3 Weeks of Crickets
Hi all 👋
I've been thinking lately about indecision, and how differently I used to read it.
I was going through old folders on my computer and came across some notes from this one deal. It was some time ago. I had three meetings with the director, who seemed genuinely excited to work with me and even told me it just needed to go up for executive approval.
They asked for a proposal in a week and a half, and we delivered. Over the next 3 weeks I heard nothing, despite a couple of follow-up calls and emails. I even offered to fly up and take them to lunch to get better acquainted. Crickets on that too.
In the past, I used to read that as a loss to a competitor, but now I realize that almost all of them just sit there until they die. So I have stopped treating that as a character flaw in the buyer.
Indecision shows up when somebody has not been given enough to act on. You cannot commit to a number you have no basis for. You cannot approve a build you cannot picture. And honestly, that part is on us, not on them.
What's Happening in AI
I saw a benchmark this week on 158 B2B companies. Quota attainment actually dropped in 2026. 48% of reps hitting quota, down from 51% two years ago. And that's with AI tools more widely adopted than ever.
Reps at the heaviest AI-adoption companies hit quota 57% of the time. Reps at the lightest-adoption companies, 39%. So AI isn't lifting the average seller at all. It's just making the gap between the teams using it well and the ones that bolted it on a lot more obvious.
And "bolted on" feels like the right way to put it. Adding AI to a sales team isn't the same as adding it to a stack that already runs on trust and relationships. A tool can get you more activity, more data, more first drafts. What it can't do is build the thing a buyer is actually responding to when they say yes. That's still a person they trust.
So if quota attainment keeps sliding while adoption climbs, I don't think the tool is the problem. I think it's being asked to do a job that was never going to be its.
This Week's Case Study
You cannot fix a churn problem if your data only flags it after the person has already walked out. We worked with a university where the real problem wasn't the students. It was that the support team only found out once there was nothing left to do about it. They weren't preventing dropouts. They were processing them.
We built a model that reads historical and platform activity to flag the pattern weeks before a student disengages, not the moment after. It kept $500,000 to over $1 million a year in students who would have otherwise been lost.
If your systems only tell you what already happened, your people are always the last to know.
→ Read the full case study.
A Useful Resource
One question I get quite often is: "How do I know if a process is actually worth automating?"
That's why we built the Administrative Tax Calculator. It shows you how much you're paying premium salaries to do low-value work, chasing updates, manual entry, bridging gaps between systems. Before you hire another person, see how much you're already losing to broken processes.
That's it for this week.
— Robbie
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Hi all 👋
I've been thinking lately about indecision, and how differently I used to read it.
I was going through old folders on my computer and came across some notes from this one deal. It was some time ago. I had three meetings with the director, who seemed genuinely excited to work with me and even told me it just needed to go up for executive approval.
They asked for a proposal in a week and a half, and we delivered. Over the next 3 weeks I heard nothing, despite a couple of follow-up calls and emails. I even offered to fly up and take them to lunch to get better acquainted. Crickets on that too.
In the past, I used to read that as a loss to a competitor, but now I realize that almost all of them just sit there until they die. So I have stopped treating that as a character flaw in the buyer.
Indecision shows up when somebody has not been given enough to act on. You cannot commit to a number you have no basis for. You cannot approve a build you cannot picture. And honestly, that part is on us, not on them.
What's Happening in AI
I saw a benchmark this week on 158 B2B companies. Quota attainment actually dropped in 2026. 48% of reps hitting quota, down from 51% two years ago. And that's with AI tools more widely adopted than ever.
Reps at the heaviest AI-adoption companies hit quota 57% of the time. Reps at the lightest-adoption companies, 39%. So AI isn't lifting the average seller at all. It's just making the gap between the teams using it well and the ones that bolted it on a lot more obvious.
And "bolted on" feels like the right way to put it. Adding AI to a sales team isn't the same as adding it to a stack that already runs on trust and relationships. A tool can get you more activity, more data, more first drafts. What it can't do is build the thing a buyer is actually responding to when they say yes. That's still a person they trust.
So if quota attainment keeps sliding while adoption climbs, I don't think the tool is the problem. I think it's being asked to do a job that was never going to be its.
This Week's Case Study
You cannot fix a churn problem if your data only flags it after the person has already walked out. We worked with a university where the real problem wasn't the students. It was that the support team only found out once there was nothing left to do about it. They weren't preventing dropouts. They were processing them.
We built a model that reads historical and platform activity to flag the pattern weeks before a student disengages, not the moment after. It kept $500,000 to over $1 million a year in students who would have otherwise been lost.
If your systems only tell you what already happened, your people are always the last to know.
→ Read the full case study.
A Useful Resource
One question I get quite often is: "How do I know if a process is actually worth automating?"
That's why we built the Administrative Tax Calculator. It shows you how much you're paying premium salaries to do low-value work, chasing updates, manual entry, bridging gaps between systems. Before you hire another person, see how much you're already losing to broken processes.
That's it for this week.
— Robbie
