Producing go-to-market work used to be hard.

We used to spend hours conducting research, crafting strategies, writing materials, analyzing data.
Not anymore.

We now have AI tools ready to do the heavy lifting for us.
They can research our competitors inside-and-out before we finish our coffee.
Distill our company positioning into a single line.
Draft a new blog post in any tone, length, and language we want.

This is a paradigm shift in our work.
We no longer do the work — we guide AI to do it for us.
Producing three times what we used to in a half of the time.

It's an age of easier work.

So how can we get ahead today?

As producing work gets easier with AI, orchestration of that work is becoming the king.

Productivity in the past required us to become great orchestra players.
We were rewarded for doing the research, writing, analysis better than anyone else.
Today, the game isn't about doing — but about orchestrating what's being done and how.

Our job now shifts to three crucial skills:

Shaping work: Defining and structuring work for AI-powered execution.
Assembling context: Getting AI to execute work with relevant, fresh context.
Maintaining focus: Staying on top of execution and keeping moving forward.

Shaping meaningful work, assembling relevant context, staying focused. That's the new moat.

But it comes with a problem.

Our current tools weren't built around this new moat.
They're shaped by old-world workflow, turning orchestration into a pain. Here's how.

The planning problem.

The tools where we keep work (Notion, Asana, Linear) were designed for human execution.
They optimize for tracking who's doing what — not for feeding AI the right brief to do it well.
They don't capture the direction, prompts, rules, examples AI needs.
So when we want to AI to do the work for us, we have to scope AI tasks from human tasks.
That's impractical, inefficient, and against the logic of orchestration.

The execution problem.

AI can produce brilliant work — but only with the right context.
Clear briefs. Current goals and strategy. Higher-level work. Past insights.
Yet, these artifacts don't live, or live in silos, or get outdated very quickly.
So for AI to execute well, we have to hunt and assemble everything from scratch.

The work management problem.

Finally, as AI tools make work effortless, they create a new issue.
ChatGPT, Claude, Sana, Jasper… they're all built for instant execution.
Draft, analyze, create anything. Every idea is doable, so we chase them all.
We jump between tools, spinning up action after action, losing rhythm in the noise.

This isn't orchestration. This is improvisation — effortful and messy.

AI promised efficiency, we got it, but we also got a new overhead.
We became a manual bridge constantly hunting, copying, pasting. Planning work for us, delegating parts of it AI, manually filling the gap. Painfully assembling context. Losing focus along the way.

So we stepped back. And built a thesis.

If we unite disjointed tools, we can finally start orchestrating.

By unifying planning and execution tools into a single system, something clicks.
We can plan the work — small tasks, bigger activities, long-term initiatives.
We can get it done — seamlessly, without leaving the system.
And we can finally see and control the flow of our work.

But that's not enough. Next, we must index each tools on its new-world job.

Planning must become prompt factory: creating clear briefs for AI work.
Execution must become context engine: auto-assembling knowledge for the work.
And both need to work together to keep us moving forward.

Finally, we must put AI all along the workflow.

Al that produces work is just scratching the surface.
The next stage is about deploying it across our workflow.
So it help us identify the right work, shape it, do it, learn from it, and iterate forward.

And that's how to go to market today.
Not with more hustle — but with a better orchestration.

That's what Questo is built for.