Artificial intelligence is rapidly reshaping industries, and many fear that sales professionals are becoming obsolete. But, in The AI Edge, Jeb Blount and Anthony Iannarino argue the opposite: AI isn’t a replacement for human talent; it’s a powerful catalyst for those who know how to use it. Their core thesis is that, while AI excels at processing data and recognizing patterns, it lacks the essential human skills—trust-building, adaptive communication, and complex problem-solving—that actually close deals.
Understanding the balance between human intuition and machine efficiency is the new key to career longevity. Keep reading to learn why AI requires constant oversight and how you can strategically integrate AI tools into your sales workflow and gain a competitive advantage.
Table of Contents
Overview of The AI Edge (Jeb Blount & Anthony Iannarino)
Artificial intelligence is rapidly advancing, shifting industries and changing everything about the way we work. Some even claim that this technology will make sales professionals redundant—because humans simply can’t compete with such powerful, efficient tools.
But, in The AI Edge, Jeb Blount and Anthony Iannarino argue that AI tools can’t replace sales professionals for two reasons: First, AI tools can’t replicate the human abilities that drive sales. Second, these tools need constant human oversight.
This doesn’t mean your job’s safe, though—AI may not replace sales roles, but sales professionals who effectively use AI tools will replace those who don’t. According to the authors, success lies in knowing which sales tasks to delegate to AI tools and which must remain in human hands.
The authors draw their expertise from decades in the sales industry. Blount is CEO of Sales Gravy, a sales training corporation, and author of multiple best-selling sales books, including Fanatical Prospecting and Sales EQ. Iannarino, an alumnus of Harvard Business School, publishes The Sales Blog and has written sales books such as Elite Sales Strategies and The Only Sales Guide You’ll Ever Need. Together, the two have trained thousands of sales professionals worldwide.
This overview of The AI Edge (2024) covers the authors’ key ideas in two parts:
- Part 1 clarifies why AI tools can’t replace sales professionals, covering how AI training enables these tools to excel at certain types of tasks—and fail miserably at others.
- Part 2 outlines how you can benefit from AI tools through three key stages of the sales process.
Part 1: Why AI Tools Can’t Replace Sales Professionals
Blount and Iannarino argue that AI tools can’t replace sales professionals for two reasons: First, they’re incapable of replicating the human abilities that drive sales; second, they need constant human oversight. To clarify why these weaknesses exist, we’ll first explain how AI tools are trained and what this training enables them to do better than humans.
AI tools are trained to detect statistical patterns in massive amounts of text data—such as which words and phrases frequently appear together—and to generate outputs that match those patterns. The authors explain that this training enables AI tools to excel at three types of tasks:
- Processing enormous amounts of information quickly. For example, they can scan hundreds of sales reports simultaneously and compile findings in seconds.
- Recognizing patterns across vast datasets. For example, they can analyze thousands of email subject lines to determine which language generates the highest response rate from specific customer groups.
- Generating content by recombining existing patterns. For example, they can mimic the structure and style of existing marketing materials to create promotional copy for new products and services.
It’s clear to see how delegating tasks to AI tools can save you hours of manual effort. However, Blount and Iannarino point out that the same training that enables AI tools to excel at pattern recognition and replication prevents them from understanding things the way humans do. As a result, these tools lack the human skills that drive sales, and they can’t work reliably without human oversight. Let’s explore these two limitations.
Limitation #1: AI Tools Lack Core Sales Skills
Because AI tools can’t understand things the way humans do, they’ll never develop three skills that Blount and Iannarino say are key to making a sale: trust-building, adaptive communication, and complex problem-solving. Let’s look at how each skill contributes to sales success, as well as why AI tools can’t mimic them.
Sales Skill 1) Trust-Building
You build trust in various ways, such as showing potential clients that you understand their needs, being transparent about what you can deliver, following through on your commitments, and proving your credibility with evidence of how you’ve benefited others. Blount and Iannarino say that trust-building drives sales by persuading potential clients that you care about their best interests, will deliver on your promises, and will take responsibility if things go wrong. This makes them feel valued, lowers their perception of risk, and encourages them to consider your recommendations more seriously.
AI tools can’t build trust because they don’t understand why someone might want something or care about whether their response serves that person’s best interests. When asked a question, these tools generate a response based on which words typically follow that question. They don’t consider why someone would ask that question, can’t know if their responses are helpful, and feel no sense of responsibility for the consequences of providing false information.
For example, suppose a client asks whether your product integrates with their existing systems. You’ll instinctively understand why that might be a concern and might respond, “It depends on which specific systems you’re using—I’ll need more information before I can give you an accurate answer.” This response builds trust by demonstrating that you’re willing to put their interests ahead of making a quick sale. In contrast, an AI tool might respond, “Yes, our product integrates seamlessly with all major platforms.” It will have chosen this response after noticing how frequently this phrase appears in sales content, not because it knows it to be true. This response demonstrates a lack of concern that erodes trust.
