Will AI take away jobs in tech? Some of course. Even more often, already, it’s leading to headcount being rebooted and used in new ways.
Salesforce for example has already repurposed headcount from 1000s of support agents into sales. Same headcount, but different roles.
But that’s not how AI will change tech headcount and jogs the most. There’s a much bigger role for AI to play, and problem to solve: we don’t have enough humans to do the actual work that drives B2B growth.
Not the glamorous work. Not the “strategic” work. Not managing teams. But the grinding, repetitive, essential work that separates successful B2B startups from the rest.
Even again Salesforce itself had 1,000,000+ leads its own human SDRs didn’t want to, or didn’t think they had time to … follow up on:
The biggest change for AI + roles in tech isn’t about layoffs or replacing talent.
This is about acknowledging a fundamental mismatch between what needs to get done and what humans are truly willing to do at scale.
The Work No One Wants to Own or Really Do Well. If At ALll.
Walk into any B2B company and you’ll find these gaps everywhere:
Sales Development That Actually Works Everyone talks about personalized outreach, but who wants to research 200 prospects a week and craft truly customized emails? The answer is almost no one. The best SDRs burn out. The mediocre ones send templates. The result? Most outbound efforts fail because the volume of quality work required exceeds human capacity and motivation. Even at Salesforce, 100,000,000+ leads were never followed up on during its 2.5 decades in business. 100 million.
Following Up on B and C Leads Your sales team focuses on A leads. That’s rational. But those B and C leads sitting in your CRM? They represent millions in potential revenue, but following up properly requires hundreds of touches across months. Sales reps won’t do it consistently. It’s not rewarding enough.
Customer Winback at Scale You can recover 10-15% of churned customers with the right approach. But that means systematic outreach across multiple channels over extended periods. Who’s going to manage 500 different winback sequences simultaneously? No one raises their hand for that assignment.
Real Onboarding Not the automated email sequence you call onboarding. Real onboarding means checking in, troubleshooting, hand-holding, and ensuring actual adoption. It requires patience, repetition, and attention to detail that doesn’t scale with human teams.
Small Deal Velocity Your AEs want to close six-figure deals. But what about the $1,000 monthly subscriptions that add up to millions in ARR? These deals need attention, follow-up, and care, but they don’t provide the dopamine hit or commission structure that motivates top performers.
Building the Boring Applications Every engineer wants to work on the core product, the AI features, the sexy infrastructure. But someone needs to build the admin panels, the reporting dashboards, the integration tools. These are table stakes for enterprise customers, but they’re career dead-ends for ambitious developers.
Actually Picking Up the Phone Cold calling works. Everyone knows it works. But who wants to make 50 calls a day and get hung up on 47 times? Even experienced sales professionals avoid it when possible.
Why This Isn’t About Skills, Re-Skilling or Hiring
This isn’t a talent shortage problem you can solve by raising salaries or improving benefits. These are fundamental human nature issues:
- Repetitive work is psychologically draining
- Low-reward activities get deprioritized
- Humans optimize for immediate feedback and recognition
- Scale requirements exceed individual capacity
- We all got used to working a certain way from home from 2020-2022
- Many of the next generation is just less interested
You can hire more people, but you’ll get the same patterns. The work still won’t get done consistently at the level needed for competitive advantage.
AI Bails Us Out. And … Will Let Us Fly
The current generation of AI tools is already handling pieces of this puzzle. But we’re moving toward something more comprehensive:
Intelligent Sales Development AI that researches prospects, identifies trigger events, crafts personalized outreach, and manages multi-touch sequences at infinite scale. Not templates pretending to be personal, but actually relevant communication based on real insights.
Systematic Lead Nurturing AI managing hundreds of nurture tracks simultaneously, identifying when prospects show renewed interest, and escalating to humans at the optimal moment.
Customer Success That Scales AI monitoring product usage, identifying at-risk accounts, managing onboarding flows, and ensuring customers hit their first value milestone before a human ever needs to intervene.
Automated Deal Progression AI handling the discovery, demo scheduling, proposal generation, and follow-up for smaller deals, only involving human sales reps when contracts are ready to close.
The Flywheel Effect
When AI handles the work humans avoid, several things happen:
Your human team focuses on high-value activities they actually want to do. Sales reps spend time with qualified prospects instead of cold calling. Engineers build features instead of admin tools. Customer success managers handle strategic accounts instead of routine check-ins.
You achieve consistent execution across all these unglamorous but essential functions. Every lead gets followed up. Every customer gets proper onboarding. Every winback opportunity gets pursued.
Your competitive advantage compounds because you’re doing things at scale that competitors can’t sustain with human-only teams.
Implementation Reality
This transition isn’t happening overnight, but the pieces are falling into place rapidly:
- Sales automation tools are becoming genuinely intelligent
- Customer communication AI can handle complex, contextual conversations
- Code generation tools are tackling routine development work
- Voice AI is making phone-based outreach scalable again
Smart SaaS companies are already running experiments. They’re identifying the specific workflows where AI can take over the grinding work and free humans for strategy, relationship building, and complex problem-solving.
AI Does What We Need, But No One Wants To Do
The future of SaaS isn’t about AI replacing humans. It’s about AI doing the essential work that humans will do poorly or not at all because it’s repetitive, unrewarding, or doesn’t scale.
Companies that embrace this reality first will have operational advantages that compound over time. They’ll have cleaner data, more consistent processes, better follow-through, and human teams focused on work that actually drives satisfaction and retention.
The technology is almost there. The question is whether your organization is ready to let AI handle the jobs that, frankly, no one really wants to do anyway.
When that happens, everything else becomes possible.
Why We Have 11+ AI Agents Already in Production Ourselves
It’s true at tiny team SaaStr, too. Too many core functions … humans didn’t want to do:
- We couldn’t get our SDRs to follow up on most potential prospects. They didn’t see the return.
- We couldn’t get our content team to review 1,000s+ of submissions. Too many to find the diamonds in the rough.
- We couldn’t get our sales team to chase lapsed sponsors and partners. They didn’t see the ROI.
- We couldn’t get our AEs to do any prospecting at all. Few want to.
- We couldn’t get the GTM team to follow up on leads instantly.
- We couldn’t get our sales team to update our collateral consistently. Or to make custom pitch decks for sponsors (Gamma does this now).
- We couldn’t get the agencies that helped support ticket holders to really answer questions correctly, or promptly.
- We couldn’t get anyone to email out to SaaStr AI Summit / Annual attendees to get them to come back. They were only willing to drop them all in a mass cadence.
So we replaced all of that headcount … with AI. Not to save money. But because no one would really do those critical roles. We’d hire them, they’d quit or only do the parts of the jobs they wanted to. That was OK in 2020-2021. Not today.
The work just wasn’t getting done, no matter how much we paid or whom we hired. In some cases, e.g. content review, AI did a better job than humans. In other cases, it came up short of what a human did on our team. But — it did the job. That’s what matters.
More on what we’ve already done and learned here:
