Sales AI: How To Use AI In Sales, Benefits & Challenges

Artificial intelligence (AI) for sales describes using automated technology to simplify and streamline the sales process.

Forward-thinking companies are catching on to this fact. According to a Salesforce State of Sales report, sales leaders expect their AI adoption to grow faster than any other technology. Salesforce also found that high-performing teams are 4.9X more likely to be using AI than underperforming ones.

Sales is a field that relies heavily on human interaction, but technology has always played a significant role in enhancing its efficiency and effectiveness.

AI is one of the latest technologies that’s making a big impact on the world of sales. In fact, according to a recent survey, 50% of senior-level sales and marketing professionals are already using AI, and another 29% plan to start using it in the future. AI tools can provide sales teams with valuable insights based on data, identify new leads, personalize customer experiences, and optimize sales processes.

Yet AI is not replacing salespeople. The vast majority of companies using AI or planning to also plan to increase staff. That’s because AI is creating practical value for sales teams by giving them superpowers, with several real-world use cases and tools being used today.

However, there’s so much hype around AI that salespeople often miss this fact.

Understanding the concept of AI in sales

AI in sales uses artificial intelligence to simplify and optimize sales processes. This is done using software tools that house trainable algorithms that process large datasets. AI tools are designed to help teams save time and sell more efficiently.

Artificial intelligence algorithms learn from and apply data in various ways, including

Performing specific tasks and decision-making functions.
Predicting behavior and making suggestions based on historical and current data.
Interacting with humans intelligently.The Need for Artificial Intelligence in Sales

An estimated 33% of an inside sales rep’s time is spent actively selling. Administrative to-dos and meetings can pull these professionals away from prospects. Artificial intelligence presents a compelling opportunity to improve this stat and level up your sales operation.

Types of AI-powered sales tools

Organizations use different kinds of sales AI for different functions. To build a complete AI-enabled tech stack, they can employ the following tools.

Chatbots

Chatbots are generally used for customer service and lead generation, but they can also serve as a mechanism for direct selling if a customer is ready to make a purchase decision.

Mainly, they eliminate the need for sales reps to engage with customers for simple, repetitive tasks, such as:

Providing basic product information
Answering frequently asked questions
Giving personalized recommendations
Booking product demos and discovery calls
Navigating the website, product catalog, or knowledge base to find content

Since they are software components that integrate with the company website, they deliver immediate responses to customers 24 hours per day while freeing up valuable time for sales teams to focus on more complex tasks.

Chatbots are meant to integrate with CRM, email marketing, and e-commerce platforms to create a seamless sales funnel. They can also collect and analyze data about how customers use and feel about the product, which sales and marketing teams use to improve their efforts.

Machine Learning

In the context of AI in sales, machine learning algorithms are often trained on historical sales data. They learn from past transactions, customer interactions, product information, and many other variables to understand patterns and correlations.

Once these algorithms digest this data, they can forecast future sales, identify promising leads, or suggest products to show customers. Machine learning algorithms continuously learn as they are exposed to new data, meaning they get “smarter” every time the company uses them.

CRM platforms often use machine learning to predict customer behavior and recommend actions to sales reps.
Sales enablement tools use it to find content that helps reps sell on a personal level.
Email marketing platforms use it to optimize email cadences, subject lines, and content.

Sales reps and managers use the insights generated by machine learning to inform their strategies and make data-driven decisions. For instance, a sales rep might use a machine learning-powered tool to prioritize leads based on their predicted conversion likelihood.

Natural Language Processing (NLP)

NLP analyzes text data from unstructured sources (such as customer emails, chat logs, social media comments, and product reviews) contextually. It identifies and extracts customer sentiment and intent, as well as entity segmentation, including names, dates, and locations.

Sales teams can use this data to see which products customers are interested in buying, what kind of response they got from prospects during conversations, or how satisfied customers are with their product.

For instance, an NLP algorithm might analyze customer emails and categorize them based on whether they contain positive or negative sentiment, or whether they mention certain products or features.

Alternatively, they might use an NLP-powered Zoom plugin to search through sales calls and identify trends in customer conversations.