Sales Skill 2) Adaptive Communication
You practice adaptive communication by listening actively—not just to what’s said, but to how it’s said. You notice tone, pace, expression, and body language that reveals unspoken meaning, then adapt your message and delivery to fit what the moment calls for. Blount and Iannarino explain that adaptive communication drives sales by keeping conversations relevant and responsive to what clients say. This helps clients feel genuinely heard and understood, which makes them more willing to share their priorities and stay engaged throughout the sales process.
AI tools can’t communicate adaptively because they don’t perceive the subtle verbal and nonverbal cues that shape human interaction. They process language statistically, matching words and phrases based on frequency rather than interpreting tone, facial expression, or body language. As a result, they can’t recognize intentions or emotional states—the very information needed to communicate responsively.
For example, suppose a client says, “That sounds interesting.” You’ll instinctively notice whether their tone and posture show genuine curiosity or uncertainty, or polite dismissal. Depending on what you observe, you might say, “I’m glad this caught your attention—let me share how others have used it successfully,” or, “It sounds like you’re unsure—what concerns do you have?” Adjusting your responses this way demonstrates awareness and flexibility that keeps the conversation relevant. In contrast, an AI tool might reply, “Great! Here are the next steps for implementation,” because that phrase frequently follows expressions of interest in sales data. This generic response ignores the client’s cues and risks ending the conversation altogether.
Sales Skill 3) Complex Problem-Solving
You demonstrate complex problem-solving by weighing multiple factors—such as goals, constraints, and competing priorities—and applying judgment and creativity to design tailored solutions that deliver the best outcomes. Blount and Iannarino explain that complex problem-solving drives sales by helping clients navigate uncertainty and overcome obstacles that make purchasing decisions difficult. This reassures them that you can guide them to a solution that will work for their specific situation.
AI tools can’t solve complex problems because they can only recombine patterns from data they encountered during training. When asked to solve problems, these tools search their training data for similar situations and suggest approaches that worked in those contexts. However, when facing unprecedented problems or situations that don’t closely match their training data, these tools can only suggest generic solutions or cobble together responses from loosely related examples. While such solutions seem plausible on the surface, they rarely account for the unique combination of factors at play.
For example, suppose a client wants to purchase your software but has a limited budget and specific integration requirements. After evaluating these constraints, you might propose a phased implementation that prioritizes the most critical features first, and you might schedule additional support to ensure a smooth rollout. This solution helps the client adopt the software successfully despite their limitations. In contrast, an AI tool might suggest a standard implementation plan it has seen frequently in its training data. This plan won’t work in practice because it doesn’t account for the client’s specific constraints.
Limitation #2: AI Tools Are Unreliable
Now that we’ve clarified why AI tools can’t drive sales, let’s discuss why they’ll always require human supervision. Blount and Iannarino explain that because AI tools don’t understand meaning, they often misinterpret requests and generate information that sounds accurate but isn’t. Let’s explore these two flaws in detail.
Flaw 1) AI Tools Misinterpret Requests
The authors explain that AI tools misinterpret requests because they interpret language literally. When given an instruction, they follow it exactly as written—they can’t infer meaning, fill in gaps, or consider intentions or priorities. As a result, they often produce incomplete or irrelevant outputs that require extra time and correction. For example, say an AI tool is asked to summarize which sales strategies generated the most revenue from a quarterly report, but the request is simply, “Summarize this report.” The AI might produce a broad overview of figures and highlights without identifying which strategies drove results—completely missing the insight that matters.
For this reason, to provide meaningful insights or perform a helpful sales service, AI tools always need clear, detailed instructions that explicitly state the purpose of the request. For example, a more effective instruction would be: “Summarize the Q2 sales report by identifying which strategies generated the most revenue and explaining why they were effective.”
Flaw 2) AI Tools Output False Information
Blount and Iannarino say that even with clear instructions, AI tools often produce false or misleading information. This is because they’re designed to generate convincing responses, not ensure accuracy. When they lack reliable data to fulfill a request, they don’t admit uncertainty. Instead, they assemble language patterns that sound factual, outputting statistics, case studies, or examples that look credible but are entirely fabricated.
For example, if asked to find a recent statistic showing how training software improves sales performance, an AI tool without access to real data might output, “Companies using training software saw a 27% increase in sales productivity,” and attribute the claim to a well-known research firm. The statistic sounds legitimate, but the study may not exist—the system may have invented it to provide a satisfying answer. Since relying on or sharing false information can undermine performance and credibility, AI outputs should never be used without verifying their accuracy against reliable sources.
