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Generative AI

What An MBA Didn’t Teach You About Sales

The sales profession is challenging. You need to work hard at it to succeed. You need to learn from the best. You need to improve your skills continuously. If you think you can sell since you are a hit at parties and have a lot of friends, you may soon find that you are a failure as a salesperson. Blunt truth:

because the sales profession is so hard, you have to focus on doing everything in sales very well, or you will be considered a failure.

I call this blog, Skinned Knees because I try to relate all of the learning that I have done over the past 4+ decades (while skinning my knees in the learning process).

I hope that you learn from my mistakes so that your business will grow!

How to Win When AI-Powered B2B Buyers Already Know Your Product

Imagine walking into a major prospect meeting after two days of preparation. You have reviewed the account, refined your presentation, and rehearsed your opening. Three minutes into your presentation, the vice president of operations interrupts.

“We already know what your product does. We compared you with five competitors using AI.”

She turns her laptop around. On the screen is a spreadsheet with 200 rows of features, prices, reviews, and implementation claims. Some entries are accurate. Others are questionable. Every entry has already influenced how her team thinks about your solution. Your instinct might be to continue the presentation, challenge the inaccuracies, or defend your product feature by feature. All three responses surrender control of the business conversation. The buyer does not need another source of product information. She needs help making a decision.

The Salesperson’s Information Advantage Is Gone

Artificial intelligence has changed what B2B buyers can accomplish before they speak to a salesperson. Generative AI tools can summarize technical documents, compare competing products, model pricing scenarios, identify perceived weaknesses, and develop vendor shortlists in a fraction of the time those activities once required.

According to Gartner’s May 2026 research, 45% of surveyed B2B buyers used generative AI during a recent purchase. Sixty-seven percent said they preferred a sales-rep-free experience. Yet 69% wanted to validate AI-generated insights with a sales representative. Those numbers are not contradictory. Buyers increasingly prefer to gather information independently, then turn to someone who can help them assess its reliability, apply it to their business, and reduce the risk of a bad decision.

This changes the definition of value selling. A salesperson’s value is no longer primarily access to information. It is the ability to translate evidence into business judgment and confidence in decisions.

Your Most Dangerous Competitor Is No Decision

An AI-assisted buying committee may be more informed individually and less aligned collectively. The CFO asks an AI assistant to identify the cheapest alternative. Operations prioritizes reliability and throughput. IT investigates integration and cybersecurity. Procurement scores the vendors against another set of criteria. Each stakeholder arrives with an apparently objective answer. The answers do not necessarily agree. More information creates more debate, more validation requests, and more reasons to defer the decision.

This is how information paralysis becomes a revenue problem. The salesperson who responds with another 40-slide deck adds information without resolving uncertainty. The stronger seller identifies the few factors that determine whether the investment produces a measurable business result.

For a manufacturing automation purchase, those factors might be unplanned downtime, production throughput, and integration costs. The other 197 spreadsheet rows may be informative without being decisive.

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AI Sales Coaching: How to Stop Rescuing Deals and Start Developing Salespeople

There is a dangerous moment in the growth of many B2B companies. It happens when the best salesperson becomes the sales manager.

The promotion seems obvious. This person consistently exceeds quota, understands customers, knows how to close difficult deals, and has earned the team’s respect. Who could be better qualified?

Six months later, that same person is personally rescuing the team’s largest opportunities, handling difficult customer objections, negotiating with executives, and keeping the forecast alive.

The company’s revenue looks healthy. The owner is satisfied. The sales manager appears indispensable.

And that is precisely the problem.

The company hasn’t developed a scalable sales organization. It has become dependent on one talented individual. Artificial intelligence offers an opportunity to change this pattern. But only if we rethink what sales management should accomplish.

The Difference Between Managing Deals and Developing Salespeople

Traditional sales management relies heavily on inspection and intervention. Managers review pipeline reports, examine forecasts, enforce activity requirements, and intervene when opportunities get into trouble. These activities are necessary, but they are not the same as coaching.

Consider a salesperson who confidently announces during a pipeline review that Mary is the Champion for a major opportunity. The manager suspects that Mary is simply a friendly contact who likes the product. An experienced manager might join the next customer meeting, take control of the conversation, and personally secure executive support. The opportunity might close. Unfortunately, the salesperson hasn’t developed the ability to identify and validate a real Champion.

