In our last issue, we were talking about how a process that is built in an AI-native way is very different from a traditional process. We thought it would be helpful to illustrate what this looks like in the form of a case study from a client we recently worked with. This client runs a commercial team that handles a high volume of account calls every week. Before we worked with them, pre-sales call prep looked like this: a rep opens Salesforce and pulls whatever notes exist from the last call (if the last call was logged at all). Often, the information wasn't well documented, or it was logged three weeks late by someone reconstructing it from memory. Often, reps would also check documentation in other locations like personal spreadsheets, a notebook, or whatever side system they used. If the account has a service history, that meant a separate message to the service team to ask whether anything has been serviced recently. A little research on what's changed at the account through LinkedIn Sales Navigator rounds it out, though how thorough that gets usually depends on how much time is left before the call.

From those scattered pieces, the rep builds a pre-call plan. Once the call happens, the rep then has to reconstruct what was said, from memory, and get it back into the CRM before it's forgotten or blurred with the next three calls of the day. What got logged depended on what the rep remembers and how much time they have before the next meeting. None of this is a knock on the rep. It's what pre-call and post-call prep looks like when the process was built around what a person can hold in their head and complete between calls.

We rebuilt the pre-call plan and post-call coaching and follow-up around what good sales management requires, using tools that already existed. We started with an analysis of their CRM: What did good sales process look like? What types of follow-up and coaching, at what intervals, lead to BoB (best of best) closed-won activity. From there, we build the AI native process. A pre-call briefing skill in Claude (connected to their CRM, service records and market intelligence database) pull the account's history, the last several interactions, and any relevant intelligence. The skill (a repeatable process encoded into Claude) builds a pre-call plan structured to the seller’s preferences and the aligned organizational sales methodology, not a generic template. Granola (An AI-native call transcription tool) captures the call itself, so nothing depends on memory.  Immediately after the call, a coaching and wrap-up skill (another one of those encoded processes) runs against the recording. It gives the rep three things:  real-time coaching on how the call went, a complete, structured record that is approved by the seller and then saved into their tasks and the CRM and finally, it drafts any follow-up emails (to the customer with next steps, or to the internal team on something they need support with). Now, what used to happen during “office hours” on Friday or at 6pm happens immediately after the call. Nothing gets lost, and the seller is able to focus on everything the AI shouldn’t do.

The rep still runs the call. The rep still makes the judgment calls: what to say, how to read the room, when to push and when to let something sit. What changed is everything around the call. The pre-call plan is no longer built from whatever the rep can piece together in fifteen minutes. The post-call record no longer depends on what they remember at the end of a long day. That's the difference between bolting AI onto the old process and building the process around what AI can actually hold.

This is what we meant last issue when we said AI-native process development flips who's actually running the process. Not the call itself; the human owns that. Everything that used to eat the time and attention a good call actually deserves can be owned by the AI tool.

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