AI Agents Require Human Oversight Time
by Jason Lemkin on January 1, 2026
AI is replacing jobs people don't want to do and displacing the mediocre, while creating new opportunities for those who adapt.
Jason Lemkin transformed his SaaStr sales team from 10 humans to 1.2 humans plus 20 AI agents, maintaining the same performance levels. This shift wasn't driven by a desire to cut jobs, but by frustration with the revolving door of junior sales staff who would quit after a few months without fully understanding the product. The experience revealed that AI excels at replacing routine tasks that humans often perform poorly or inconsistently.
For leaders, this means recognizing a fundamental shift in go-to-market strategy: the plays still work (outbound, webinars, podcasts, events), but the playbooks are broken. AI agents can handle email-based SDR work, lead qualification, and support with greater consistency than mid-tier performers. The best human salespeople remain valuable, but being a "people person" is no longer enough—product knowledge and technical expertise are increasingly critical.
The transition requires significant investment in training and oversight. AI agents work 24/7, including weekends and holidays, creating a constant stream of output that requires human review. This orchestration role is substantial—Lemkin's team spends 10-15 hours weekly reviewing agent outputs. Leaders should budget for this oversight work and identify team members with the right mindset to manage these systems.
For implementation, leaders should start small with one agent addressing a specific pain point. Take your best performer's approach, train the agent on it, and iterate. The key is doing the work yourself rather than delegating it or expecting instant results. Most organizations should buy rather than build these solutions, but they must choose vendors based not just on technology but on implementation support.
For individual contributors, the message is clear: embrace these tools rather than resist them. The most adaptable professionals will learn to work alongside AI, becoming more productive and valuable. Those who fight the transparency and efficiency that AI brings risk being left behind as organizations shift toward models where each salesperson handles significantly more revenue ($3-5M per rep versus $300-500K previously).
The future of sales isn't fewer jobs overall—AI leaders still can't hire enough enterprise reps—but rather a transformation of roles toward higher productivity, deeper product knowledge, and the ability to orchestrate AI systems that handle routine tasks at scale.
The New Go-to-Market Landscape
The market has bifurcated dramatically. Traditional B2B companies struggle with decelerated growth while AI companies face unprecedented demand—often with everyone in their market trying to buy simultaneously. This creates different incentives for AI adoption: slow-growing companies need efficiency, while hypergrowth companies need systems to handle overwhelming inbound interest.
For organizations implementing AI in sales, the orchestration challenge is significant. Multiple agents handling different parts of the funnel (outbound, inbound, reactivation) must be properly segmented to avoid conflicts. This requires someone with a data-oriented mindset who can spend hours daily reviewing outputs and making corrections.
Practical Implementation Steps
The most successful approach starts with taking your best performer's emails and methods, training the agent on them, and letting it iterate through A/B testing. Well-trained agents can personalize messages using data from your CRM and website visits, producing quality outreach that's often better than what mid-tier salespeople create.
When evaluating vendors, prioritize those willing to help with implementation over those with marginally better technology. The forward-deployed engineer or solution architect who will help train and deploy your agent is often more important than feature differences between platforms.
The transition to AI in sales isn't about replacing all humans, but about dramatically increasing productivity while maintaining or improving quality. Organizations that embrace this shift thoughtfully will find themselves able to engage with more prospects, provide better service, and focus human talent on the highest-value activities.