Generative conversational AI is an absolute game changer across virtually every industry. Over the past few years, the technology has evolved significantly, and LLMs can hold their own in just about any conversation. When you chat with customer service at your phone company, for example, you might not even realize you’re speaking to a bot. New employees find themselves getting information about their roles not from a human mentor, but a personalized GPT.

The point is that AI is everywhere, but nowhere are conversational AI’s capabilities more apparent than when it comes to the sales process. Unlike human sales reps, who need downtime and personal lives, AI is always ready (and willing) to jump in and take over. AI shines in the specific, repetitive tasks that can make the sales process especially challenging. Here are some of the ways that AI is creating a smoother sales process for both agents and customers.

1. Detailed Answers

In order to succeed in the sales process, an agent needs two very specific knowledge sets. They need a deep understanding of their customers’ core needs, and encyclopedic knowledge of their products. Of course, there are other skills involved in landing a sale, but these can be two of the most memory-taxing. A human agent must retain tons of facts about each customer, know their products inside and out, and memorize new features as they’re introduced.

While comprehensive conversational AI tools, like ChatGPT, have access to broad, universal knowledge, more companies are turning to specialized AI. They’re using industry-specific AI tools that can be trained on large, discrete data sets. For example, consider automotive sales software and AI-driven real estate tools. These products are designed to suit the distinct needs of these industries, and answer each customer’s questions with a deeper level of nuance.

2. Instant Responses

Most human sales representatives need to sleep at some point, even if they work especially long hours. That can be a problem when a customer, say, works the second-shift and reaches out with a late-night question. To add to that, customers now expect follow-up within a much shorter time window before moving on to a competitor. Where they might’ve once waited 48 hours for a response, many won’t give you more than a few minutes or hours.

In order to serve this changing customer base, sales teams need to be more responsive than ever. Instead of turning to manpower, they’re turning to conversational tools like AI-driven chatbots and SMS reply generators. These tools can ensure that every prospect who reaches out gets a response within as little as a few seconds. With that kind of immediate contact in their hands, they’re more likely to set an appointment and stick with a particular company or sales rep.

3. Lead Scoring

AI is getting better and better at understanding peoples’ wants, needs, and motivations. One area in which it’s particularly efficient is evaluating the likelihood of a purchase (or churn). AI can instantly let a sales team know, based on chat content, online behavior, and more, how interested a prospect is. A whopping 98% of sales teams using AI say it has improved their lead prioritization. Agents know who to follow-up with more assertively, and who to give some time.

Where the “generative, conversational” part of AI lead scoring really comes in, though, is what to do with this knowledge. For example, AI can determine how to word communication, like texts and emails, based on what’s most likely to sell. Generative lead scoring can also involve creating more advanced models of potential customers. Instead of looking for patterns in larger data sets, they can determine customer characteristics from shared traits and predictive analytics.

4. Persistent Follow-Up

Unlike forgetful human beings, who have to be reminded to follow up and gauge their tone, AI is automatic — and somewhat relentless. Generative AI can determine the right frequency, methods, and times to follow up, and the right language to use to persuade the customer. Whether it’s a “thanks for stopping by!” text after an onsite visit, or a “long time no see” email, generative AI has it covered.

AI also knows when to leave well enough alone, so as not to annoy a prospect with too much communication. It can use negative sentiment analysis, unresponsive behavior, purchase history, frequent complaints, and other data analytics to determine when not to engage. It also knows to exclude certain customers from follow-up, such as when interest is seasonal or time-sensitive. It can even use financial data to exclude customers who can no longer afford the product.

5. Personalized Recommendations

One last area where generative AI really shines is in making personal recommendations to customers, based on their data. It can aggregate their past purchase history, budget, likes and dislikes, age, location, size, favorite colors, and more. It can then apply this knowledge in conversational search recommendations, such as in a chat conversation with web marketplace customers. It can also “think” about how to phrase the information in the most effective tone.

For example, let’s say a woman is shopping for a pair of headphones on her favorite online marketplace. She asks conversational AI what would be the best new headphones for her needs, and AI looks at the available data. She recently purchased a new pair of gym shoes, so the AI asks her if these are for the gym or for work. It also asks her if she’d like a noise-cancelling option, since data shows she lives in New York City, close to an elevated subway.

Just the Beginning

The era of generative conversational AI in the sales process is only just beginning. There’s still so much potential for new use cases, and versions of AI that feel even more human. Yes, rising concern about data privacy can make it harder for algorithms to get their hands on the most current info. Still, AI is getting better and better at making predictions, understanding peoples’ needs, solving problems, and making the right recommendations.