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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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Your Sales Compensation Plan Is Quietly Managing Your Sales Team

A salesperson reaches the final week of the quarter with a major opportunity ready to close. Legal has approved the agreement. The customer is prepared to move forward but asks for a 15 percent discount.

The salesperson understands what the discount does to margin. So does the sales manager. Finance knows as well. Yet the salesperson is measured on revenue, the manager needs the deal to make the quarterly forecast, and neither is directly rewarded for protecting margin.

The discount is approved. The deal closes. The commission increases. Everyone celebrates.

The company may have just paid someone extra to give away its profit.

No one acted dishonestly. The compensation plan worked precisely as designed. That is the problem.

Compensation Is a Management System

Most companies treat sales compensation as an administrative process. Leadership creates a plan, finance calculates payments, and managers resolve disputes when the numbers do not match expectations.

That view misses the strategic role compensation plays.

A compensation plan influences which customers salespeople pursue, how aggressively they discount, whether they favor one-year or multi-year agreements, how they collaborate, and whether they prioritize new business, renewals, margin, or market share.

What the company pays for will eventually outweigh what its leaders say they value.

Many sales leaders inherited plans that accumulated years of exceptions, temporary accelerators, regional variations, product overlays, split-credit rules, and special arrangements. The resulting spreadsheet may be technically functional while being strategically incoherent.

The warning signs are familiar:

Salespeople maintain private spreadsheets because they do not trust their commission statements.

Managers spend time resolving payout disputes instead of coaching pipeline.

Quotas are created by adding a percentage to last year’s number rather than analyzing territory potential.

A handful of top performers benefit from every contest while most of the team decides the competition is irrelevant.

These are not motivation problems. They are design problems that consume selling time, reduce margin, and erode trust.

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The Buyer’s Clock Starts Before Your Sales Team Notices

A buyer does not become urgent when your CRM creates a record. The buyer became urgent earlier; at that moment, they decided the problem was worth interrupting their day for.

That distinction matters because too many B2B companies design their inbound process around internal workflow rather than buyer momentum. A prospect searches, compares, reads, evaluates, talks to a peer, visits your site, reviews your proof, and finally raises their hand. Then the company they contacted responds as though the buyer has agreed to wait patiently while marketing automation, CRM routing, territory logic, rep availability, and inbox notifications sort themselves out.

That is not a speed problem. It is a revenue system problem.

The real issue is not whether a sales rep should call faster. Of course they should. The deeper question is whether the business can detect, interpret, prioritize, enrich, route, and respond to buyer intent while the buyer still cares. When the answer is no, the company loses revenue without realizing it. The lost buyer does not announce their departure. They simply book with someone else, cool off, or decide the issue can wait.

This is why inbound orchestration deserves executive attention. High-intent inbound activity is not a generic lead flow. A demo request from a target-fit company is not the same as a newsletter signup. A pricing inquiry is not the same as a content download. Treating all of them as “leads” may simplify reporting, but it destroys commercial judgment.

When every lead looks the same, nothing feels urgent.

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Reclaim Selling Time: How AI Eliminates the Sales Tax and Restores Pipeline Momentum

Most sales leaders are trying to solve a 2026 productivity problem with 2010 management logic. They hire more people, increase activity targets, and apply pressure to the same system. The system doesn’t respond because the constraint isn’t an effort. It’s architecture.

The operational reality is brutal: administrative work is consuming the day and choking selling time. Reps are stuck doing low-level research, logging notes, and stitching together follow-ups across disconnected tools. That “sales tax” creates a momentum gap between good conversations and slow execution. The outcome is predictable: fewer high-quality touches, slower deal movement, less accurate forecasting, and a pipeline that looks busy yet remains fragile.

The fix is not another round of tactical efficiency. It’s a structural reversal: move from a human-led, tech-assisted model to a tech-led, human-centric model. In that design, AI does the machine work—data extraction, workflow orchestration, logging, drafting, hygiene—and the human seller does the work that actually wins deals: judgment, stakeholder navigation, risk reduction, and credibility in the moments that matter.

Think of it as building a Cognitive Revenue Engine. Your reps stop being the engine. They become the orchestrators of an automated engine that produces consistent execution at scale.

This shift has two pillars.

Tactical Efficiency is your time reclaimer. Automate the tollbooth moments: post-call notes, CRM updates, basic research, and first-draft follow-ups. This is not about saving a few minutes. It’s about reclaiming hundreds of hours per rep per year and converting them into customer-facing time.

