AIMultiple ResearchAIMultiple Research

AI in Sales: 15 AI Sales Applications/ Use Cases in 2024

Cem Dilmegani
Updated on May 9
5 min read
AI in Sales: 15 AI Sales Applications/ Use Cases in 2024AI in Sales: 15 AI Sales Applications/ Use Cases in 2024

Sales is hard, especially in B2B. Conversion rates are low and the sales cycle is longer. Your customers do not just take out their credit cards to buy things. They need hand-holding and a lot of validation. Sales leaders need to make calls, meet them in person, answer their concerns and continue to guide their customers after sales to ensure that you build a healthy relationship with them.

Artificial intelligence can help improve these sales processes. According to a study by Harvard Business Review, companies using AI in sales were able to increase their leads by more than 50%, reduce call time by 60-70%, and realize cost reductions of 40-60%.

How can AI support sales?

As a sales leader, you may be hearing that artificial intelligence will rule the world. You imagine a future where all sales are done by cheap yet effective AI assistants. We are not there yet essentially because AI is not as mature enough to handle complex conversations and relationship building required in sales. AI today does not aim to replace sales reps but acts as an assistant to:

  • Help them automate repetitive tasks like data entry and meeting scheduling or complicated jobs that do not require personal relationships like sales forecasting
  • Enable them to prioritize more effectively and become a better salesperson by highlighting patterns in customer responses
  • Provide team leaders with detailed analytics on all communication between sales reps and potential clients including emails, phone calls and chats.

We have identified 15 artificial intelligence use cases and structured these use cases around 4 key activities of today’s sales leaders. We are currently focused on inside sales, for example, a retail sales function has different main activities and therefore different AI use cases. Our framework is by no means comprehensive but it is ever improving so please let us know if you have any comments and suggestions.

Though AI applications are numerous, correct prioritization is key to success. Process mining can help sales teams to automatically monitor and manage their sales operations by extracting and analyzing process data from CRM, other relevant IT systems, and documents.

Primary sales activities and AI use cases in these activities are:

Forecast sales more accurately

1. Demand forecasting

Forecasts are complicated but automatable. AI allows automatic and accurate sales forecasts based on all customer contacts and previous sales outcomes. Give your sales personnel more sales time while increasing forecast accuracy. For more information on AI-powered demand forecasting, feel free to check our article.

Help sales reps with leads

Better prioritization can enable sales reps to better use their time. Sales reps normally leverage their experience from the last 5-10 years to decide which prospect to focus on. However, AI systems can leverage data from hundreds of sales reps to understand the factors that increase a prospect’s likelihood to buy and help your sales reps focus on the right prospects.

2. Lead generation

If you like your sales reps, give them leads! Without leads, sales reps spend precious time searching for leads instead of closing deals. For more info, please visit our explanatory article about lead generation.

3. Predictive sales/lead scoring

After lead generation, it is necessary to determine the priority of leads. These platforms score customers’ likelihood of converting based on 3rd party and company data, allowing your sales reps to prioritize effectively. For more info, please visit our explanatory article about predictive sales.

Another source of data for lead prioritization is your company’s traffic. Website identification tools can help businesses manage the prioritization of leads using how potential customers interact with your company’s digital properties. These tools enable you to identify leads that spend time on the company website and provide company contact information. You define the criteria of what a high-quality lead looks like and then these platforms send “trigger reports” into your sales reps’ inbox automatically.

4. Sales content personalization and analytics

Once priority customers are decided, sales reps serve them better with sales content personalized to their needs and preferences. Leads’ engagement rate increases with personalized content, businesses convert visitors and retain customers.

5. Sales rep next action suggestions

AI will analyze your sales reps’ actions and leads will be analyzed to suggest the next best action. No one wants to waste time on email setting up a demo, when they could be closing another deal.

Automate mundane sales activities

Simple activities or activities that do not require relationship building can be automated.

6. Sales data input automation

AI will synch data from various sources effortlessly and intelligently into your CRM

7. Sales rep response suggestions

AI will suggest responses during live conversations or written messages with leads

8. Meeting setup automation (digital assistant)

Leave AI to set up meetings freeing your sales reps time. For example, Calendy links emails and conversations to your calendar while Clara responds to your emails and organizes your meetings.

9. Sales rep chat/email bot

Business leaders claim that chatbots can increase sales by 67% on average. This is because a sales chatbot can help break the ice with a personalized message, making it easier for the customer to either engage at that moment, or return to the chat later. AI algorithms can also create customized emails that are specific to a person and help sales reps outreach prospects without wasting time writing numerous emails.

10. In-store sales robots

This is mostly relevant in B2C. Physical bots are deployed in various types of stores. Lowe’s has been experimenting with LoweBot in collaboration with Fellow Robotics since 2016. Given the costs and difficulty to replace humans in diverse tasks, it seems that these bots are going to remain niche in the next few years.

11. AI Avatar

As your sales AI Avatar learns, it gets more intelligent and automatically creates digital marketing interactions with leads. This is another application that can increase the engagement of customers since humans are more comfortable interacting with human-like beings. For example, Dave.ai is an AI Avatar vendor that helps businesses visualize home lifestyle products in concept rooms with the help of VR and provides AI-powered recommendations.

Improve sales analytics & performance management

12. Sales attribution

Leverage big data to attribute sales to marketing and sales efforts accurately

13. Customer sales contact analytics

Analyze all customer contacts including phone calls or emails to understand what behaviors and actions drive sales. Share these insights with all your sales force to promote productivity.

14. Price optimization

Machine learning based dynamic pricing tools scrape the web automatically to gather data on competitors and provide pricing recommendations based on competitors’ pricing information and individual customer’s price perception.

15. Layout optimization

In B2C retail sales, AI-powered analytics helps businesses optimize in-store/ web page layout based on customer behavior data.

For more, feel free to read our article on sales analytics.

Now that you know about AI applications in sales, you can read more about these applications in our section on AI in sales. And you can discover all of the latest AI-powered sales assistant software on the market and see how you can bring artificial intelligence into your work environment to make it more efficient and innovative.

If you believe you can benefit from AI in your business, you can view our data-driven lists of Data Science / ML / AI Platform, and AI Consultant. Also, don’t forget to check out our sortable/filterable list of sales intelligence software vendors.

And if you have a business problem for which there could be an AI based solution:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
Follow on

Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read

Comments

Your email address will not be published. All fields are required.

2 Comments
Linda
Aug 27, 2022 at 08:05

Great article!

Just an addition for your next edit

Dealcode GmbH – is an AI Guided Selling Software that extracts data from CRMs, running its patent AI and machine learning model. It is a predictive analytics tool that determines the winning probability of prospects and risks in the selling pipeline. It provides sales teams with up-to-date information on what deals they should focus on and who to talk to urgently. This predictive analytics is done by analysing sales processes using a patented machine learning model. Dealcode determines individual factors that contribute to the success or failure of a sales team. As a result, it makes sales measurably more effective. In addition, to saving cost-intensive resources for complex data analyses.

Cem Dilmegani
Aug 30, 2022 at 09:25

Thank you for your comment!

nouraai
Jul 22, 2021 at 07:10

Great article! You can define the parameters of forecasting big and small businesses that are very informative for every businessman. If you want to know more about AI Sales Forecasting visit our website.

Related research