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AI-powered CRM Systems in 2024: In-Depth Guide

Updated on Feb 5
6 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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AI-powered CRM Systems in 2024: In-Depth GuideAI-powered CRM Systems in 2024: In-Depth Guide

The rise of Artificial Intelligence (AI) pushed the boundaries of customer relations, providing companies rich source for analyzing and understanding customer data. However, However, according to the research by Freshworks, only 12% of CRM users actually use an AI-powered CRM tool in their processes. Research also shows that 40% of buyers want to invest in a CRM platform that is suited for their needs and accelerate their productivity.

As CRM tools become more intelligent, they offer more accurate sales insights and helps companies make better decisions in sales processes. Considering the increased volume of unstructured data and the growing complexity of customer relations/processes, AI technology is becoming a necessity in CRM systems.

Many users think that AI-powered tools are too expensive or aren’t extremely useful considering the price today. Thus, we still need to have more time to observe AI’s actual impact on sales processes. 

In this article, we will provide:

  • Leading vendors on AI-powered CRM software in the market,
  • Why businesses should use AI-powered CRM software,
  • Use cases of AI-powered CRM.

Please note that we might use the terms, “AI-powered CRM,” and “CRM automation,” interchangeably.

What are the leading vendors that offer CRM automation use cases?

CRM vendor market includes a wide range of solutions to support businesses. You can have the sortable list with more than 300 CRM software. Here are the leading CRM vendors that leverage AI technologies in their CRM solution:

VendorsNumber of reviews*Average ratingLow-code / no-code developmentPredictive lead scoringNext action recommendationsCall data entry & collection automation
Pipedrive4,9484.3/5
Salesforce Sales Cloud36,4754.3/5Low-code
ClickUp13,1184.6/5Not provided
HubSpot Sales Hub11,3404.4/5Not provided
Zoho CRM9,6614.1/5
ActiveCampaign for Sales2,3174.3/5
Freshsales1,7614.3/5
Quickbase1,7244.5/5
monday.com CRM1,0964.5/5
SAP Sales Cloud8584.1/5Not provided
Zendesk Sell5484.1/5Low-code
Creatio3904.6/5

*Based on the total number of reviews on software review platforms G2, Capterra, and Trustradius.

Note: With the sponsors at the top, we sorted the list based on number of reviews in a descending order.

If you plan to invest in a CRM solution, check out our benchmark on CRM software in the market.

Why should businesses integrate AI into their CRM tools now? 

There are four main reasons for CRM automation:

1. The increasing volume of unstructured data that is infeasible to process without AI/machine learning

With the growing amount of transactions, the size of customer data also increases. This increase can help businesses understand their customers better, as they can process more information about them. However, it also means that they need to work more to extract relevant information because the majority of the data remains unstructured. While it is challenging to understand unstructured data, which constitutes around 90% of the total data, AI tools can convert unstructured data to structured data.

Source: M Files

After converting to structured data, machine learning algorithms can detect patterns and provide vital insights for businesses. Considering the growing amount of data, AI technology offers scalable solutions for companies and enables them to handle a higher volume of data rapidly and with fewer errors.

2. The increasing need for data scientist talent

The growing demand for data analysis in CRM systems has spiked the need for data scientists, specialists skilled in extracting insights from intricate data. However, hiring such talent can be pricey and challenging due to their limited availability. This gap is efficiently bridged by no-code/low-code AI platforms. These platforms empower businesses to integrate AI into their CRM without the need for deep technical expertise, automating tasks like lead scoring and customer segmentation, thus offering an non-technical employees flexibility while customizing CRM platform.

Check out our article to learn more about no-code CRM.

3. The increasing complexity of relationships

Besides the growing volume of data and IT employees, the business processes and relationships also become more complicated with the increasing transactions. This complexity makes it harder to understand company relationships and analyze customer patterns accurately. According to Xant, sales representatives spend over half of their time spent in CRM for trying to manage CRM tasks more effectively. AI technology can easily surpass this challenge by automating most of these tasks and offering valuable insights.

4. The increasing popularity

As seen above, the interest in AI-powered CRM tools is expanding since the middle of 2022. We can relate this increase to the following reasons:

  • The advances of AI enable this technology to be integrated into the CRM tools, and these tools are becoming more preferred by businesses.
  • The impact of AI in CRM tools is observed better as processes become more complex, and the amount of customer data increases.

What are some AI applications in CRM?

AI in CRM

Interface.ai shares that 87% of sales teams are not satisfied with their CRM. This is due to manual tasks performed in processes or difficulty in using CRM tools. Thus, the integration of AI might help businesses improve their CRM processes and reduce human intervention while performing specific tasks like manual data entry. As a result, employees can focus more on higher value-adding activities and improve their productivity. Here are the main CRM automation use cases:

Sales

Sales forecasting

Sales forecasting is one of the most critical and wide-spread features of CRM tools. With AI, these tools can provide more accurate forecasts. AI can detect patterns in customer sales data and offer valuable insights about sales predictions. With these forecasts, businesses can make their plans accordingly and optimize their sales processes.