AI visualization

AI visualizations serve several purposes in the sales process:

Sales reporting. AI-powered visualizations allow sales reps to quickly identify trends and check the performance of their activities.
Revenue forecasting. AI-enabled visualizations can generate forecasts based on past sales data, allowing sales reps to plan their strategies accordingly.
Customer segmentation. By leveraging AI, sales teams can segment customers and tailor content and offers to different groups of buyers.
Product recommendations. AI-powered visualizations surface products that customers are most likely to buy, allowing sales teams to personalize their offers and maximize conversions.
Product configuration. Physical products vendors (such as B2B manufacturers) can help buyers visualize their products using CPQ software with a 3D product configurator.

Augmented analytics

Augmented analytics is the future of data-driven decision-making. It combines NLP, machine learning, and text mining to enhance data analysis processes.

It aims to improve user experience by removing manual efforts from the process. Augmented analytics platforms ingest vast amounts of data from multiple sources and analyze it in near real-time.

The insights generated by these platforms are used to create predictive models and generate actionable recommendations for sales reps.

Benefits of AI in sales

From 2018 to 2022, AI adoption in sales has increased by 76%, with high-performing sales teams 2.8 times more likely to use an AI-integrated sales stack.

There are several benefits of AI for sales, including:

More accurate sales attribution

One of the biggest points of contention between sales and marketing teams is which organization’s touchpoints had a greater impact on a sale.

In smaller organizations, it’s fairly easy to determine who is responsible. But as the sales cycle becomes longer, sales performance becomes increasingly difficult to attribute to any one source.

According to research from Rain Sales Training, it takes an average of eight touchpoints for sales reps to land meetings (or other forms of conversion). In some B2B sales processes, it can take upwards of 20 touchpoints to close a sale.

Machine learning models learn to analyze the impact of each touchpoint more effectively, giving credit where credit is due. And more importantly, sellers are more aware of which sales strategies actually improve the chances of closing a deal.

Accurate sales forecasts and reports

Sales managers need to report projections to executive leadership and use reliable data points to determine whether their sales team is on track. With software that uses deep learning models based on historical sales and customer data, accurate forecasts and reports can be generated at the click of a button.

AI also automates the creation of regular internal reports so that managers can check in on team performance without having to manually compile spreadsheets every week or month. This way, AI can save reps and managers time that they would otherwise spend on manual report consolidation and sales forecasting processes while ensuring the accuracy of its projections.

As these projections move their way up the rungs of the company hierarchy, executive leadership and investors can make better decisions about the future of the company.

Higher degree of customer engagement and personalization

Buyers want personalized interactions. For B2B sellers (i.e., the majority of sales reps in this context), personalization isn’t just about the product—it’s about how the customer is treated. 73% of B2B buyers say they want personalized experiences like those B2C customers receive, but only 22% say that sellers are meeting that need.

Enlisting the help of AI means SDRs can access valuable insights that enhance their lead engagement. They can use this information from the lead’s website use patterns, current solutions they use, and past digital interactions to personalize content recommendations based on their preferences and needs.

Before AI, sellers needed to frantically sift through emails, social media DMs, and CRM notes to prepare for their product demos and intro calls. With artificial intelligence handling the data, these data points are brought to a single source of truth.

With a 360-degree view of their customers, sales reps are more organized and productive.

Lower churn rates

A high churn rate holds companies back from sustainable growth, and often sales reps don’t have the data they need to spot customers at risk of churning.

Based on historical customer responsiveness, engagement, and consistencies among past customers that churned, AI-powered customer success models provide insights on which customers are likely to renew their subscriptions or contracts, as well as any that need extra attention.

Likewise, AI-powered customer segmentation models help sales and marketing teams discover patterns in customer buying behavior that indicate churn risk.

Using this data, SDRs can reach out to at-risk customers and offer discounts or other incentives to keep them from leaving.

Real-time feedback on sales calls

AI in sales gives reps real-time feedback during discovery calls and product demos. It picks up on small conversational nuances like their talking speed, tone of voice, and facial expressions, and provides feedback on how to adjust their approach, helping reps become better at building relationships with prospects.