Strategic Intelligence is where the advantage compounds. AI should be used as a decision partner, not a faster typewriter. The questions change from “Can you write this email?” to “Given this account’s context and our past wins, what risk is most likely to stall this deal, and what’s the next best action?” That is the difference between activity and impact, and it’s the difference between noise and revenue generation.

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Why AI in B2B Sales Fails at the Last Mile and How to Fix It

Most conversations about AI in B2B sales focus on speed. Fewer focus on control. That is the blind spot.

AI can produce drafts, summaries, research, and follow-up frameworks in seconds. That part is real. But the final 20%, the last mile, is where revenue quality is either protected or destroyed. That final layer requires human judgment: context, timing, risk assessment, and the decision of what should happen next.

The central operating issue in sales today is not effort. It is an allocation. Too many high-value salespeople are spending prime hours on low-value administrative work. CRM cleanup. Internal updates. Document hunting. Manual transcription. Reformatting information that should already be structured. That is a sales management design flaw, not a rep discipline issue.

When sales organizations fix this, performance changes fast. More customer-facing time creates more trust-building interactions. More trust creates better access, stronger positioning, and better conversion outcomes. This is not theoretical. It is how revenue generation compounds in real markets.

The right model is not “AI only.” It is a hybrid model: deterministic automation for correctness, AI for speed and language quality, human oversight for business judgment.

Deterministic systems should control anything that must be exact: pricing, contract elements, offer logic, approval rules, and data integrity. AI should then layer natural language, personalization, and messaging refinement on top of verified inputs. This is how you scale value selling without introducing preventable errors.

If your team is still using AI as a standalone drafting tool, you are under-leveraging it. If your team is sending AI output without last-mile review, you are overexposing the business. The goal is not automation theater. The goal is repeatable, high-confidence sales processes that increase throughput without compromising trust.

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Zombie Deals in B2B Sales: How AI Improves Forecast Accuracy and Coaching

Zombie deals aren’t a pipeline nuisance. They’re a leadership problem with a math problem attached.

A deal that sits in “Proposal” for months doesn’t just cloud your forecast. It steals capacity. Every hour a rep spends nurturing a flatlined opportunity is an hour not spent creating new demand, advancing real deals, or improving customer trust. Multiply that across a team, and you get the same symptom every quarter: missed numbers, reactive hiring decisions, and management time wasted on interrogations that create more friction than clarity.

The common response is predictable: more pipeline discipline. More required fields. More approvals. Longer forecast calls. More “updates.” That feels like control, but it’s usually just activity theater. It increases administrative drag and reduces selling time, exactly the opposite of what revenue management needs.

The fix is a mindset shift: move from intuition to evidence.

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The Dual Blueprint Requirement: Why Growth Demands Two Plans, Not One

Launching a company or steering one through a merger, turnaround, or major transition requires clarity about how value will be created and, just as importantly, how revenue will actually be generated.

Many leadership teams recognize the need for a Business Plan, but overlook that sustainable growth requires a second, complementary plan. The main breakdown is not the strategy itself, but the assumption that strategy automatically creates revenue. Bridging strategy and revenue requires a distinct plan for that conversion, targeting a different audience.

The Business Plan sets direction from the top down. The Sales Plan is validated by demonstrating how that direction can become actual revenue from the bottom up.

Both are essential. Neither works in isolation.

The Business Plan: Charting the Course (Top-Down)

The Business Plan exists to answer specific questions for a particular audience. Its primary readers are CEOs, CFOs, bankers, private equity partners, and venture investors. These stakeholders are evaluating risk, scale, and return. They want to know where the company is going and why the destination is worth the journey.

At its core, the Business Plan articulates strategic intent. It defines the mission, the long-term objectives, and the differentiated value proposition that the company believes the market will reward. It frames the opportunity in language that aligns leadership, capital, and governance.

Market analysis in this context is necessarily high-level. It focuses on the total addressable market, industry dynamics, competitive positioning, and macro trends. The goal is not to explain how every deal will be won, but to establish that a meaningful opportunity exists and that the company has a credible right to pursue it.

Financial projections follow the same logic. They are built on broad assumptions: projected market share, average selling price, renewal and retention rates, inflation, and multi-year revenue targets. These numbers are directional. They signal ambition and scale rather than operational certainty.

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