do
- How they make money
- Which products they offer
- What pricing & packaging looks like
- Any major news
- Who top competitors are
#3 Prospect: Before AI, reps would spend hours finding contacts within an account or resort to spray & pray rather than trying to find the best contacts. With AI having all of the context, it’s easy to take out the guesswork and suggest the right buyers. AI can use the key signals you define to pinpoint the right buyers based on previous product usage, marketing engagement, or even just their LinkedIn activity.
🔮 How Pocus helps: Pocus' AI Prospector surfaces the best paths into an account based on network connections, intent, and fit. Use this data to find the best contacts and create higher-quality personalized outreach.
#4 Create personalized content: Personalization of content has been complicated for reps. Often only worth it for the 1 or 2 whales in their book. Not just talking about basic email personalization but sharing collateral, business cases, and more deeply personalized to their needs. With AI, you can easily personalize content at scale, embedding the needs of the buyer into everything from emails to an ebook.
#5 Manage deals/pipeline: No seller enjoys updating pipeline or any of the other sales management tasks. If you asked any seller if they want an assistant they’d say hell yes. With AI you can offer assistant-like capabilities to every rep without the cost, starting with the most painful exercises like consistently updating Salesforce.
🔮 How Pocus helps: Quickly update your account records in Hubspot or Salesforce with the click of a button in Pocus. You can even configure Playbooks to handle sales management tasks as part of a complex workflow.
RevOps
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#1 Identify target accounts: Before AI, RevOps had to dig into historical data and run account analysis to find their top target accounts. Now you can use AI to quickly recommend the accounts most likely to purchase, identify expansion opportunities, and flag accounts at higher risk for churn.
🔮 How Pocus helps: Pocus captures every important signal across the life of a customer from the first touch with your brand to the moment they renew, then uses AI to analyze that data and identify which accounts to focus on first.
#2 Enrich accounts with custom signals: Before AI, you were beholden to the rigid intent signals provided by data vendors or ABM tools. With AI you can scrape the internet and find custom signals that are specific to your business. For example, sell cybersecurity software? Find mentions of accounts who’ve suffered breaches in recent news articles, podcasts, or blogs.
🔮 How Pocus helps: Enrich user and account profiles with an all-in-one data solution that covers dozens of 3rd party enrichment providers and Pocus' custom AI scraping.
#3 Playbook experimentation: Experimenting with new playbooks used to depend entirely on your RevOps team doing deep data analysis and stitching together reports. Launching new experiments could take weeks. With AI, RevOps can more rapidly experiment with AI-suggested playbooks. AI can look at historical conversions to suggest which signals matter most. RevOps can then build these playbooks, launch with the team, test, and repeat.
🔮 How Pocus helps: Pocus Predicts AI goes beyond traditional scoring, looking at the entire customer journey, and recommends the Playbooks your team should run to hit specific goals
There are lots of tools that can help with the jobs to be done above. We’ll get into more of those details next week! In the meantime, if you want to see Pocus' AI tools in action, request a demo.