If you spend five minutes in the comments under any “AI replaced our sales team” video, you’ll see the same reaction repeatedly: skepticism, cynicism, fear, and a very fair question underneath it all – What happens to the humans? Honestly, I think we all get that reaction. For most practitioners, those headlines do not sound like innovation; they sound like a layoff memo with better branding. And the data says that concern is real: Pew Research found U.S. workers are more worried than hopeful about AI at work. Only 6% say AI will create more job opportunities for them in the long run, while 32% think it will create fewer.
So let’s get more precise than the hype cycle usually allows. Yes, the shift is real. Yes, GTM jobs are changing. No, the right conclusion is not that humans are obsolete. The better conclusion is that work is being re-bundled again, which is what every major paradigm shift does (think the dawning of the printing press, the Industrial Revolution, and the Internet). Similar to those historical shifts, this next change will also bring a net-positive to the job market. The World Economic Forum projects 170 million jobs will be created and 92 million displaced by 2030, for a net gain of 78 million, while 39% of workers’ core skills are expected to change by then.
Is this a disruption? Indeed, yes. But is it also a reallocation, reskilling, and redesign? Absolutely, 100%.
That is the frame I think B2B SaaS leaders need right now. Not “AI versus people.” Not “headcount elimination as strategy.” But a much more practical question: “What work should software now do, and what work should humans spend more of their time doing?”
At Nvidia’s GTC 2026 conference, Jensen Huang predicted that SaaS companies will evolve into AaaS businesses – Agentic AI as a Service. Gartner is seeing the same movement from the application layer: it predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025, and that agentic AI could drive about 30% of enterprise application software revenue by 2035. That is not a feature trend. That is a category-level shift in where software value lives.
But let’s not confuse “real shift” with “every agent pitch is real.” Gartner also predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 because of rising costs, weak controls, or unclear business value, and it explicitly warns about widespread “agent washing.” In plain English: the market is moving, but plenty of companies are still packaging hype as capability.
You can already see how leading software companies are framing that shift. Xero accounting software says AI is helping it evolve from a financial system of record into a system of action and decision-making, while explicitly saying it designs with augmentation, not replacement in mind and that human value remains essential. TechnologyOne now calls itself the world’s first SaaS+ company and upgraded FY26 guidance on the strength of SaaS+ and AI momentum. Salesforce says Agentforce can operate autonomously in the background of any business process, and Oracle launched AI Agent Studio to help customers create and manage AI agents and agent teams across the enterprise.
My read is simple: AaaS is software that performs work, not just software that helps people perform work. The SaaS layer does not disappear. It becomes the infrastructure underneath the agent layer: data, workflows, permissions, integrations, governance, memory. The commercial value shifts upward, going from access now to execution.
GTM is one of the earliest places where this model becomes economically obvious because the work is digital, repetitive, signal-driven, and measurable. The inputs already exist: CRM history, ICP definitions, firmographic filters, buying signals, routing logic, calendars, message libraries, objection patterns, and conversion goals. That makes top-of-funnel revenue work a very natural candidate for agentic execution.
The community of GTM practitioners pushing back on SaaStr’s Jason Lemkin’s high-profile AI SDR experiment are not irrational. They are making an important point. What works for event ticketing, reactivation, or lower-complexity motions does not automatically translate one-for-one into enterprise SaaS. And generic AI spam absolutely will get ignored, filtered, or remembered for the wrong reasons. In more complex B2B sales, trust, discovery, internal politics, multi-threading, commercial nuance, and relationship depth still belong squarely in human hands.
That is why the real lesson from the early AI-SDR wave is not “replace your sales team.” The real lesson is: use agents on the work your humans are under-covering, under-following-up, or never getting to in the first place.
And the operational takeaway is even more useful than the headline. SaaStr’s advice is to hyper-segment everything and start with something in your go-to-market motion that simply is not getting done.
That is the mature version of agentic adoption. Not “point AI at the entire database and hope.” More like, start where the workflow is repetitive, measurable, low-risk, and currently leaking revenue.