A better manager asks a different question:

“What has Mary actually done that proves she is your Champion?”

Has Mary introduced the salesperson to important stakeholders? Has she explained the customer’s internal decision process? Has she advocated for the solution when the salesperson wasn’t present? Has she taken any personal risk to help the opportunity advance? If the salesperson cannot identify meaningful actions, the relationship remains an untested hypothesis.

The manager’s next responsibility isn’t to take over. It is to help the salesperson determine how to test that relationship. That coaching conversation improves the salesperson’s judgment, potentially affecting every future opportunity.

The objective of sales leadership is not to make the manager better at closing deals. It is to make the entire team better at closing deals.

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AI Sales Automation: Why Your Chatbot Is Not a Sales Process

Imagine preparing for an important customer meeting. You open your favorite AI assistant, give it the prospect’s name, and ask for a summary of the company’s business challenges and the questions you should ask. Within seconds, you receive a beautifully written briefing. The assistant identifies potential operational problems, competitive pressures, and several opportunities to demonstrate the value of your solution.

There’s just one problem. One of those impressive insights concerns an acquisition the prospect supposedly completed last year.

Except the acquisition never happened.

The artificial intelligence fabricated a plausible business event and presented it as fact. Had you trusted the briefing, you might have opened your meeting with a question that immediately damaged your credibility.

This scenario illustrates a much larger problem with how B2B sales organizations use AI.

We’re asking artificial intelligence to perform too many different kinds of work without distinguishing which tasks require intelligence and which require reliable execution.

That distinction matters more than the sophistication of the AI model you choose.

The Problem Isn’t Just Hallucinations. It’s Process Design.

Salespeople increasingly rely on ChatGPT, Claude, Gemini, and other Generative AI applications to prepare for meetings, research prospects, write follow-up messages, and analyze opportunities. Those are legitimate applications of AI. In fact, I’d question why a salesperson with access to these capabilities wouldn’t use them. But consider what happens before the salesperson receives a useful answer.

They retrieve information from their CRM, gather previous meeting notes, identify stakeholders, check pricing, and reconstruct details from previous conversations. They paste everything into their AI assistant, ask a question, verify the response, and correct any mistakes.

Tomorrow, they repeat the process for another customer.

The AI may have improved the quality of their analysis, but the underlying sales process remains largely manual.

When twenty salespeople independently reconstruct the same process every day, you don’t have an AI adoption problem. You have a sales management problem.

You’re paying experienced professionals to repeatedly assemble information that your existing systems should already provide.

The solution isn’t necessarily better prompting. It’s better workflow design.

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Stop Asking AI to Write: How Salespeople Can Use AI to Think Better

Most salespeople are using artificial intelligence too late in the sales process. They gather the account information. They interpret what matters. They decide what argument to make. They formulate the questions. They determine what the buyer probably cares about. Then, after doing all of the difficult thinking themselves, they hand the last 10% of the job to AI and say, “Write me an email.”

When the result sounds generic, they conclude AI isn’t particularly useful for serious B2B selling. The problem is not necessarily the technology. The problem is where they inserted it into the process.

A salesperson can get significantly more leverage from AI by using it before the answer is obvious. That means treating the system less like a copywriter and more like a junior analyst, researcher, sales coach, and thinking partner.

The Skill Is Not Prompt Writing

An entire industry is built around prompt libraries. “50 Best Prompts for Salespeople” sounds useful because it promises a shortcut. It also teaches the wrong lesson.

A prompt that worked for someone else’s account, market, buyer, sales process, and objective may be almost useless for yours. The more strategic the sales problem becomes, the less likely a canned prompt is to fit it.

The more durable skill is learning to decompose commercial work into reasoning steps.

A useful sequence is:

  • Context.
  • Objective.
  • Constraints.
  • Reasoning steps.
  • Output.
  • Critique.
  • Revision.

Instead of asking AI to jump from context directly to output, make it work through the intermediate decisions with you.

Suppose you are preparing for a meeting with a CFO. A weak request is:

“Write an email to the CFO.”

A stronger conversation begins by asking AI to identify the financial issues most likely to matter to that executive based on the evidence you provide. Then ask which conclusions are facts, which are inferences, and what additional information would increase confidence. Challenge the assumptions. Compare several possible value arguments. Only then should you ask for the email.

The writing is now the consequence of better thinking.

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