AI also helps sellers understand their potential customers’ sentiments and body language. It can also guide their focus to the most important parts of the conversation to generate a more accurate picture of customer requirements.

AI also converts sales calls into written transcripts in seconds. Individual reps can review these to learn and find improvements, and sales leaders can use them to measure the overall performance of their sales team.

More time for sales activities

The average rep spends less than one-third of their time on sales activities—a clear indicator as to why 79% of sales team members report disengagement.

According to Salesforce, the three most common time-wasting activities are:

Managing emails
Logging activities
Inputting notes and sales data

AI-driven sales processes practically eliminate these tasks.

Logging activities like sales pipeline movement, customer interactions, and follow-ups can be automated. Notes are created and stored automatically in CRM. And email autoresponders can handle the first line of engagement from prospects, freeing reps to focus on more important tasks.

Use cases of AI in sales

Sales forecasting

Artificial intelligence systems exist that can predict or forecast outcomes using historical data to inform future results. Common predictions that sales AI systems can make include:

Deals or prospects most likely to close.
Deals or prospects to target next.
New customers may be interested in what you’re selling.

Now, the accuracy of those predictions depends on the system in use and the quality of the data. But the fact is that, with the right inputs in the past and present, AI is capable of showing you who is most likely to buy in the future.

Predictive forecasting can also create value for sales teams internally.

Using the same types of data analysis, AI can help sales managers forecast their team’s performance for the quarter well in advance, so they can take proactive steps based on the numbers.

Expert recommendations

Beyond prediction and prioritization, some AI systems may actually recommend sales actions, going so far as to tell sales teams which actions the system thinks make the most sense, based on your goals and insights from the data.

These recommendations may include advice on how to price a deal, who to target next, or which customers to target first with upsells or cross-sells.

The result is targeted guidance on what actions to take. With it, salespeople can free up bandwidth to close deals, rather than deliberating about what to do next.

Lead scoring & prioritization

Artificial intelligence can look dispassionately at large datasets from a number of sources and tell you which leads you should prioritize, based on the scores the AI has given them.

As noted by Victor Antonio in the Harvard Business Review, human salespeople usually approach lead scoring and prioritization in an unscientific way:

“Often, this decision-making process is based on gut instinct and incomplete information. With AI, the algorithm can compile historical information about a client, along with social media postings and the salesperson’s customer interaction history (e.g., emails sent, voicemails left, text messages sent, etc.) and rank the opportunities or leads in the pipeline according to their chances of closing successfully.”

In this case, AI can bring a level of logic and standardization to the process that humans just can’t match.

Sales automation & productivity

AI can also automate or augment your work to take away some of the drudgery that distracts you from higher-value tasks. It can also help with everything from managing your calendar to scheduling meetings to assessing a sales team’s pipeline by automatically doing these things for you or making them dramatically easier by using your historical usage data to make decisions.

This use case is very similar to how some consumer calendar and productivity apps work, recommending recurring events or to-dos dynamically thanks to AI.

Tips for implementing AI in sales

Want to start implementing AI sales tools for your organization? Here are a few tips for successful implementation.

Define your goals. Before you begin implementing AI tools, it’s important to define what you want to achieve with those tools and identify the areas where AI will add the most value to your processes. Defining your goals early on gives you something concrete to base decisions on, enabling more consistent results.
Choose the right AI tools. One of the most crucial steps for implementing AI for sales is choosing your tools. Make sure you put adequate time into researching your options and choosing tools that fit your needs and budget.
Set realistic expectations. When looking into AI tools, research their capabilities and limitations so you have realistic expectations of what they can and can’t do. It’s also important to have realistic timelines for how long it will take to implement AI tools into your sales process.
Ensure quality data. To get quality output from AI, you need quality data. It’s often helpful to provide AI tools with data from your organization to make the outputs more specific to your needs. Ensure you have high-quality data by using a quality CRM and managing it according to data management best practices.
Spend time on training. To get value from your AI tools, your team needs to fully understand why and how to use them. Invest time in training to ensure your team is on the same page and has the knowledge they need to get the most value from the tools.

Sales AI: How To Use AI In Sales, Benefits & Challenges

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Understanding the concept of AI in sales