Billions of dollars are spent on at least 30 different categories of tooling, and you could very well pick a category and try to build an AI-powered version of something that exists. Sure, an incumbent could stand up a team and build in AI natively, but there’s a reasonable chance you could move fast enough to displace them. A lot of these tools weren’t built by product-forward teams; it’s sales SaaS after all.
Sales process today
A better way to approach ideation is to dive deep into the sales process. It’s mostly human-driven today, and highly variable depending on the size of the company, the customers they sell to, etc. The flowchart I made below is based on my view on the process, so it won’t necessarily apply to the group you decide to sell to. Make it an exercise for yourself to create your version of the flow *after* you have a few dozen customer interviews or case studies under your belt.
Now that you’ve got both the eagle eye perspective (via market map) and an on-the-ground view (via sales process), you can combine the two to see which parts of the sales process have ample tooling right now. From here, there are two approaches: You could choose to go where the tools form clusters, or you could try to tackle greenfield areas. Let’s first look at the existing tools and how AI might disrupt them. I’ll focus only on the highlighted areas.
Prospecting tools - The ultimate infinite feedback loop
Find the “global minimum” of messaging, ie what types of messages work for the different personas you’re hitting up? What does the perfect sequence look like, step by step? The ideal tool doesn’t even need to consider lead generation. As long as the BDR can input a spreadsheet of contacts that fit within a certain persona, the AI tool should be able to segment out that list, test out different combinations of sequences, and iterate every day, every week, or at whatever cadence works.
It’s way harder than it sounds, because sequences often touch different channels (LinkedIn, Twitter, email, call, etc) and can be anywhere from 1 to 10 steps. What is important is that AI automates this process of finding the true message fit. Every cycle it should be looking at past cycles’ response and meeting booking rates, and using that to inform the formation of future sequences. It’s a continual process and there won’t be a definitive answer to “what is the perfect sequence,” but at least this tool would save 10-50% of a BDR’s time, which can be diverted to live qualification calls and smarter activities.
ABM - Campaigns on autopilot, also on infinite feedback loop
Similar to the above, it’s easy to generate a target list of companies and contacts, but hard to test out and align messaging for those big accounts. There’s huge opportunity to make the process of ABM more scientific, from the sequence of personas to touch (ie is it better to reach out to the product manager first, or their technical counterpart?) to the channels to prioritize (should Big Buyer see your Google Search ad before they get an email?)
Data enrichment - Fresh data that triggers actions
An ideal AI qualification tool should be deeply integrated with data enrichment APIs and real world news and context data. Being able to detect these changes and run automated qualification and handoff to BDRs would be key. This is assuming the lead was not qualified on a call and/or qualification needs to happen via email.
Marketing automation - Smarter lead nurturing
Similarly, AI-powered lead nurturing would be much smarter than current BDR response templates. Being able to tap into the sales team’s collateral and the information that’s available on the company’s website would help train AI on what information to surface when progressing through nurture phases.
Call analysis - Live, on the fly direction
Existing call recording tools are really only good for retroactive analysis and coaching. There’s space for AI-powered live call assistants for sales teams. It should be able to provide real-time insights and recommendations (perhaps via Slack pings) during product demos and customer interactions.
Sales enablement - Universal search bar
AI can help sales teams quickly access relevant information, resources and training through better search across tools. Currently the bottleneck is in direct integrations between tools, ie outbounding tools sometimes don’t surface all interactions into the CRM, where a rep might be looking prior to a call. There are interesting apps like Scratchpad that are essentially like Superhuman for Salesforce, but the ideal tool is a pure search bar for the sales stack. Something like Commandbar or Slite’s (YC W18) new Ask product could work.
Revenue operations - Make data queries in plain English
All great sales leaders have a great revenue operations counterpart. Having a tool that can better automate forecasting and sales analytics would drive better results for sales teams. This might look like the AI assisted SQL query tools that are cropping up.
Greenfield opportunities exist because there are areas that can’t be productized without AI.
Post-human call execution - Why do we even need humans on sales calls?
Not to sound too Black Mirror, but there’s a world where software buyers no longer need to interact with sales reps. Maybe you feed AI the headshots of your existing sales team or it generates its own sales reps, or in the most dystopian version, sales calls are just run by a pulsing white orb… There are so many ways we can cut down the time we spend on qualification and discovery Zooms. Obviously though, this type of tool isn’t for everyone, certainly not for the pure PLG (product led growth) crowd. And more importantly, for this to exist, we need to assume a world where building human rapport no longer matters / produces an edge.