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InterviewCRM & Data8m · 09:01 GMT · 5 min read
The CRM Buying Decision Starts Before the Vendor Shortlist
As AI agents take on more work across customer-facing systems, CRM investment decisions need to account for data readiness and the processes businesses want to automate. Here’s why CX leaders should get the right people involved before committing their next platform.
Your customer relationship management (CRM) strategy for 2027 might depend on people outside your CX team. As AI agents begin retrieving customer information, updating records and taking action across business systems, the decision about which CRM platform to buy becomes part of a much bigger question around whether the organization has the data, processes and people needed to make those agents useful.
If you’re a CX leader planning your next technology investments, the buying process should start before the vendor shortlist, establishing what you want AI to achieve, where customer information resides and which teams must work together to make the intended outcomes possible.
Britt Mills, VP of CX Solutions at consulting firmBridgeNext, sees data readiness becoming a central consideration as organizations look to introduce AI into customer experience. Mills told CX Today:
“The possibilities are endless, and there’s a lot of excitement around how AI can create a personalized experience or really help to be proactive with customer interactions. But the focus now is on that foundational piece and building that correctly.”
CRM Investment Is Becoming a Cross-Business Decision
CRM purchasing has traditionally involved requirements from sales, service and other customer-facing teams. AI introduces a wider set of dependencies, particularly when organizations want agents to work across multiple systems rather than assist employees within a single application.
Customer data may sit across departments, platforms and operational processes that individual CX teams do not control. Before introducing autonomous workflows, businesses need to understand how those sources fit together and whether the information is reliable enough to support automated decisions.
Mills recommends making data a bigger part of technology assessments than it may have been previously.
“Get the right people in the room to discuss what’s possible from a data standpoint and have that a bigger part of the conversation and these build-vs-buy decisions than probably it was a year or two ago.”
IBM’s State of Salesforce 2025–2026 research found that poor data availability and quality were the main barrier to the adoption of agentic AI, cited by 53 percent of Salesforce customers surveyed. Only 26 percent of executives said that their customer data primarily resides in Salesforce, illustrating why CRM buyers need to assess information held across other enterprise systems.
When CX leaders involve data, IT and relevant business stakeholders before finalizing requirements for a new CRM platform or AI capability, the discussion should establish who owns critical customer information, how its quality will be maintained and which systems an agent will need to access. It should also clarify who is responsible when information is incomplete or an automated process cannot confidently determine the right action.
Without that groundwork, a technology purchase risks creating new dependencies without resolving the underlying problems that could limit its value.
Decide What AI Should Do Before Deciding What to Buy
The growing availability of AI agents can make new platform capabilities attractive, but buyers need to distinguish between tasks where AI can assist an employee and workflows where it will act autonomously.
Mills draws a clear distinction between those scenarios when assessing data readiness.
“If there’s a human intervention point still in the process,” organizations may be able to begin using AI before their data is fully prepared.
However, Mills takes a firmer position on autonomous processes: “If you’re thinking about leveraging AI for autonomous workflows, I’d say do not start until the data is in a good place.”
That distinction gives buyers a practical starting point. Rather than treating AI readiness as a single organization-wide status, they can assess the data requirements and risks of individual use cases.
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