The era of merely experimenting with AI tools in go-to-market teams is over. B2B go-to-market teams are now expected to achieve efficiency gains upwards of 30% using AI. For instance, Battery Ventures showcased how integrating AI tools can reduce a sales and marketing headcount from 110 to 75 while maintaining high productivity levels. This sets a clear expectation for what go-to-market leaders need to deliver.
The AI Tool Explosion: Navigating the Options
Most sales organizations are already using or experimenting with AI tools. However, keeping track of the ever-expanding list of available tools can be overwhelming. For instance, here is a list of over 750 AI tools dedicated to sales. With so many options, companies often struggle to determine the best strategies for implementing AI in their go-to-market teams to achieve these efficiency gains.
In this series, we will explore different parts of go-to-market organizations and share ideas on how to implement these efficiency increases using available tools. This blog post will focus on one area that is particularly well-suited for transformation through AI: Outbound Demand Generation.
3 Key Areas Where AI Transforms B2B Outbound Jobs
Within typical outbound demand generation teams, there are usually two main roles: SDRs who handle outreach activities and generate pipeline for AEs, and Lead Managers who qualify, prepare, score, and distribute leads.
1. Lead Management
Lead management is an area where we expect to see decreasing team sizes very soon. The days of manually importing and cleaning lists of companies are over. Tools like Clay and Apollo can automate many tasks traditionally done by lead managers, including providing enriched datasets for prospecting. These AI-powered tools streamline the lead management process, automatically scoring and prioritizing leads based on their likelihood to convert.
2. Automation of Routine Tasks
AI can handle repetitive tasks like scheduling meetings and updating CRM entries, freeing up SDR teams to focus on more complex, relationship-driven activities. This shift not only improves productivity but also enhances job satisfaction among SDRs, who can engage in more meaningful interactions. However, this change also means that SDR roles will evolve. While high activity and resilience were previously key to success, analytical skills and the ability to leverage AI insights will become increasingly important.
3. Improved Customer Interactions
AI tools can analyze customer data to provide insights that personalize interactions. Personalization and the right timing are crucial for successful outbound operations. Manually creating the data for these personalized and timely outreaches is labor-intensive and cost prohibitive. AI tools, combined with sales engagement software, can automate these tasks and execute outreach sequences faster. Additionally, some tools can predict customer needs in real-time, allowing sales teams to engage with prospects who show intent and need at the optimal moment.
Conclusion
As AI tools become more advanced and widespread, the key to leveraging this technology lies in selecting areas with a high potential to boost sales efficiency1. We believe that the role of lead managers will be significantly transformed, if not replaced, by AI tools. One dedicated AI tool specialist can ensure that every SDR has the right leads at the right time.
The role of an SDR is already undergoing significant changes. What used to be an entry-level sales job focused on high activity and resilience is evolving into a role that requires designing and automating personalized conversations. Many companies operate their outbound teams with an SDR:AE ratio of 1:1 or 1:2. Moving towards a ratio of 1:3 can be a great way to measure the ROI on AI implementation.
[1]: Sales Efficiency is a metric that quantifies how effectively a company’s sales and marketing investments generate new revenue. It is calculated using the formula: