Can AI Assistants Add Value to Your Sales Team?

A salesperson is preparing for an important customer meeting. They rehearse a pitch with an AI-powered digital coaching tool which is tailored to the company’s objectives and sales philosophy. The system analyzes the salesperson’s tone, words, and pacing. It points out areas for improvement, for instance, suggesting use of phrases that emphasize collaboration (“let’s explore this together…”) and reminding the salesperson to schedule a next meeting with the prospect. Further, the salesperson gets data-driven insights about the customer’s needs and preferences, including recommendations about sales actions and cross-selling opportunities. The role of AI as sales coach is a powerful emerging capability. Also growing rapidly is AI’s role as an analyst, diving into vast amounts of sales data to uncover insights, for example, about why certain geographies are underperforming, or what market trends are developing. AI instantly performs tasks that once took sales analysts days or weeks to do. AI assistants that act as coaches and analysts for salespeople, complement the more widely used AI-powered tactical advisors. Such tools deliver suggestions, helping salespeople identify promising leads, craft tailored pitches, and design customized offers. This drives enhanced customer engagement, higher conversion rates, more sales, and stronger customer retention. Getting value from AI sales assistants requires addressing several complications — significant upfront costs, data integration and quality challenges, training models on company content, and driving adoption with sales teams. These factors can make it difficult to achieve and measure ROI. This article will examine the capabilities of AI sales assistants now available and discuss paths to adoption and usage of these tools. AI Assistant Capabilities For AI assistants to coach, analyze, or advise, they need both analytical and verbal-visual capabilities. Analytical capabilities These features use customer profiles, interaction trails, buyer engagement, and historical performance data to create insights and suggestions for sales teams. By leveraging data across all salespeople and customers, AI intelligently suggests what actions work for each customer and sales situation. Pfizer, Schneider Electric, and Microsoft are among the many companies using such capabilities to give salespeople timely tactical advice about specific, predefined issues, for example, what next-best action to take and how to prevent customer churn. The result is improved responsiveness to customers and enhanced sales productivity. For example, when Microsoft began using “Daily Recommender,” an AI-based system that gives salespeople insights about customer needs and suggested sales actions, sales productivity (measured by time in front of customers and leads converted to opportunities) rose by as much as 40%. Recent advances allow AI models to answer open-ended questions. At a biopharmaceutical company, a salesperson asks an AI-based system: “What are the latest disease and prescribing trends for diabetes in my territory?” The system translates this natural language query into computer code, which is then executed to analyze the latest data. The insights produced are shared with the salesperson: “Diabetes prevalence increased by 2% but treatment rates declined by 8%. The top-prescribed medications in your territory are three established therapies, but there is growing interest among healthcare providers in learning about innovative approaches.” The salesperson queries further, “What can I do to enhance treatment rates?” to which the system responds with suggestions including which healthcare providers to visit in person and which ones to contact via email, what research findings to share about cardiovascular benefits, weight loss advantages, and convenient dosing, and the most effective strategies for distributing product samples. The salesperson blends these insights with their own experience, and is then more prepared to have relevant, insightful, and productive customer interactions. Verbal-visual capabilities These features synthesize mostly non-numeric data (text, image, video) using gen AI to enhance content and communications. Rudimentary applications for salespeople include transcribing and recapping recorded customer conversations and effortlessly creating customer-specific sales collateral. Without AI, content creation and customization take up about a quarter of salespeople’s time. Sales teams at IBM, Cintas, and Morgan Stanley are among those that use AI’s verbal-visual capabilities to streamline these types of tasks. At Morgan Stanley, a tool called the AI @ Morgan Stanley Assistant gives financial advisors access to about 100,000 of the firm’s own research reports and documents. Advisors query the database and get instantly summarized information for making investment recommendations and executing transactions. The “pull” approach of the gen AI-based tool complements the “push” approach of a “next best action” system which uses analytical capabilities to share personalized investment ideas for clients. A growing use of gen AI is for sales coaching. AI-based platforms analyze recorded or live sales calls. In addition to providing feedback about the salesperson’s communication style and content, these tools detect the customer’s sentiment and give immediate improvement suggestions. The guidance becomes even more personalized and contextual when the AI models integrate customer engagement history and salesperson performance data. The use of AI-powered customer avatars for sales training is also gaining traction. AI- and VR-powered sales training modules create realistic simulations, with AI-bots role-playing as the customer and simulating questions and objections the customer might raise, allowing salespeople to practice their skills in a safe environment. New-to-sales hires get up to speed quicker while experienced sellers accelerate competency development. When technology company Zoom needed to bring its sales team