CRM for Customer Service: AI, Other Trends, & Providers

As CRM systems swallow up more of the service stack, they are becoming increasingly central to day-to-day contact center operations.

The latest AI innovations are helping to drive that trend forward, especially around conversational intelligence, which helps secure new intent, sentiment, and behavioral data.

However – alongside AI - there are several other critical trends. From native voice to low-code orchestration, CRM for customer service is a blossoming field.

Thankfully, our nine industry experts for this month’s roundtable have their fingers on the pulse. Each comes from a prominent CRM provider within the space. They are:

• Steve Rycroft, VP & GM UK at Freshworks
• Brad Birnbaum, CEO and Co-founder of Kustomer
• James Dodkins, Customer Experience Evangelist at Pegasystems
• Mara Vicente, VP of Customer Support at Pipedrive
• Paul O'Sullivan, SVP at Salesforce
• Shardul Vikram, CTO at SAP I&CX
• Simon Morris, VP of Solution Consulting at ServiceNow
• Stuart Hall, SVP of Customer Success at SugarCRM
• Suvish Viswanathan, Head of Marketing UK & Europe at Zoho

Below, these specialists share the most pervasive trends from the CRM for customer service space before highlighting what differentiates the providers they work for.

CRM for Customer Service: AI Trends

AI Pulls CRM and ERP Systems Together

Vikram: AI is transforming how service teams interact with their customers by providing powerful, data-driven recommendations, insights, and a more holistic view of customer relationships.

Yet, businesses should consider a CRM platform that connects customer conversations to relevant enterprise data. Typically, that data lives within an ERP system.

Combining enterprise-wide data with generative AI delivers insights to customer service representatives’ fingertips, including a holistic view of the customer and how best to resolve a customer’s concern.

Some of the key AI-driven capabilities for service teams are automated case and interaction summaries, generative answering, ticket categorization, and next-best actions.

Thanks to the integration of these AI capabilities with business data, the service agent sees the complete 360 profile of the customer.

Consider a customer call for a problematic refrigerator. With that 360 profile of the customer, the agent doesn’t need to verify the product’s warranty status. AI has analyzed the customer's purchase history and product details to inform them if it is under warranty. As such, the agent can swiftly start the replacement process, if needed.

AI Generates More Customer Context

Morris: The modern consumer shows a clear preference for hyper-personalized experiences, now more than ever before. This is where AI comes into the equation.

By analyzing vast amounts of customer data, such as browsing behavior and purchasing history, CRM providers build a clearer understanding of their customers’ history through buying patterns.

Understanding the customer is the first step of the journey. CRM providers can leverage AI-driven recommendation engines to suggest products or services tailored to unique customer preferences.

As a result, they may provide customers with the hyper-personalized experiences they seek, ultimately improving overall customer satisfaction.

Native Copilots Become All the Rage

Rycroft: An AI copilot can significantly enhance productivity for customer service teams. These assistants, always available, summarize conversations, suggest wording tweaks, improve tone, and prompt next steps, leading to better experiences for both employees and customers.

Moreover, copilots offer real-time guidance that increases efficiency and saves time. With embedded machine learning, they also continuously improve, helping service desk operators handle complex interactions by understanding context and providing relevant responses.

Additionally, these assistants streamline processes through automated ticket routing, ensuring tickets are assigned to the right agents based on skill, workload, and predefined rules.

Lastly, they utilize predictive analytics and personalization capabilities to analyze past trends, optimizing service for each customer.

AI Auto-Summarizes Customer Cases

Hall: Case summarization is an excellent example of how AI is helping Customer Service teams deliver better outcomes for customers.

Summarizing large amounts of information such as emails, case notes, and other data enables service agents to get up to speed very quickly on unfamiliar cases, preventing the customer from having to repeatedly give the same information and moving cases forward faster.

AI-based summarization can also provide a more consistent structure which can be used to build better knowledge bases.

Lastly, summarized cases can be used to improve the training and onboarding of new agents, enabling them to get up to speed faster.

Conversational AI Comes to the Fore

Birnbaum: AI chatbots are replacing the need for customer service agents to answer every customer query by automating responses using generative AI. This helps service teams scale without additional headcount and also allows agents to focus on handling more complex issues.

At Kustomer, we’ve already seen our AI chatbots cut service queries by 45 percent.

By integrating AI chatbots with CRM data, the responses are much more relevant to the customer and the situation's context.

Also, if the bot transfers the customer to a live agent, then AI can quickly summarize the conversation for the human agent to get up to speed quickly, and not require the customer to have to repeat him/herself.

Meanwhile, AI boosts productivity by 65 percent for agents by using CRM data to suggest contextually relevant responses to customers in their local language.

O'Sullivan: The 2024 Salesforce State of Service Report found that 93 percent of service professionals at organizations investing in AI say the technology saves them time on the job.

Chatbots that automate routine tasks and provide AI-generated answers to common customer queries are a significant part of this. They free up customer service agents' time to focus on more complex issues that require a human touch.

In addition to facilitating simple, consistent, and smooth implementations, advanced chatbots support a variety of languages and communication channels, enabling customer support personnel to provide quicker and more individualized services.

Viswanathan: Customer service chatbots can use AI to aid customer service. Rather than needing staff to manage every inquiry, AI-enabled chatbots can handle initial requests to streamline the process and create a more effective system for customers and staff.

In businesses with high customer inquiry volumes, AI-powered chatbots can significantly reduce the time of response, boosting customer satisfaction.

Typically, these chatbots are trained with a pre-defined script and set of rules and handle the first line of customer interaction. However, with advancements in technology, the whole approach can be made more intelligent, personalized, and engaging.