For instance, Salesforce released Einstein GPT, a generative AI technology, and integrated it into their CRM systems to automate processes such as real-time analyses to predict sales.

Check out our article to learn more about generative CRM and its applications.

Predictive lead scoring

AI tools can analyze customer sales data, including demographic data, geographic data, activity data, and web behavior, to determine their buying readiness. Companies can analyze won versus lost deals to detect trends that can inform predictive lead scoring methods. Feel free to read our research on the topic to learn more.

Customer churn reduction

As most company revenues come from existing customers, it is critical to prevent customer churn for businesses. AI-powered tools can go through customer data to analyze specific patterns and identifying the reasons for customer churn. As a result, companies can take concrete actions to reduce customer churn effectively.

Marketing

Lead qualification

Sales representatives spend only 32% of their time selling or pitching tasks and 20% of their working hours for managing their CRM-related tasks like producing reports and other administrative duties. Businesses should create more time for their sales teams in sales processes and reduce their workload for their other responsibilities.

During lead qualification processes, AI-powered CRM tools can automate the majority of analyzing needs. They can leverage chatbots and email bots to understand leads’ needs and inform the sales teams to improve their performance. With insights gained from these bots, companies can optimize their sales processes.

Sentiment analysis during calls

In sales, understanding customer sentiment is critical for developing trust with them. As it is harder to build trust without face-to-face interaction, HubSpot Research shares that only 3% trust salespeople. AI can offer a solution to solve this problem. AI-powered tools can analyze customer conversations during calls and assess emotional states using sentiment analysis.

As an example, Cogito offers real-time conversational analyses to evaluate customer emotions, how effective the calls are, and how best to respond to them. According to the company, understanding customers’ emotional states helps businesses increase their revenue per customer by 10%, according to the company.

Accelerated content production

With the integration of natural language generation platforms, CRM tools can automatically organize personalized emails, reviews, and client reports. This characteristic can also be used in preparing descriptions for specific products, landing pages, social media posts, and news articles. Wordsmith, developed by Automated Insights, can be implemented in CRM tools and automate employee emails, as seen on this page.

Recommendation systems

While CRM systems use customer data to understand customers better, AI can discover their needs or desires to offer a personalized experience. Customer data to understand such patterns includes their information like age, gender, region, as well as their sales history and online interactions. As a result, your company can offer a personalized experience to your customers and recommend products based on their needs. Salesforce shares that personalization can improve sales by 10%.

Virtual Assistants

We can group virtual assistants leveraging CRM data in two main categories:

  • In-Office Tasks: Virtual assistants can handle simple in-office tasks like managing schedules meetings, taking notes, and notifying follow-ups in a CRM system.
  • Intelligent Call Routing: Based on CRM data, AI can interpret natural language queries for customer segmentation and handle customer calls to support call centers in simple customer tasks.

Data

Data cleaning

Customer data can include many irregularities, anomalies, duplicates, and other errors, which may cause inaccurate predictions. According to Dun&Bradstreet, 91% of data in CRM systems is incomplete, 18% is duplicated, and 70% is rendered stale each year. To improve decision-making quality, an AI-integrated CRM system can:

  • Detect potential issues
  • Clean the duplicated data
  • Notify the users to correct the errors
  • Look for incomplete data in other systems
  • Suggest actions to update potentially stale data

Data entry

While data entry is one of the most repetitive and tiresome tasks in businesses, AI-powered takes it over and allow employees to focus on higher-value adding tasks. Data entry includes entering customer data in the desired format and automatically capturing data from SMSs, calls, emails, images, etc. with document capture, image recognition, and speech recognition technologies.

Transparency statement:

Numerous tech companies sponsor AIMultiple, sponsors receive links from AIMultiple research.

If you have questions about AI-powered CRM software, don’t hesitate to contact us:

Find the Right Vendors

CRM usage stats: Getbase

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
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Cem Dilmegani
Principal Analyst

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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2 Comments
Kuldeep Kundal
Jan 20, 2022 at 10:56

Good thing I have read this kind of article. It was very helpful, including your graphic research, it makes me understand it better. Thanks for sharing!

Bardia Eshghi
Aug 23, 2022 at 09:37

Thanks, Kuldeep! We aim to please.

Vassili
Mar 19, 2021 at 22:48

You are missing C3 AI’s offering

Cem Dilmegani
Mar 20, 2021 at 06:21

Thanks! They can add themselves on https://grow.aimultiple.com

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