up to speed on new messaging for selling its full solution platform, sales reps around the world honed their skills by practicing with an AI-bot partner named Jenny. This led to a noticeable improvement in skills and message mastery across all sales reps. Integrating the capabilities The power of AI assistants is amplified when analytical and verbal-visual capabilities work collaboratively. For example, verbal-visual tools gauge a prospect’s interest by analyzing emails, chat transcripts, and social media communications. This insight feeds the analytical tools, which use data about the prospect and similar customers to identify upsell opportunities. Then, verbal-visual capabilities weigh in with content and coaching advice to help a salesperson communicate the opportunity. Several AI-enabled sales tools bring these capabilities together. Thus, building digital assistants is mostly about customizing and integrating existing technologies, not inventing new ones. CRM solutions offering both analytical and verbal-visual tools include Microsoft Dynamics 365 and Salesforce’s Sales Cloud with Einstein. Companies such as SAP, Oracle, Adobe, HubSpot, and Zoho have incorporated similar capabilities. Hundreds of companies offer gen AI tools that can be integrated with CRM systems. Zoom Revenue Accelerator is one of many solutions that use gen AI to analyze sales meetings and provide insights. Although the plethora of options complicates choice, it has democratized the capabilities, making them accessible to companies of all sizes. Paths for AI-Mature and AI-Early-Stage Organizations Sales teams are at various points on the journey of implementing AI assistants. AI-mature companies follow a different path from those who are just getting going. AI-mature organizations are well-established users of AI’s analytical capabilities for advising salespeople (for example, 70% or more of next-best-action recommendations are accepted.) The next frontier for these early adopters is to leverage the growing power of gen AI (if they haven’t already), starting with low-risk features, such as drafting customized emails and providing salespeople with faster access to information in the company’s knowledge base. As experience with developing and using these capabilities grows, these AI-mature organizations explore more complex and unstructured analytics and hyper-personalized coaching and training. Organizations that have not yet ventured down the AI path use a different approach. For these newcomers, starting with low-risk gen AI applications is the quicker route to value. Simultaneously, AI-early-stage organizations begin laying the groundwork for using AI’s analytical capabilities. This requires a reasonably robust system for managing customer and interaction data. Although perfect data is not needed, many such organizations discover they must upgrade their CRM system. With a solid data management platform, it’s possible to leverage AI-powered analytics to give salespeople tactical advice. And as both organizational and technological experience grows, the AI as coach and analyst can be explored. Facilitating Adoption and Usage To address cost, model training, and adoption challenges of implementing AI sales assistants, pay attention not just to technology, but also to the human elements and change management. Use a phased implementation approach. Start a pilot program with a small division or team. This minimizes upfront costs and risks and allows for iterative improvements before full-scale rollout. Train AI models on the company’s context. Even gen AI solutions that were built for specifically for sales can’t create enough value on their own; the power comes from training them on situation-specific data. Start by training on company data (e.g., transaction and interaction history for analytical models; past emails, proposals, and marketing content for gen AI models). Supplement this with synthetic data generated by algorithms to mimic real-world data. After models are launched, user feedback on AI recommendations is critical for driving continuous improvement. Measure ROI. Use a framework that considers both short-term and long-term benefits, including metrics such as time saved on administrative tasks, improvement in lead qualification, increase in sales conversion rates, and enhanced customer satisfaction scores. Manage the Change. Adoption and usage are greater when features work seamlessly with familiar tools such as CRM and email systems while fitting with the daily workflow of salespeople. Leverage support systems and forums where salespeople can share their experiences (e.g., at sales team meetings or in online collaboration spaces). Reverse mentoring, where junior tech-savvy team members help more senior members, is also effective. By harnessing AI assistants as coaches, analysts, and advisors, salespeople can drive growth while delivering exceptional value to customers. . . . Technology has been transforming sales for decades, and the latest wave of AI innovation promises to be perhaps the most disruptive force yet. Digital assistants will change the sales job in many ways. Salespeople will stop doing some tasks, such as recording notes in the CRM, which will become automated. They will perform other tasks more efficiently, such as responding more quickly with personalized communications for customers. Salespeople will also handle certain responsibilities differently and more effectively, for example, planning and executing more relevant and powerful conversations with customers. Additionally, they will take on new responsibilities, such as orchestrating actions across channels to better serve customers. As these changes unfold over the next few years, successful sales organizations will continue to evolve and embrace new opportunities for salespeople and digital to work together to drive value for customers and the business. Adapted from the HBR Sales Management Handbook, forthcoming October 2024.

Can AI Assistants Add Value to Your Sales Team?

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