John Golden, Chief Strategy and Marketing Officer at Coevera, argues that AI in B2B sales must support human relationships, not replace them. His warning comes as enterprise buying grows more complex, with more stakeholders and internal influence, putting pressure on sales teams to understand the people behind each deal. For managers overseeing customer experience and sales, the message is clear: AI tools should make sellers more prepared, not more detached.
The CRM trap: command and control
Golden points to a structural problem in many legacy CRM platforms. They were designed around "command and control," he said, which placed the burden of data entry on salespeople while offering limited practical support in return. The result is a system that asks for constant updates but rarely gives sellers useful insights to move deals forward.
"AI shouldn't own the relationship," Golden said. That principle becomes critical when businesses layer AI onto a CRM foundation that already frustrates the people who use it. Without fixing the underlying workflow, adding automation can multiply existing problems.
AI as a preparation engine
Golden sees a more useful role for AI: making sellers the most informed person in every conversation. "It should make sure that the human is the most prepared and the most knowledgeable person in the room," he said. That means AI should handle task-based work-research, note-taking, activity logging-so salespeople can focus on strategy and trust-building.
For managers, this shifts the conversation from "how much activity can we track?" to "how well are we preparing our teams?" As AI becomes central to sales enablement, managers are seeking ways to upskill their teams; resources like AI for Sales Courses can help sellers learn to use AI for account research and meeting preparation without losing the human touch.
Data quality determines AI's value
The promise of AI-driven insights collapses when the underlying data is weak. Golden warned: "If that data is not clean, if that data is not accurate, or if that data is thin," AI becomes "a more efficient way of giving you inaccurate information." For management teams, this means auditing CRM data quality is a prerequisite before any AI rollout.
Thin or incomplete records-common in organizations where sellers see data entry as a chore-can produce recommendations that look convincing but lead to poor decisions. Managers who treat AI as a fix for messy data risk accelerating mistakes rather than improving outcomes.
Where revenue really lives
Golden's most direct statement cuts through the noise: "The revenue really lives in the relationships." AI can map buying centers, identify influence across an account, and flag where relationships might make or break an opportunity. That kind of analysis helps sellers understand who matters, not just what stage a deal is in.
But he cautioned against pushing automation into outbound engagement. "People still want to interact with people," he said. For CX and sales leaders, that means AI-led outreach can frustrate buyers if it replaces human interaction rather than supporting it. The goal is to remove friction from the sales process, not remove the seller.
Why this matters for management
Managers who lead sales or customer experience teams face a clear choice. AI can be a tool that sharpens seller judgment and deepens customer relationships, or it can become a layer of automation that erodes trust. The difference often comes down to data readiness and how the technology is positioned internally. Before investing in AI, audit the accuracy and completeness of your CRM records. Then, deploy AI for the task-based work that pulls sellers away from conversations-research, scheduling, note capture-rather than as a shortcut to replace human judgment. For managers themselves, understanding how to integrate AI into sales strategy is critical, and AI for Managers Courses offer guidance on leading AI adoption without eroding customer trust.
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