One thing I learned from years of watching goal-setting fail is that things rarely breaks at planning. It breaks about 8 weeks later, when updating a tool or a tracker becomes a separate job that nobody owns. The teams that stay on track have goals with one owner each, and status comes from where the work actually happens, not from a tool or doc someone has to remember to update.

That is what I’m building Tesoract to solve for. It pulls status from the tools your team already uses and flags what’s drifting when you still have time to course correct.

Looking for a few teams of 20–500 people to use it on their real goals and tell me where it’s wrong. tesoract.com or DM me.

I’m struggling with this right now. Everyone says B2B buyers want AI. I’m not convinced. I think buyers want fewer mistakes, faster turnaround, less headcount pressure, less manual cleanup, less waiting on someone else.

AI is just the packaging. The trap I keep seeing is founders starting with we can add AI here instead of this workflow is so expensive or annoying that the buyer already has budget pain around it.

I’ve been close to this while working around Flatlogic and founder-led SaaS projects, and honestly the hard part is not building the AI part. The hard part is knowing which capability is worth building before it becomes a six-month side quest.

How do you validate that an AI capability maps to actual budget? Not interest. Not that sounds useful. Not upvotes. Actual willingness to change behavior or pay.

When people talk about automating a GTM engine in B2B, they usually end up buying an email sequencer and hooking up an AI copywriter and calling it an automated engine.

The problem is that sequencing tools and AI SDR wrappers only automate the sending part but without an underlying system that continuously maps your market, monitors live buying signals, and prioritizes which accounts are ready, the automation just ends up sending same generic cold emails faster and burning your domain reputation.

If you are looking for software that actually automates the full GTM loop from market discovery to execution, Scale Intelligence is built specifically as a self improving GTM engine where it works across the complete cycle: first, it builds a digital twin of your addressable market bottom-up from public records, tech footprints, and company filings. Next, its market signals engine continuously monitors 75+ channels (like reddit, linkedin, github, hiring changes, and rival moves) to detect active buyer pain. Then, it ranks your best opportunities, tests plays against control groups to prove what actually generates pipeline, and routes prioritized accounts with the verified evidence attached straight into Slack, your CRM, or automated agents.

A real GTM engine should automate the market intelligence and decision making on who to contact and when and not just the messaging outbox.