I've been building systems for most of my adult life. Early web platforms, personalization engines, MarTech stacks, RevTech infrastructure, sales enablement architectures, AI-driven GTM workflows. Across all of it, the constraint was usually obvious: we could only build as fast as our people could execute.
If you wanted more output, you hired more builders. If you wanted more velocity, you added engineers. If you wanted more coverage, you added reps. Production capacity was the limiter, and that assumption shaped org charts, hiring plans, budgets, and entire career paths.
And then, almost quietly, the constraint moved.
The Moment It Clicked
Recently, I read about an autonomous system that deleted a live production database. It didn't hallucinate. It didn't crash. It didn't go rogue. It did exactly what it was allowed to do. The failure wasn't in the execution. It was in the definition.
That story hit differently because I've seen this pattern before. Not with AI, but with humans. In GTM teams, we've always had this dynamic: if you give a vague playbook, you get inconsistent results. If you define qualification loosely, you get noisy pipelines. If you don't define handoffs clearly, revenue leaks. The execution engine simply amplifies whatever logic you feed it. AI just amplifies it faster.
That's when I saw it: the bottleneck moved upstream. What matters now isn't how fast we build, but whether we understood the problem well enough to specify a solution that won't break in production.
I've Watched This Shift Before
Every era of technology changes the dynamic of scarcity. What's in short supply and high demand becomes the new source of value. In the early web, distribution was scarce. Then attention became scarce, then data, then signal. Now execution is becoming abundant. Code gets written faster, content gets generated instantly, analysis happens in seconds, outreach can scale infinitely.
What exactly are we trying to accomplish? Under what conditions? With what boundaries? With what failure modes accounted for?
For most of my career, you could get away with imprecision because humans fill in gaps. They ask questions, apply judgment, catch edge cases. Machines don't fill in gaps. They operationalize them. And that forces a different discipline entirely.
This Is Changing My Own Work
As Head of Solutions, I used to think primarily about delivery capacity. How do we onboard? How do we implement? How do we support? How do we scale without breaking? Now the most leveraged part of my role is upstream of all of that. It's about encoding intent correctly.
What is a real buying signal versus noise? When does automation act, and when does it pause? What constitutes risk, and what constitutes momentum? Those decisions used to live in tribal knowledge, inside experienced reps, strong managers, and seasoned operators. Now, if we want AI to operate safely and effectively, that knowledge has to be articulated precisely. Not vaguely, not culturally, not in the realm of "everyone kind of knows." Explicitly.
The leverage moved from execution to specification.
This Shift Is Dividing Teams Into Two Distinct Camps
This shift is dividing GTM teams into two distinct camps. There are people who understand how to execute a motion, and there are people who understand how to design the system that determines when that motion should trigger, for whom, and under what conditions.
Execution skill is about running the play well. System design skill is about deciding what the play should be, when it runs, and where it stops. As execution becomes cheaper through AI, system design becomes exponentially more valuable in revenue operations.
This doesn't mean people are obsolete. It means the value stack shifts in GTM teams specifically. The ability to decompose a revenue motion into clear logic, anticipate edge cases in deal progression, set boundaries on automation, define what "qualified" actually means with precision, and design feedback loops that surface risk before it compounds—these capabilities are becoming premium in revenue operations. And most organizations aren't hiring for them yet. They're still hiring for yesterday's bottleneck: people who can execute volume.
The Real Risk Isn't AI. It's Vague Thinking.
The database deletion story wasn't about artificial intelligence. It was about under-specified authority. That pattern shows up everywhere. If you automate without defining escalation paths, you scale mistakes. If you apply AI to messy data, you scale confusion. If you let systems act without clear constraints, you scale risk.
The speed of execution is no longer the dangerous part. Ambiguity is. And ambiguity at machine speed compounds quickly.
Why This Actually Energizes Me
Here's the part that feels optimistic. When execution becomes cheap, we get to spend more time on design. On architecture, on thinking, on shaping how systems behave under pressure. For someone who's always cared about systems thinking more than surface features, this is exciting.
It means we can stop obsessing over manual throughput and start obsessing over structural integrity. We can ask better questions: What should this system actually optimize for? What tradeoffs are acceptable? What decisions require human judgment? Where must autonomy stop?
That's a deeper level of work. Not louder or flashier, but more durable.
When Execution Is Cheap, What Creates Leverage in Revenue Operations?
From where I sit building revenue systems, what's in short supply, and therefore valuable, is the ability to think clearly about GTM motion design. Specifically: knowing what constitutes a real buying signal versus noise, understanding when automation should act and when it should pause, articulating what makes a deal risky before the forecast call, defining handoff logic that doesn't leak revenue, and designing systems that surface the right context to the right person at the moment it matters. Those skills were always valuable. They're just no longer optional.
The constraint moved. And if you've spent years building and refining systems, you've probably felt it too.
The future advantage won't belong to the fastest executors. It will belong to the clearest thinkers.
About the Author
Chris Zakharoff has joined GTM Engine as Head of Solutions, bringing more than two decades of experience designing GTM systems that integrate AI, personalization, and revenue operations. He's helped companies like Adobe, Cloudinary, Symantec, Delta, and Copy.ai bridge the gap between R&D and real-world revenue impact by leading pre-sales, solution design, and customer strategy for organizations modernizing their stack. At GTM Engine, Chris is helping define the next generation of RevTech, where real-time orchestration, AI-powered workflows, and personalized engagement come together to transform how companies go to market.