This speeds up customer response time and frees up customer service teams, who can be deployed to higher-complexity cases referred by the chatbot if it doesn’t have the necessary information.

Finally, GenAI-enabled chatbots can summarize and review conversations while serving up customer sentiment insights.

Hyper-Personalization Becomes Paramount

Dodkins: AI is aiding customer service teams by creating personalized experiences. Consider AI tools that analyze the heaps of customer data that businesses collect to provide tailored product and service recommendations and the next best steps at the right time.

Personalizing each interaction makes customers feel as if they are individuals, which leads to much higher satisfaction and loyalty.

In addition, predictive analytics, powered by machine learning and process AI capabilities, can be used to create proactive customer service practices. These AI tools can predict customer needs and behaviors by analyzing past interactions and resolving issues even before they arise.

Providing this level of tailored interaction requires enhanced data management, so implementing AI right into the heart of CRM capabilities ensures that customer service agents don’t need to manually sort and analyze data. Instead, they can spend their time doing higher-value tasks.

AI Offloads the Simple Tasks

Vicente: By handling the simplest tasks, AI for customer-facing teams enables them to be faster and better equipped to deal with the complexities of their work.

Indeed, teams using AI are able to leverage technology to enhance customer relationships and make human interactions as meaningful as possible.

According to Pipedrive’s recent 2024 State of Sales and Marketing Survey, an overwhelming 83 percent of respondents from businesses of all sizes believe AI will play a crucial role in business, with 35 percent already incorporating it into their daily tasks.

To put these use cases into perspective, Pipedrive has released an AI suite as part of its CRM designed specifically to help customers operate more efficiently.

The "Write My Email Using AI" feature enables users to draft emails with customized prompts and other user-defined criteria with the click of a button. As such, agents can write "enticing" emails in only 44 seconds.

CRM for Customer Service: Other Trends

Democratized CRM Systems Grow in Prominence

Viswanathan: Many businesses fail to unify various customer touchpoints within a centralized system. This can result in disjointed customer engagement, leading to an inconsistent and sometimes negative experience.

Democratized CRM systems are one solution, offering all customer-facing staff relevant access to provide a consistent, unified experience.

Importantly, this can allow sales teams, who predominantly oversee customer relationships, to communicate and coordinate a customer’s deliverables, such as onboarding, contract management, and solutions engineering, all from one place and enable anyone else dealing with a customer to see the entire customer journey.

This democratized approach improves visibility for every stakeholder in the customer journey, mitigates gaps in coordination, reduces turnaround time, and improves the experience.

Workflow Integration and Automation Remain Critical

Vicente: Beyond AI trends, streamlining CRM workflows remains crucial for maximizing efficiency and effectiveness. Automation in CRM is top of mind for sales professionals.

According to Pipedrive’s recent State of Sales and Marketing Report, 81 percent of respondents indicated that they use automation tools directly integrated within their CRM.

To stay competitive as a CRM provider, easy integration of automation into CRM software is key.

When all integrations can interact with each other, customers can sync their business data in one place. This enables different teams to have a comprehensive view of all data across platforms, facilitating the effective management of customer relationships and empowering CRM professionals to make the best business decisions.

CRM Unites Service, Sales, and Marketing Data

Vikram: Beyond AI, organizations are searching for CRM platforms that offer a holistic view of customer experiences.

To do so, they must integrate data from sales, marketing, and operations to reduce silos, increase collaboration, and inform customer interactions.

This can improve productivity for customer service teams by streamlining repetitive tasks and increasing resources spent on high-quality service.

An integrated CRM platform can also adapt to the ever-changing needs of customers and instantly provide updates to all teams.

From there, customer service professionals can provide responsive and comprehensive assistance as they can anticipate and prepare for opportunities and potential challenges across the business.

Managing Different Types of Data Becomes Table Stakes

Birnbaum: Newer business models such as on-demand delivery services and marketplaces seek one system to manage relationships across multiple parties - not just customers, but also partners and other business sellers.

In these models, multiple stakeholders interact with the customer across different branded channels.

These businesses need a CRM that is flexible enough to ingest, organize, and manage all these different data types while giving the right visibility to the data to protect customer privacy. This is massively complex.

Omnichannel Holds Firm as a Critical Focus

Dodkins: One non-AI-related trend impacting CRM for customer service is omnichannel operations, which create seamless integration across all channels.

These interactions are personalized, consistent, and continuous regardless of which touchpoints the customer chooses, such as in-person, online, mobile app, email, or phone.

As such, customers can choose the channel that works best for them, which increases satisfaction.

With omnichannel CRM systems, all customer interactions are tracked, so organizations can better map their entire customer journey.

This is especially beneficial when businesses are looking to identify pain points and ways to improve their service quality and build stronger customer relationships.

Not only that, as all customer interactions are stored in one place it allows customer service agents to quickly access information, which increases efficiency when helping customers.

"Whitespace Analysis" Becomes a Critical Initiative

Hall: The deeper CRM and ERP data integration that allows companies to do "whitespace analysis" and more effectively discover hidden upsell, cross-sell, link-sell, and switch-sell products and services.

Together, the solutions bring the front-office (CRM) and back-office (ERP) together for greater data visibility and to proactively interpret the information into actions for more impactful sales strategies to drive revenue, profitability, and customer retention.

Service Teams Connect Experiences by Overcoming Data Silos

O'Sullivan: Traditionally, customer service teams have struggled with data silos.

According to Salesforce research, 81 percent of IT leaders report data silos are hindering digital transformation efforts - causing fragmented experiences where customers are repeatedly asked for the same information by different departments.