For companies at the bleeding edge, unfettered growth remains the goal. The OpenAIs and Anthropics of the world are growing so fast that nothing else matters. When you’re setting growth records, you can keep on hiring, expanding, investing. But for companies not on record-setting growth trajectories, the spotlight has shifted to efficiency. Boards are asking how every dollar, every effort, and every tool ladders up to growing efficiently.
For most of us, AI is a big part of the answer, and it needs to be. We’ve all seen how much more efficient AI can make us and our teams. But the truth is that AI is still underutilized and coming of age. According to McKinsey, more than three-quarters of organizations use AI in at least one business function, but only 21% have fundamentally redesigned any workflows around it, which is where the real impact shows up.
Sure, content teams are shaving off a few hours a week using Claude to turn blogs into LinkedIn posts. Customer success teams are writing emails in 5 minutes instead of 15, thanks to ChatGPT. These incremental gains are happening across our organizations.
But each of these wins amounts to crumbs, while our stakeholders expect a whole damn cake.
Boards want to see the kind of structural efficiency that delivers measurable margin expansion. According to Bain’s 2025 Technology Report, early AI leaders are already seeing 10 to 25 percent improvements in EBITDA by scaling AI across core workflows — proof that significant impact comes from redesigning how work gets done.
Redefining Efficiency Before It’s Redefined for You
The way I see it, go-to-market leaders have two choices: define what efficiency means for our business — or have it defined for us through hiring freezes, budget cuts, or layoffs.
The trap is thinking efficiency equals elimination. It doesn’t. If all we did was cut without growing, we wouldn’t make anyone happy. We need to focus instead on workflow design, as noted in the McKinsey report. The goal is not to do the same work with fewer people — it’s to remove friction so that your best people spend their time on high-value activities that drive growth.
Here’s how I break it down:
Define Efficiency in Your Terms.
At 6sense, I’ve always benchmarked efficiency as marketing spend from two quarters ago versus pipeline generated now. It’s a modified “magic number,” but the logic applies anywhere: how much output are you getting from your inputs over a realistic cycle time?
Every company’s equation will look different, but it has to connect investment to revenue, not to volume or vanity metrics.Find the Inefficiency.
You can’t fix what you don’t map. Look at a single workflow — say, the path from signal to meeting. You’ll find inefficiencies everywhere: data gaps, delayed follow-up, missed handoffs, clunky scheduling.
In one recent exercise, my team found more than 30% of the total process time was wasted on steps that didn’t create value. Thirty percent! That’s the cake — if you redesign the system, not the staffing plan.Fix the System, Not the Symptoms.
Tactical automation is good, but orchestration is the end goal. Think about collapsing steps, not just speeding them up. Compress sales cycles by aligning signal detection, scoring, enrichment, and engagement into one flow.
That’s when efficiency compounds — and that’s when the board starts paying attention.
Incremental Productivity Is the Starting Point — Not the Finish Line
Incremental productivity isn’t a bad thing. In fact, it’s where many of us are right now in the generative AI lifecycle.
We’re in the experimental stage — testing, learning, building confidence. We’re seeing small productivity gains in a lot of places, and that’s awesome. Those early “crumbs” matter, because they teach us what works and build momentum for the bigger changes ahead.
I think the key is not to stop there, but to think more deeply about what’s possible and then double-down on making it happen.
Productivity and efficiency aren’t the same thing, but they’re connected.
Productivity speeds up what you already do.
Efficiency reimagines how you do it — or whether you should be doing it at all.
We need both. The small, measurable productivity wins prove the value of AI in real workflows. Over time, those learnings compound into the kind of structural efficiency that redefines roles, collapses handoffs, and unlocks entirely new capacity.
That’s how crumbs become cake.
When your board asks about AI ROI, they’re not just asking for cost savings; they’re looking for evidence that today’s pilots are laying the groundwork for tomorrow’s transformation. They want to see a plan for scaling what you’re learning for bigger gains
As GTM leaders, it’s up to us to connect the dots from early productivity to enduring efficiency. That’s how we turn today’s experimentation into tomorrow’s enterprise impact.
Our AI experiments are all necessary. They build confidence, fluency, and trust in the technology. Those are the crumbs that will eventually lead to something bigger.
The Leadership Imperative: Reinvent or Be Redefined
Reinvention isn’t optional right now — for companies or for leaders.
AI isn’t certainly changing how we work, but more importantly, it’s redefining what “working efficiently” even means. In every boardroom, the conversation needs to shift from “How can we be more productive?” to “How do we reimagine the system?”
That shift is forcing leaders — myself included — to confront a harder truth: we’ve spent the last decade optimizing for scale, not redesigning for leverage. Now, we have to rewire how value even gets created.
And to be honest, that can feel overwhelming. But as with all big changes, we can either treat this AI wave as something happening to us — another round of tools to absorb — or as an opportunity to get excited about the new possibilities it creates.
That’s the real mandate we’re facing right now. Reinvent how we’re running go-to-market in this AI-driven world:
Take control by defining efficiency.
Redesign roles and workflows around outcomes, not activities.
Reimagine our teams as systems of leverage, not lines on an org chart.
Crumbs That Become Cake
Our AI experiments — the pilots, the early automations, the 10-minute wins — are all necessary. They build confidence, fluency, and trust in the technology. Those are the crumbs that will eventually lead to something bigger.
Because the future isn’t crumbs or cake. It’s crumbs that become cake.
And if we can balance both — learning fast now while building for transformation later — we'll be able to define what efficiency really looks like in the age of AI.
Agree? Disagree? Have an opinion?
This Week Across Topline
This Made Us Think
This episode on Rockefeller is a reminder that empires usually start with one unglamorous constraint. For Rockefeller, transportation costs were the hill to die on, and paired with his maxim that “the good ones know more,” it’s a useful gut check: among all our OKRs, what single cost or chokepoint quietly determines everything else?
Bramdom’s write-up on Gemini 3 is another sign that frontier AI isn’t slowing down; it’s compressing. Three major releases in nine weeks, record reasoning scores, and now Google’s Antigravity IDE pushing agentic coding into the mainstream. The takeaway isn’t “who’s winning,” but how fast user expectations are resetting; what felt cutting-edge in June is table stakes by November.
Emily Kramer’s ecosystem playbook flips the growth question from “which channel?” to “whose trust are you borrowing?”. In a world where search, outbound, and events are all noisier, the real unlock is treating creators, communities, and integration partners as an extension of your GTM team.
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