The availability of foundation large language models (LLMs) and AI tools poses a challenge to enterprises looking for differentiation. If competitors can access broadly similar technology, the model alone may not offer much long-term advantage.
But as AI tools become widely implemented throughout the customer experience, the hard-to-replicate advantage for enterprises may be in what they teach those tools about their own customers and operations based on the data in their customer relationship management (CRM) systems.
Businesses have accumulated years’ worth of customer information across CRM platforms as well as sales and support tools, but much of that data has never been used to its full potential because it is incomplete or trapped in disconnected systems.
AI changes the equation, as it can work across the information, help clean it up and turn customer interactions into a source of institutional knowledge. There is an opportunity to build AI around what is unique to business rather than relying on capabilities that competitors can also access, John Cheney, CEO and Founder of Workbooks, told CX Today.
“One of the challenges with AI generally is everyone's got access to the tools, so they reduce the ability for them to be competitive. [But] AI as a tool to create knowledge, I think is a really interesting strategy for businesses because it creates competitive advantage.”
“If you can build knowledge inside your business using AI to help you be more effective and create competitive advantage, that's a really valuable way of driving an AI strategy that creates value for your business,” Cheney added.
The Data Advantage Starts Inside the CRM
AI is only as useful as the information it can access. A CRM containing little more than contact details and pipeline records provides limited context for an AI agent trying to understand a customer.
“To make AI work well inside CRM, you ideally want all your customer information inside the platform,” Cheney said. “With that much richer data set, AI becomes much more valuable.”
That broader picture can include order and invoice information, sales activity and other records that show how the relationship has developed. “So not just the names and the phone numbers and email addresses, but also order information, invoice information, pipeline data,” Cheney explained.
The challenge is that many businesses are starting from a weaker data position.
“It depends a little bit on the state of the current customer CRM,” Cheney said. “The data quality might be low because they'll just have, for example, maybe their pipeline data, but they won't have a complete view of the customer.”
That can restrict what AI can reliably do. An autonomous agent asked to identify an upsell opportunity, summarize an account or recommend the next action will struggle if the customer record is incomplete. As Cheney pointed out:
“CRM with poor quality data can be a real challenge and can limit some of the capabilities of AI.”
When businesses embark on an AI strategy, improving the data foundation becomes part of the implementation rather than a separate housekeeping exercise.
“That would often be our first recommendation,” Cheney said, “that you take a step back, work out what do we need to do to improve the quality of data inside the CRM and AI is definitely a way of doing that.”
AI Can Help Fix the Data Problem
The same technology creating demand for better data can also help organizations prepare it, Cheney said.
“The good news is AI is also incredibly good at helping us clean data.”
AI can help organizations to identify gaps, interpret information and enrich customer records rather than relying entirely on employees to perform repetitive data-management tasks.
“One of the things that we've been helping our customers with over the last 12 to 18 months is how do we use AI as a tool itself to improve the quality of data that's in the platform?”