Part 2: How to Apply AI Tools Throughout the Sales Process
Since AI tools have many limitations, you might be wondering if it’s worth integrating them into your work process. Blount and Iannarino stress that not doing so will put your job at risk—you’ll miss out on the efficiency benefits they offer and eventually be replaced by those who use them effectively. However, they warn that relying too heavily on AI tools also puts your job at risk, as it may undermine the very capabilities that differentiate you from these machines. So, to safeguard your job and thrive in sales, you must create a balance—delegate tasks that AI tools are trained to excel at, and keep control of everything else.
We’ll outline how you can achieve that balance and benefit from AI tools through three key stages of the sales process: targeting potential clients, approaching potential clients, and making a sale. Since both authors have built their careers training organizations to maximize sales, they emphasize how AI tools can support business-to-business (B2B) scenarios—where sales professionals sell to other companies rather than directly to consumers. Their advice, however, applies regardless of what you sell or to whom.
Stage #1: Targeting Potential Clients
To make a sale, you first need to target those who might be interested in what you have to offer. In B2B scenarios, this involves three steps:
- Finding relevant companies: Identify companies that might need your offer by reviewing industry reports, marketing materials, or company announcements. For example, if you sell logistics software for manufacturers, you might look for mid-sized manufacturing companies in the process of expanding production capacity.
- Identifying decision-makers: For each company, focus on the roles and departments most likely to use what you offer and determine who is responsible for making purchasing decisions. For example, you might seek out operations executives responsible for supply-chain performance.
- Evaluating opportunities: Analyze the information you’ve gathered and decide which companies and contacts are worth pursuing.
Blount and Iannarino suggest you can use AI tools to automate the first two steps and save hours of manual work. You can instruct them to search industry databases, scan company websites, and compile contact lists based on roles or departments to gather all the information you need. For example, you might use this prompt: “Identify mid-sized companies with growing production capacity and list operations executives responsible for supply-chain performance.”
However, the authors emphasize that evaluating opportunities must remain your responsibility. This involves two steps: First, you need to fact-check the data and interpret its relevance. Then, you need to decide which companies and contacts are worth pursuing. If you skip these steps, you risk chasing companies that look promising on paper but don’t need your offer, or contacting people who don’t influence purchasing decisions. For example, say an AI tool flags a company that’s announced plans to expand. You’ll need to dig deeper to determine whether this expansion affects the company’s software needs, whether your offer will be relevant to those needs, and whether the listed executives have real decision-making authority.
Stage #2: Approaching Potential Clients
Once you’ve identified which companies and decision-makers to target, the next step is to make contact and quickly demonstrate why you’re worth talking to. You achieve this through three tasks:
- Defining your approach: Review what you’ve learned about the company and the decision-maker and decide how you’ll make your message feel immediately relevant.
- Crafting your message: Write a short, specific message that connects your offer to the decision-maker’s likely priorities.
- Managing interactions: Keep track of who you’ve contacted, when, and what response you received so that each subsequent message feels timely and intentional.
Blount and Iannarino suggest you can use AI tools to streamline all these tasks. You can instruct them to summarize company information, highlight patterns in what similar decision-maker roles value, draft messages based on tone or formality, and help you track communications.
However, the authors emphasize that communications must stay under your control. You need to make sure your messages sound like you, interpret the responses you receive, and judge how much persistence is appropriate. Otherwise, you risk sounding generic, contacting people too often, or pursuing opportunities that aren’t real. For example, say an operations executive hasn’t responded to your initial message. Instead of asking an AI tool to draft a generic follow-up, check whether the company has posted updates or investigate what project the executive is currently working on. Use that context to adjust your timing and wording—for instance, acknowledge the update and restate how your offer helps address this development.
Stage #3: Making a Sale
After engaging clients in conversation, you then need to work toward closing a deal. This entails three tasks:
- Tailoring a proposal: Learn about the client’s specific needs, then adapt your offer to address those needs.
- Negotiating terms: Discuss pricing, timelines, and conditions with the client while addressing any concerns that arise.
- Finalizing a contract: Create, review, and sign a legal agreement to make the sale official.
Blount and Iannarino suggest you can use AI tools to support all three tasks. You can instruct them to organize conversation notes, draft proposal sections, suggest responses to common concerns, and review contract language for issues.
While you can use AI to assist with sales tasks, the authors emphasize that you must make all strategic decisions. You need to decide what to emphasize in proposals, how to respond during negotiations, and which contract terms to accept.
For example, say your client mentions a problem that disrupts their production schedule. You’ll need to structure your proposal to emphasize how the software solves this specific problem rather than listing all its features equally. During negotiations, if the client doubts whether the software can resolve their issue, you’ll need to respond with relevant case studies or customer endorsements that demonstrate proven results. Then, if the client requests a contractual clause guaranteeing the software will resolve their production issues within six months, you’ll need to verify that this timeline is realistic before committing.
