Jai Toor on outcome-first GTM systems
Jai Toor's April Tools Day talk argues that GTM teams should stop overdesigning workflows. Define the outcome, the evidence, and the checks, then let the system find the route.
Jai Toor's talk starts with the question almost every modern GTM team answers the same way:
Have you ever used more than three tools in one workflow?
Most hands go up.
Then comes the line that matters:
"All your time is just pushing data between different tools and you don't actually remember what you were doing in the beginning."
That is not just a complaint about messy ops. It is the whole diagnosis.
The workflow gets so elaborate that the original goal disappears inside it.
The talk is not anti-automation. It is anti-step obsession.
Jai walks through a familiar GTM pattern: competitive social listening.
The old version sounds responsible:
- define the competitors
- gather the signals
- scrape the posts
- route the data
- push the outputs into the CRM
- manually qualify what comes back
It feels rigorous because there are many steps.
That is exactly the trap.
By the time the workflow is assembled, the team has spent weeks preserving the process instead of testing whether the process was even the right one.
Jai's point is not "do less thinking." It is "move your thinking up a level."
The important move is defining the outcome and the evidence
The central claim in the talk is simple:
"You should start with the outcome and let the system and the agents and the tools find the route."
Then he sharpens it:
"The more micromanagement you do of the process, the worse your outcomes will be."
That makes people uncomfortable for a good reason. The old model rewarded detailed control. If you could map every step, you looked rigorous.
Jai is arguing that this instinct now hurts more than it helps.
The operator's job is not to predesign the route. It is to define:
- what a good result looks like
- what a bad result looks like
- what evidence the system should use
- what constraints actually matter
That is still rigor. It is just higher-leverage rigor.
The Vanta example makes the abstraction concrete
The best part of the talk is the demo framing.
Jai asks Deepline plus Claude Code to find two audiences:
- people engaging with the CXOs of Drata
- people mentioning competitors like Delve and Hyperproof on LinkedIn
Then he specifies the desired outcome:
qualify the leads and push them into a Lemlist campaign.
That is the full brief.
No giant process map. No seven-tool architecture diagram. An outcome, some evidence, and a destination.
From there the system can generate the plan, collect signal, inspect the outputs, and adapt as it goes.
That is also why Jai keeps emphasizing examples and checks. Outcome-first work is not vague prompting. It is a stricter loop:
- define the target
- provide evidence
- inspect the outputs
- tighten the system
The deeper product point is about what actually matters now
One of the strongest moments in the talk is when Jai explains that Deepline had built more dashboard-heavy agent UIs, then watched customers who lived in Claude Code stop logging in.
That is a painful observation. It is also an important one.
If agents become the main interface, the durable value shifts underneath the interface:
- the data layer
- the provider integrations
- the task-specific skills
- the memory of what has already been done
That is why Jai says:
"If the world is just going to be agents accessing your information, what still matters? It's the data that it's accessing."
That is the real thesis under the product.
The UI can change. The context layer cannot.
The takeaway
The best line in the talk comes at the end:
"You should not have to think about how the sausage is made."
That line only works because the rest of the talk makes clear what has to exist underneath it:
- skills
- providers
- backtesting
- examples
- checks
- stored context
This is not a call to ignore systems. It is a call to stop confusing workflow assembly with strategic thinking.
The operator should spend more time on the business problem, the definition of a good result, and the evidence behind it. The system should spend more time exploring the route.
That is the real shift.
If you want to go deeper
For the full event context, start with the April Tools Day recap.
If you want to work this way directly in Claude Code, start with Deepline.
More talk breakdowns from the same event:
- Ben Holley on outbound from the terminal
- YG Hong on shipping lead scoring without knowing ML
- Garrett Wolfe on why cookie-cutter GTM is dead
- Shashank Khanna on revenue as a state machine
Start with the outcome. Let the route emerge.
Deepline is built for GTM teams that want to work through goals, evidence, and checks instead of rebuilding brittle workflows by hand.