This is the part GTM leaders need to communicate far better than they currently do.
If you are an SDR/BDR, AE, Sales leader, or RevOps operator, your value does not disappear. Instead, your leverage point changes.
Microsoft’s 2025 Work Trend Index says “every employee becomes an agent boss,” and that 78% of leaders are considering hiring for AI-specific roles. It also says 47% of leaders see upskilling existing employees as a top workforce strategy, 51% say AI training or upskilling will become a key responsibility for their teams within five years, and AI literacy is now the most in-demand skill. That is not the language of a workforce becoming irrelevant. It is the language of a workforce being retooled.
The new scarce skills are not just “knowing AI.” They are knowing how to brief it, supervise it, QA it, steer it, constrain it, and intervene at the right moment. They are knowing how to build clean segments, define escalation logic, improve data quality, sharpen messaging, read intent, and decide when software should act versus when a human should step in.
There is also early evidence that the best human + AI combinations outperform older ways of working. In an NBER field experiment at Procter & Gamble, individuals using AI matched the performance of teams without AI, and AI helped break down functional silos between technical and commercial contributors. Another NBER study found that leadership skills with AI agents strongly predict leadership skills with human teams. That should matter to every GTM leader reading this: Managing agents well is not a lesser form of leadership. It is quickly becoming part of modern leadership itself.
In practical terms, the skills that go up in value in an agentic GTM environment are signal interpretation, segmentation, messaging judgment, workflow design, objection handling, deliverability literacy, data hygiene, governance, and high-quality human handoff.
RevOps becomes less of a reporting center and more of a control tower.
Sales leadership becomes less about manually pushing activity and more about designing high-performing human-agent systems.
And the best sellers become the ones who know where automation ends, and trust begins.
That is why I do not buy the lazy binary that “AI replaces salespeople” or “AI changes nothing.” Both are wrong. Some repetitive, transactional roles will absolutely shrink. Some entry-level motions will be automated. Some openings will be backfilled with software instead of being rehired. Pretending otherwise is not kind; it’s dishonest.
But the bigger story is still one of workforce evolution, not workforce extinction.
Human ambition, creativity, and ingenuity remain the value engine. Microsoft says that plainly. Xero says it in product language: Design with augmentation, not replacement. That is the posture I believe responsible GTM leaders need to adopt publicly and operationally.
My advice is simple: do not roll out agentic AI as a headcount story first. Roll it out as a throughput, responsiveness, consistency, and focus story.
Use agents to remove the work that your best people should not be doing by hand:
Keep humans on live conversations, nuanced qualification, multi-stakeholder navigation, deal strategy, pricing, and closing.
The best starting use cases are the ones where you can measure lift without eroding trust: speed-to-lead on inbound, re-engagement of aged pipeline, outbound against one ICP slice, and follow-up coverage where human execution is inconsistent. That is the lane where agentic AI can create real revenue without pretending the machine can run the entire commercial relationship.
That is exactly how we think about RevAI SDR Agent Team.
Not as a flashy “replace your people” pitch.
Not as another generic outbound bot.
And definitely not as a reason to devalue the humans on your revenue team.
We see it as a revenue execution layer for B2B SaaS companies that want to automate repetitive top-of-funnel work while keeping humans focused on strategy, conversation, and closing.
That is the version of AaaS I believe will last.
Not software that makes people feel disposable.
Software that makes humans more useful.
Not AI that strips the relationship out of revenue.
AI that removes the drag so your team has more time for the relationship.
And not a story about the end of work, but rather a story about the next evolution of work, and the teams willing to build for it.
Is your outbound motion ready for agentic execution without sacrificing the human side of sales? Download the Outbound Autopilot Readiness Scorecard or request a demo to see how our RevAI SDR Agent Team built on the AnyBiz core can put repetitive prospecting on autopilot — so your team can stay focused on the work only humans should own.
Here’s to turning insight into action,
~ Nikke