Sales teams have typically not been early adopters of technology, but generative AI may be an exception to that. Sales work typically requires administrative work, routine interactions with clients, and management attention to tasks such as forecasting. AI can help do these tasks more quickly, which is why Microsoft and Salesforce have already rolled out sales-focused versions of this powerful tool.
Last month, Microsoft fired a powerful salvo by launching Viva Sales, an application with embedded generative AI technology designed to help salespeople and sales managers draft tailored customer emails, get insights about customers and prospects, and generate recommendations and reminders. A few weeks later, Salesforce (the company) followed by launching Einstein GPT.
Sales, with its unstructured, highly variable, people-driven approach, has been a laggard behind functions such as finance, logistics, and marketing when it comes to utilizing digital technologies. But now, sales is primed to quickly become a leading adopter of generative AI — the form of artificial intelligence used by OpenAI (the company behind ChatGPT) and its competitors. AI-powered systems are on the way to becoming every salesperson’s (and every sales manager’s) indispensable digital assistant.
Sales is well-suited to the capabilities of generative AI models. Selling is interaction and transaction intensive, producing large volumes of data, including text from email chains, audio of phone conversations, and video of personal interactions. These are exactly the types of unstructured data the models are designed to work with. The creative and organic nature of selling creates immense opportunities for generative AI to interpret, learn, link, and customize.
But to realize the true potential, there are hurdles and challenges to overcome. Generative AI must be non-intrusively embedded into sales processes and operations so sales teams can naturally integrate the capabilities into their workflow. Generative AI sometimes draws wrong, biased, or inconsistent conclusions. Although the publicly accessible models are valuable (hundreds of millions of users like us have already used ChatGPT to query the knowledge base on practically every topic), the true power for sales teams comes when models are customized and fine-tuned on company-specific data and contexts. This can be expensive and requires scarce expertise, including people with significant knowledge of AI and sales. So how can sales organizations harvest the value without wasting energy on heading down unproductive pathways?
Before addressing the how, consider what generative AI can do for sales organizations.
Reversing administrative creep. Almost every sales organization we touch is cursed with the gradual increase of administrative work over time. As selling complexity grows, so does the need for documentation, approvals, and compliance reporting. Unwittingly, the increasing use of sales technology is also a large factor. New technologies often lead to more training, more data entry, and more reports to peruse. Generative AI can reverse administrative creep, for example, by helping salespeople write emails, respond to proposal requests, organize notes, and automatically update CRM data.
Enhancing salespeople’s customer interactions. The use of AI in sales has been progressing of late. We have helped many companies deploy AI-powered systems that recommend personalized content and product offers, along with the best channel for salespeople to use to connect with customers. Recommendations are based on data about the preferences and behaviors of the customer and similar customers, as well as past interactions with the customer. Salespeople accept or reject the recommendations and can rate their quality to improve the algorithms.
By layering on generative AI, the models can produce better recommendations. One example would be considering customer sentiments gleaned from the nuances of language and subtle signals of customer interest or distrust — in emails, conversations with salespeople, posts on social media sites, and more. Further, the salesperson can collaborate with the system to improve recommendations in real-time. For example, after receiving a suggestion to approach a customer with a new offering, the salesperson can dig deeper — both vertically into the customer’s own needs and horizontally to find other customers who might benefit from the same offering. An interactive, conversational user interface makes the application easy to use. In a truly collaborative seller-buyer environment, even the buyer can be part of the dialog.
Assisting sales managers. Sales managers spend a lot of time studying reports and analytics on sales performance. Recently, most sales reports have progressed from passive, backward-looking documents to more interactive, diagnostics tools with drill-down capabilities. With generative AI, reporting systems can become even more powerful and forward-looking. Managers can pose questions to get insights for helping salespeople improve and for delivering more pointed and motivational coaching feedback. Sales planning tasks that took weeks can be performed in an hour, as managers dialog with the system to discover opportunities, formulate key account strategies, and determine how to allocate effort to geographies, customers, products, and activities.
The Journey to Value
Generative AI is relatively new and evolving rapidly. There is a shortage of talent for defining its role, training and fine-tuning models, and developing and implementing applications. One must find pathways that guard against falsehood challenges, realize value quickly, and deliver results while keeping costs under control.
Dealing with inaccuracy and inconsistency. ChatGPT and its competitors do sometimes give inaccurate answers or draw the wrong inferences. You ask the same question twice and you get different answers. Users must know when and how to use such technologies. One must start with high but realistic expectations. There is an art to asking questions and providing successive prompts to improve the answer. Sales organizations must learn this through training, apprenticeship, and best-practice sharing.
The risk is less when these models are fine-tuned on knowledge from the company’s context. Through added data, training, and feedback, accuracy and consistency improve. (Just like with people!) AI-generated answers in risky contexts must be reviewed by a person. Fortunately, human review is a natural part of salespeople’s and sales managers’ workflow.
Realizing value quickly. As the power of this disruptive technology grows exponentially, it’s possible to start realizing value in weeks, not months. One strategy for quick results is to integrate capabilities into existing sales systems. For example, generative AI can improve the tools salespeople use to write emails or develop sales presentations and proposals. It can also boost the quality of AI-generated suggestions by incorporating insights about customer sentiments. Such enhancements can happen in the background, so users benefit without needing to relearn application features. When it comes to speed of implementing, “buy” trumps “build.” Although building a custom AI-powered system offers greater flexibility, doing so is time-consuming and resource intensive. Buying an existing application reduces the need for specialized in-house talent and makes it easier to keep up with fast-changing technology.
Delivering results while controlling costs. It often makes sense to outsource capabilities while developing a small core of internal AI experts, who support sales as well as other functions. The odds of success are greater when efforts to bring AI to sales are led by a “boundary spanner” — an individual who understands and is respected by technical experts as well as by sales force members. By speaking both languages, a boundary spanner can help judiciously tailor solutions, so they are usable and useful for sales, but also implementable and sustainable over time. Further, an agile, iterative approach to implementation keeps efforts on the path to value while encouraging continuous improvement. Key steps include rapid prototyping, testing, and iteration based on feedback from an early experience team — a group of lead users who provide insights about system usability, value, and implementation plans.
Is AI a Productivity Aid or a Substitute for Salespeople?
We expect AI-powered technologies to rapidly become every salesperson’s and every sales manager’s digital assistant. Generative AI is already helping copywriters draft content and computer programmers write code, boosting their productivity by 50% or more. It can do the same for salespeople.
AI is already making customer self-service more powerful, and inside sales more potent. Consumers are increasingly using digital technology to research products and services on their own. Ecommerce has taken off in the B2B world too. Even in complex sales, digital plays an increasing role, taking on tasks such as lead generation and prioritization, product information sharing and configuring, and order placement. Inexorably, digital and inside sales continue to take over many tasks that field salespeople used to do, especially for familiar purchases.
However, new and complex offerings still require salespeople who can identify perceived and latent needs, tailor solutions, and navigate complex buying organizations. Yes, AI will take tasks away from salespeople and narrow their role even more on complex situations. At the same time, the companies that sell AI technologies will create large sales forces to capture the looming massive and complex opportunities.