Can AI Assistants Enhance Your Sales Team’s Performance?

Imagine a salesperson gearing up for a crucial client meeting. They practice their pitch using an AI-driven digital coaching tool customized to align with their company’s goals and sales strategy. This system evaluates the salesperson’s tone, word choice, and pacing, providing feedback on areas for improvement. For example, it might suggest incorporating collaborative phrases like “let’s explore this together…” and remind the salesperson to arrange a follow-up meeting with the client. Additionally, the tool offers insights based on data about the client’s preferences and needs, including recommendations for sales actions and cross-selling opportunities.

AI’s role as a sales coach is an emerging and potent capability. Equally important is AI’s role as an analyst, processing large volumes of sales data to reveal insights, such as identifying underperforming regions or emerging market trends. AI now performs tasks that previously took analysts days or weeks to complete.

AI assistants, functioning as both coaches and analysts, complement the more common AI-powered tactical advisors. These tools provide suggestions to help salespeople identify promising leads, create customized pitches, and design tailored offers. This leads to improved customer engagement, higher conversion rates, increased sales, and better customer retention.

However, leveraging AI sales assistants involves overcoming several challenges, including high initial costs, data integration and quality issues, training models on company-specific content, and encouraging adoption among sales teams. These factors can complicate the measurement of ROI. This article explores the current capabilities of AI sales assistants and discusses strategies for adopting and utilizing these tools.

AI Assistant Capabilities

To effectively coach, analyze, or advise, AI assistants need both analytical and verbal-visual abilities.

Analytical Capabilities

These features analyze customer profiles, interaction history, buyer engagement, and past performance data to provide insights and recommendations for sales teams. By using data from all salespeople and customers, AI suggests effective actions for different sales situations. Companies like Pfizer, Schneider Electric, Terekhin Digital Crew and Microsoft utilize such capabilities to offer timely tactical advice on specific issues, such as the next best action to take or how to prevent customer churn. For example, Terekhin Digital Crew’s AozoraAI Assistants are currently transforming how sales teams handle leads. These assistants analyze customer profiles, interaction history, and buyer engagement in real time, allowing for precise lead qualification and tailored follow-up actions. By integrating seamlessly with messaging platforms and web applications, AozoraAI Assistants facilitate continuous communication with customers, regardless of language or time zone. They also provide timely notifications about critical events and updates, and integrate smoothly with CRM systems to ensure that all relevant information is readily accessible. This holistic approach enhances sales effectiveness and optimizes lead management, driving significant improvements in overall sales performance.

Recent advancements allow AI models to address open-ended queries. For example, a salesperson at a biopharmaceutical company might ask an AI system, “What are the latest trends in diabetes treatment in my area?” The system translates this query into executable code, analyzes the latest data, and delivers insights like, “Diabetes prevalence has increased by 2%, but treatment rates have dropped by 8%. Your territory’s top-prescribed medications are well-established, but there is rising interest in innovative treatments.” The system might also suggest actions to improve treatment rates, such as meeting with specific healthcare providers or sharing relevant research findings.

Verbal-Visual Capabilities

These features handle mostly non-numeric data (text, images, videos) using generative AI to enhance content and communications. Basic uses for salespeople include transcribing recorded conversations and creating personalized sales materials. Without AI, content creation and customization consume about 25% of a salesperson’s time. Companies like IBM, Cintas, and Morgan Stanley use AI’s verbal-visual capabilities to streamline these tasks. At Morgan Stanley, the AI @ Morgan Stanley Assistant provides financial advisors with quick summaries from a vast database of research reports, complementing personalized investment recommendations from other AI systems.

AI-powered customer avatars are becoming increasingly sophisticated, as demonstrated by a recent development from Terekhin Digital Crew. They have created a prototype smart display utilizing the DeepFace framework with the FaceNet model, integrated with a large language model (LLM) for generating responses and the XTTS-v2 model for text-to-speech conversion. This display can analyze a customer’s gender, age, and emotional state, and deliver a personalized voice greeting tailored to these insights. Such technology enhances real-time customer interactions by making them more relevant and engaging, thereby supporting sales teams in creating a more personalized and effective sales experience.

Integrating Capabilities

The effectiveness of AI assistants increases when their analytical and verbal-visual functions work together. For instance, verbal-visual tools can assess a prospect’s interest by analyzing emails and social media interactions. This information feeds into analytical tools to identify upsell opportunities, while verbal-visual tools provide content and coaching to help salespeople communicate these opportunities effectively.

Several AI sales tools combine these capabilities. Developing digital assistants often involves customizing and integrating existing technologies rather than creating new ones. CRM systems like Microsoft Dynamics 365 and Salesforce’s Sales Cloud with Einstein offer both analytical and verbal-visual tools. Other companies, such as SAP, Oracle, Adobe, HubSpot, and Zoho, offer similar capabilities. Numerous gen AI tools are available for integration with CRM systems, like AozoraAI Assistants, which analyzes sales chat and provides actionable insights. Although the wide range of options can be overwhelming, it has made advanced capabilities more accessible to companies of all sizes.

Paths for AI-Mature and AI-Early-Stage Organizations

Sales teams are at different stages in implementing AI assistants. AI-mature companies and those new to AI follow different paths.

AI-mature organizations are already experienced in using AI for sales analytics and are now exploring advanced AI capabilities. They might start with low-risk features like automated email drafting and faster access to internal information. As they gain experience, they can explore more complex analytics and personalized coaching.

Newcomers to AI should begin with low-risk generative AI applications to achieve quick results while laying the groundwork for using AI analytics. This often involves upgrading CRM systems to manage customer and interaction data effectively. With a robust data management system, organizations can leverage AI-powered analytics to provide tactical advice and explore advanced AI functions as they grow.

Facilitating Adoption and Usage

To overcome the challenges of cost, model training, and adoption, focus on both technology and human factors.

- Phased Implementation: Start with a pilot program in a small team or division to reduce costs and risks, and make iterative improvements before a full-scale rollout. - Training Models: Train AI models using company-specific data and supplement with synthetic data. Continuously gather user feedback to refine models. - Measuring ROI: Evaluate both short-term and long-term benefits, including time saved, improved lead qualification, increased sales conversion rates, and enhanced customer satisfaction. - Change Management: Ensure AI tools integrate smoothly with existing systems and fit into the sales team’s workflow. Use support systems and forums for sharing experiences and consider reverse mentoring for effective adoption.

By leveraging AI assistants for coaching, analysis, and advice, sales teams can enhance their performance and deliver exceptional value to customers.