Summary of "6 Months Later, How Our AI SDRs Actually Work as AI Runs GTM with SaaStr's CEO and Chief AI Officer"
Summary: 6 Months Later, How Our AI SDRs Actually Work as AI Runs GTM with SaaStr’s CEO and Chief AI Officer
This session provides an in-depth review of SaaStr’s six-month journey deploying AI-driven Sales Development Representatives (SDRs) and other AI agents across their Go-To-Market (GTM) operations. The discussion covers strategy, operational insights, vendor selection, metrics, challenges, and practical recommendations for companies adopting AI in sales, marketing, and support.
Key Business Insights & Frameworks
AI GTM Deployment Framework
- Start small and scale: Begin with one AI use case (e.g., outbound SDR) before layering in inbound, support, marketing, and other agents.
- Use AI to clone and scale existing best practices rather than expecting AI to create new winning strategies.
- Continuous human oversight, training, and iteration are essential; AI is not “set and forget.”
- Prioritize empowering A-player human team members by pairing them with AI agents to become “S-tier” performers.
- Manage AI agents as a portfolio: Different agents for different use cases, audiences, and goals (e.g., cold outbound, lapsed customers, current sponsors).
Operational Tactics
- Use segmented AI agents trained for specific audiences and goals (e.g., five different outbound agents for different sponsor categories).
- Maintain manual oversight on AI outputs, especially for complex or multi-threaded conversations.
- Regularly refresh and feed AI with new, clean contact data; frequency affects performance (e.g., twice weekly updates improved response rates).
- Avoid overlapping contacts across multiple AI agents to prevent duplicate outreach; requires manual orchestration if multiple platforms are used.
- Integrate AI agents with CRM (Salesforce) and marketing automation (Marketo) for data consistency and context.
Vendor & Tool Selection Playbook
- Avoid running excessive vendor bake-offs (e.g., 10+ simultaneous trials); focus on 1-2 vendors to invest time in training and integration.
- Budget realistically for AI tools: $50k-$100k+ annually is typical for enterprise-grade AI SDR/BDR tools including onboarding and training.
- Expect onboarding/training costs ($20k-$30k) and demand vendor support for implementation.
- Choose vendors with strong human support teams (onboarding specialists, solution architects) rather than relying on sales reps unfamiliar with AI.
- Negotiate pricing and access to beta features if possible, especially if you can demonstrate volume or strategic use.
- Natural attrition of human headcount can be a budget source for AI tools rather than firing existing staff.
Measurement & KPIs
- Outbound AI SDRs sent ~20,000 messages over 6 months.
- Overall outbound response rate: ~7% (about double average industry benchmarks).
- Positive outbound response rate: ~4% (above average).
- AI-driven outbound emails per rep: AI agent sends ~3,000 emails/month vs. 75-285 by humans.
- AI outbound contributes ~10% of ticket revenue for SaaStr events.
- Inbound AI agent (3.5 months live) handled ~700,000 sessions, ~1,000 conversations, and booked ~100 meetings.
- Inbound AI agent attributed to ~$1 million in revenue, with 70% of October’s closed deals linked to AI-sourced meetings.
- AI agents improve speed and conversion rates by providing context-rich conversations and pre-qualified leads.
- Email open rates for AI outreach can reach ~72%, significantly higher than traditional campaigns.
Concrete Examples & Case Studies
Outbound AI SDR (Artisan platform)
- Specialized agents targeting different segments (cold outbound, lapsed sponsors, current sponsors, event attendees).
- AI scaled outbound volume massively, maintaining or improving response rates.
- AI effectively sold lower ASP event tickets (~$1,000 or less) autonomously.
- Warm-up period of 2-3 weeks needed for deliverability optimization.
- Human-in-the-loop approach: AI drafts responses, humans review for complex queries.
- Challenges with multi-threaded or nuanced conversations (e.g., sponsorship vs. free tickets).
Inbound AI Agent (Qualified platform)
- Handles live website sessions and conversations, booking meetings instantly.
- Provides detailed context to sales reps (website behavior, multiple contacts from same company).
- AI empowered to sell event tickets and handle discount requests, including follow-up emails.
- Reduced lead response time from hours/days to instant booking.
- Specialized training with extensive ingestion of SaaStr’s content (20 million words across multiple domains).
Agent Force (Salesforce integrated AI agent)
- Used to follow up on ~1,000 leads previously ignored by sales reps.
- Achieved high open rates (72%) and response rates early in deployment.
- Operates within Salesforce UX, leveraging CRM data for personalized outreach.
- Early stage but promising for re-engagement and sales follow-up automation.
- Setup required vendor assistance but manageable without deep Salesforce expertise.
Actionable Recommendations
AI Adoption Strategy
- Do not expect AI to fix broken processes; first optimize existing workflows.
- Deploy AI incrementally, starting with outbound or inbound before expanding.
- Dedicate significant human time to training, monitoring, and iterating AI agents.
- Pair AI with your best performers, not to replace them but to amplify their output.
- Avoid expecting immediate ROI or headcount reduction; AI is a force multiplier requiring investment.
Vendor Management
- Demand access to technical onboarding and solution experts before purchase.
- Be wary of sales reps who do not understand AI products.
- Negotiate pricing and seek beta features to maximize value.
- Beware of vendors overwhelmed by demand; not all are a good fit.
- Use natural attrition to reallocate budget toward AI tools instead of firing.
Operational Best Practices
- Maintain clean, segmented contact lists to avoid duplicate outreach.
- Refresh AI data inputs frequently (ideally twice weekly).
- Use AI to handle high-volume, lower-touch interactions (e.g., event ticket sales).
- Reserve human effort for high-value, complex conversations.
- Monitor AI outputs continuously to catch gaps or hallucinations.
Key Metrics Summary
Metric / KPI Value / Insight Notes Outbound messages sent ~20,000 over 6 months Single platform (Artisan) Outbound response rate ~7% overall ~4% positive responses Outbound emails per rep AI: ~3,000/month vs Humans: 75-285 Massive scale advantage AI contribution to ticket revenue ~10% For SaaStr events Inbound sessions handled ~700,000 in ~3.5 months Via Qualified platform Inbound conversations ~1,000 Meetings booked (inbound) ~100 Revenue attributed to inbound AI ~$1 million 70% of October closed deals linked to AI Email open rate (Agent Force) ~72% Very high open rate for re-engagement Budget for AI tools $50k-$100k+ annually Includes onboarding/training Warm-up period for deliverability 2-3 weeks Critical for outbound email successPresenters / Sources
- Amelia (SaaStr Chief AI Officer)
- Jason (SaaStr CEO)
- Additional references to vendor teams and founders (e.g., Jasper, CEO of Artisan)
Conclusion
SaaStr’s six-month experience demonstrates that AI SDRs and GTM agents can significantly scale outreach and improve conversion metrics when paired with strong human oversight and existing proven sales processes. The key to success lies in incremental deployment, continuous training, and focusing AI on amplifying the best human talent rather than expecting AI to replace or reinvent sales strategies. Budgeting realistically for vendor costs and investing time in onboarding are critical. Finally, companies should be cautious of overspending time on multiple vendor trials and instead choose a focused AI stack that fits their needs and data maturity.
This summary emphasizes business execution, operational tactics, and strategic frameworks for AI-driven GTM transformation as presented by SaaStr’s leadership.
Category
Business
