⚙ Hi {{first_name_tally|Operator}},

Thanks for filling out the survey!

A few things jumped out.

The biggest thing you're wrestling with is AI. 26% of you are trying to get it to actually work in your workflows. 17% are being asked to build an AI strategy for the org with no playbook to copy. And 24% are watching processes crack as you scale.

When I asked what would be worth your time, 44% said frameworks you can steal and use on Monday. 24% want teardowns with real numbers.

Got it. More soon.

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Last year, I sold a company. Which meant my job for a few weeks was reading every contract we'd ever signed: sales, vendor, NDA, lease, the weird one-offs. All of it had to turn into a packet a buyer's lawyers could tear through without sending me fifty follow-up emails.

My paralegal for that was Claude Code. I fed it the contracts in batches against a stack of checklists and a goal, and it worked through the night, missing things, catching things, trudging through the work in a way I couldn't have. I reviewed everything it produced. It literally saved me hundreds of hours.

That's the part everyone wants to talk about.

The part nobody wants to talk about is why almost no company can do that at the org level, even now.

AI adoption isn't a technical problem. It's an org-alignment problem wearing a technical costume.

My diligence agent worked because I had a goal, a pile of contracts, and a checklist. One person, one project, one set of files. Whatever context I remembered to feed in, the agent knew. Everything else was invisible to it. Someone two desks over may have their own agent doing something adjacent, and the two agents will never meet or collaborate.

That's where almost every company is right now, including ones you'd assume are further along. Individual people getting superpowers. The org getting nothing.

Then there's Ramp.

Every Ramp employee gets an internal AI suite called Glass auto-configured on install. One click, Okta sign-in, every internal tool and data source wired in. A CX engineer builds a Zendesk skill that pulls ticket history, checks account health, and suggests a resolution path. She ships it to an internal marketplace they call the Dojo. By the next morning, every rep on the support team has her skill running on their own queues.

What one person figures out on Monday, the rest of the team has running by Tuesday. More than three hundred of these shared skills exist inside Ramp today, versioned and reviewed like code.

The plumbing is solvable. What actually makes Glass work sits further upstream: clarity around the mission/vision, the ICP, how the product behaves at the edges (written down, not off vibes), etc. Every agent starts from there. Teams add their own context as they go.

Which brings us back to the costume: getting AI agents to work well is not a technical issue.

If the context you feed your agents is vague, contradictory, or half-written, every agent downstream inherits the vagueness. You end up with a hundred small confident agents working in slightly wrong directions. That's worse than no agents at all. At least the humans would have asked a follow-up question.

"We want to build an agent platform" is almost never what a company actually needs. What they need first is a written ICP that the CRO and the Head of Product both believe. A positioning statement someone can actually remember. Someone has to sit down and map how Sales, CS, and Product hand off to each other, with the awkward parts named out loud.

You can't buy your way past this. Do the hard (sometimes boring) work.

Somewhere right now, a Ramp support rep is asleep while his AI agent clears his overnight queue.