CRM and customer experience (CX) vendors are retooling their platforms to better integrate with existing business applications without employees realizing they’re using generative AI-powered capabilities.

This shift will improve productivity as it changes how businesses operate. As the technology becomes more embedded in daily workflows, those who fail to adopt it will be disadvantaged.

According to Paul Farrell, chief product officer at SugarCRM, as organizations grapple with large amounts of data, generative AI provides tools to understand the vast amounts of information and determine the best course of action.

He notes that generative AI rapidly evolves into a vital technology driving real business value. Some of its most impactful technologies are also the most challenging to set up.

“Nurturing campaigns, workflows, and business analytics, for example, can be incredibly powerful tools, but configuring them can be complex, especially if you don’t have the right resources,” he told CRM Buyer.

Take, for example, a prompt to generate a detailed analysis of how leads have evolved over the past three months. Generative AI simplifies the process, making it much easier for businesses to harness the full potential of these technologies.

However, Venki Subramanian, SVP of product management for CX at Freshworks, noted that simplifying CRM software is challenging because it involves balancing the needs of diverse users while maintaining ease of use.

CRM systems must manage vast amounts of customer data, automate workflows, and provide insights, which can overwhelm users if not designed thoughtfully. He sees the real challenge in creating software that meets the needs of sales teams, customer service agents, and marketers without sacrificing usability.

“The key is not just getting users to adopt the software because it’s required but making them want to use it by creating compelling user experiences that enhance productivity and efficiency,” he told CRM Buyer.

Making AI Work for CX Without Replacing People

Subramanian says the leading challenge is ensuring AI works for people, not vice versa. That means designing it to complement human decision-making rather than attempting to replace it entirely.

The goal is to use AI for tangible productivity gains or to augment decision-making rather than using it for the sake of technology. The challenge is discovering how to apply AI to improve productivity and efficiency in the service of people.

“Once that’s clear, we can talk about integration. Integrating AI into CRM systems can be challenging because AI’s effectiveness is highly dependent on data quality,” observed Subramanian.

Many organizations struggle with incomplete or inaccurate customer data, which limits AI’s ability to offer meaningful insights. To maximize AI’s potential in CRM, he added that companies need to invest in the right technology, establish robust data governance, build ethical frameworks, ensure privacy, and maintain human oversight, all while supporting responsible AI use.

One way CRM vendors are approaching that integration is by fueling a shift from CRM control to salesperson engagement, suggested Farrell. Modern CRM solutions are evolving to focus on delivering to salespeople the tools they need to succeed. These include mobile applications, revenue intelligence, and AI-driven insights.

Technologies such as IoT, AI, machine learning, and analytics ensure that salespeople know what to focus on and how to engage more effectively with customers. In this new environment, CRM is no longer just about managing pipelines or tracking opportunities. He explained that it is about optimizing the experience for personalized success, making the salesperson’s job easier, and enhancing customer interactions.

“We will see significant strides in how these technologies come together to improve how salespeople engage with customers. Mobile apps, tablets, browsers, and AI-powered analytics will drive more context-aware experiences,” he predicted.

This move toward more intuitive and intelligent systems addresses a significant barrier that has kept sales teams from fully embracing CRM — the need to input data manually. With AI and automation embedded in these systems, salespeople will no longer have to be data scientists to make sense of massive amounts of information.

“Instead, they will receive actionable insights in real time, enabling them to implement strategies quickly, close deals faster, and ultimately, look good while doing it,” said Farrell.

Balancing CRM Data Use With Responsible AI

According to Farrell, one of the developing strategies is for businesses to do more with less. They are shifting to maximizing the value of existing assets rather than constantly pursuing new ones.

“This is especially true for customer data platforms (CDPs) and revenue intelligence tools, which allow businesses to leverage the data they already have to drive growth,” he interjected.

For example, companies are looking to optimize their existing customer base. They focus on selling more to their current customers or helping low-performing salespeople perform better.

Farrell sees CRM solutions focusing on enhancing these existing assets — data, people, and relationships — by enabling smarter decisions rather than just capturing new customers. This approach will empower organizations to do more with their existing resources, creating more efficient and effective sales processes.

Businesses are also becoming more cautious about sharing and utilizing the data AI processes gather, particularly with the rise of public generative AI platforms. The positive side of this trend is that companies will use tools such as CDPs and revenue intelligence to drive greater performance. The negative side is that they will be increasingly protective of their data, implementing tighter controls to ensure security and privacy.

Farrell surmised that data is no longer just a byproduct of business activities. It is a valuable resource that organizations are safeguarding like gold.

“As a result, companies will need to balance enabling their sales teams with the responsible use and protection of customer data, ensuring they don’t leave money on the table while safeguarding their most valuable asset,” he concluded.

Matching AI Capabilities to CRM Needs

AI comes in various forms. In isolation, it is not always best suited for CRM. According to Subramanian, each type of AI serves a unique purpose.

Generative AI stands out. He added that it has unlocked new possibilities and accelerated the adoption of AI within CRM, particularly in automating tasks.

Predictive AI is invaluable for anticipating customer behavior, enabling proactive decision-making, and personalized engagement. Natural language processing (NLP) is crucial for improving AI agent interactions and customer support, allowing AI to better understand and process human language.

“When integrated effectively, these technologies transform CRM systems into powerful tools for personalization, efficiency, and customer satisfaction,” said Subramanian.

He noted that Freshworks concentrates on simplifying the user experience. Its AI suite, known collectively as Freddy, is designed to enhance customer and agent interactions across the support journey:

• Freddy Agent: A generative AI-powered assistant that autonomously handles customer requests across channels and resolves many issues without human input. It anticipates needs and guides users to the correct answers or actions.
• Freddy Copilot: Supports customer service agents by summarizing issues, recommending responses, and offering coaching to ensure quality.
• Freddy Insights: Provides valuable data to agents and supervisors, helping them improve efficiency and enhance customer satisfaction.

“Together, these AI technologies work seamlessly to optimize CRM and deliver exceptional results,” he said.

Guardrails for Using AI in CRM Platforms

Subramanian argues that the most critical guardrails for AI in CRM platforms are ensuring accuracy and maintaining responsible data use. AI must provide reliable results. Incorrect suggestions or hallucinations can cause significant issues when businesses rely on AI to take action.

For example, if an AI agent incorrectly states that returns are processed in seven days instead of the 30-day policy, it can mislead customers and damage the brand’s reputation. Ensuring accuracy is vital, and organizations should set clear performance thresholds based on industry and use case.

“Additionally, human-in-the-loop oversight is crucial in verifying large refund requests, e.g., over $200, to ensure AI decisions are validated when necessary,” he added.

Data governance, transparency, and ethical AI use are essential in CRM. AI systems require close monitoring to avoid biases, particularly when customer data influences decision-making. Privacy and security are also critical, especially in regulated industries.

“AI platforms must provide transparency about how customer data is used and how AI-driven decisions are made, maintaining trust and ensuring compliance with data protection laws,” he urged.