Sales Platforms Release New AI Automation Features
Sales technology vendors are embedding more advanced AI into their platforms to reduce manual work for reps. Apollo.io now enables users to search, enrich, and sequence contacts directly from their workflow, while Outreach's latest release features AI that can execute next steps and automatically flag pipeline risks.
- Revenue intelligence platforms like Clari and Gong provide AI-driven capabilities to improve forecast accuracy and offer a holistic view of pipeline health. Clari's platform, for instance, can unify forecasting models for subscription and consumption-based revenue, which is critical for complex hardware and software sales. Similarly, Gong's AI captures and analyzes customer interactions to surface deal risks and coaching opportunities for sales reps. - For long sales cycles common in enterprise hardware, which can average six months or more, a key metric to track is sales cycle length by opportunity stage. AI-powered forecasting can improve accuracy by up to 15% over traditional methods by analyzing historical data, deal patterns, and rep performance to predict closing probabilities. This is a significant improvement over static weighted pipeline models. - In deals with multiple stakeholders, a common feature of high-ACV sales, CRM optimization is crucial for mapping out all decision-makers, their priorities, and their engagement levels. Platforms like People.ai automate the capture of contact and activity data to provide a complete picture of the buying committee and identify key relationships. - Andrew Brown, Chief Revenue Officer at Red Hat, noted that his sales teams improved win rates by over 50 percent by leveraging People.ai's open architecture to create a more unified AI infrastructure, moving beyond isolated technology solutions. - To enhance pipeline visibility, it's crucial to define clear sales pipeline stages and use CRM software to track deal progression. AI tools can automatically score leads based on their propensity to buy and route them to the appropriate sales representative, ensuring that high-potential leads receive prompt attention. - Effective forecasting in technical sales involves a combination of methodologies. Time-series analysis, which examines historical sales data to identify trends, can provide a baseline forecast. This can be augmented with lead-driven forecasting, which analyzes the quality and quantity of leads to predict conversion rates. - According to research from Clari Labs, enterprise customers using their Revenue Orchestration Platform have seen a 20% faster closing of new logo deals compared to two years prior. This is attributed to AI-driven features like smart deal summaries and advanced opportunity scoring that boost seller productivity. - A significant challenge in sales operations is that up to 80% of CRM data can be inaccurate, which undermines the effectiveness of AI tools. Platforms that automatically capture and validate customer interaction data are essential for building a reliable foundation for AI-driven insights and forecasting.
