Unless you’ve ignored all talk of AI, odds are you’ve heard of agentic AI, the current AI buzzword. Though it can be implemented in many different use cases, agentic AI on a fundamental level autonomously performs tasks, makes decisions and engages with users without human intervention. For example, some early use cases we’ve seen for agentic AI are customer service chatbots, job recruitment and virtual personal assistants.
We’re also seeing agentic AI crop up in the sales world, specifically in the form of customer-facing AI agents. Within the past year, both Salesforce and HubSpot announced the launch of Agentforce and Agent.AI, respectively. Despite the heavy investment from both brands, adoption has been slow, and the customer response has been less-than-positive.
Why?
Successful sales have always relied on the relationships built through human-driven interactions. Though AI agents, in theory, sound great, they only benefit the sellers, helping them reach more customers in less time. For customers, the human touch is lost.
AI Makes a Great Assistant
The arrival of agentic AI in sales is not all bad news. When applied strategically on the back end, it offers real, practical benefits for sales teams. It’s great for automating repetitive and highly structured tasks that don’t require a personalized human touch to be successful. For instance, it can help with CRM data governance and hygiene, improve pipeline management and customer record-keeping, automate reporting and assist with lead scoring and segmentation.
Automating these routine tasks and responsibilities can optimize sales operations and allow sales professionals to focus on what they do best: building relationships, engaging prospects, and closing deals.
The key is to treat agentic AI as a support tool, not a replacement.
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When AI Talks, Customers Tune Out
At its core, sales is rooted in human connection, trust and empathy. Real, personalized conversations drive conversions. True personalization goes beyond knowing someone’s name, title or hobbies. It’s about making people feel understood by working collaboratively to solve a problem or fulfill a need.
That’s where AI agents are falling short. While it may be efficient, it can’t match a human-driven conversation’s nuance or emotional intelligence. Calls with AI agents are often robotic, overly scripted, and lack the depth customers expect, causing many to hang up quickly. If they stay on, it’s often for the lark of seeing just how bad the AI agent experience can get.
The result? The operational efficiency gains come with a cost–downgraded customer experiences.
Worse still, the sales team takes the reputational hit. Their efforts to nurture a lead can be undone via an AI agent in seconds. Repeated negative interactions reflect poorly on the company, damaging brand reputation and hindering the sales team’s ability to convert and grow long-term relationships.
Mastering the Human/AI Sales Relationship
To strike a balance with agentic AI use, it must be treated as an augmentative tool for sales professionals, not a replacement. Agentic AI shines in the prep work. Where humans shine is in the pitch work. When each is implemented for its strengths, it establishes clear boundaries that enhance performance while protecting the customer experience.
To avoid further missteps and disruptions from agentic AI integration, companies should take a human-in-the-loop approach, even for operational applications. This means training sales teams to interpret AI insights and recommendations critically rather than accept them at face value. When it’s wrong, teams should know how to adjust both the immediate and long-term output mechanisms so the system continues to learn and improve.
There’s a Time and Place for Agentic AI
Despite early hiccups with agentic AI, it has a place in sales. Early use cases suggest it’s most effective behind the scenes—not in front of customers.
At the end of the day, good sales interactions still rely on human insights, intuition and judgment for success. The most successful sales teams will learn to balance AI efficiency with human-led strategy and execution.
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