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sales enablement

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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AI Prospecting Agents: Build More Pipeline Without Adding More SDRs

One of the more frustrating realities in modern sales is that a company can invest heavily in technology and still leave its best salespeople doing work that should never require their time.

The CRM is open. So is LinkedIn. There is an intent-data platform in another tab, a contact database in another, a sales engagement platform somewhere else, and perhaps a conversation intelligence tool running in the background.

The company has a technology stack. What it may not have is a prospecting system.

That distinction matters.

A collection of tools still depends on someone remembering which accounts matter, noticing when a former champion changes jobs, deciding which buying signals deserve attention, researching the account, moving information between systems, writing the message, and following up. When people are the integration layer connecting all those systems, the company has not really automated prospecting. It has simply given its salespeople more software to operate.

The Prospecting Advantage Has Changed

Traditional outbound sales was built around scarcity. Finding contact information, researching a company, and uncovering a credible reason to approach someone required significant effort. That made volume difficult and valuable.

Artificial intelligence changed those economics.

Today, generating another email is nearly free. Finding another prospect is relatively easy. AI can produce hundreds or thousands of messages faster than a sales team could ever review them. That does not make those messages valuable.

When volume becomes inexpensive, volume stops being a competitive advantage. Relevance becomes the scarce resource.

We need to evolve our systems away from asking, “How can we send more outreach?” Rather, we need to be thinking of, “How can we identify the people who deserve our attention right now and give our salespeople something useful to say when they contact them?”

A modern prospecting system therefore has to answer five questions continuously:

  1. Who should we pursue?
  2. Why should we pursue them now?
  3. What should we say?
  4. Which channel and timing gives us the best chance of engagement?
  5. When should a human salesperson enter the conversation?

The fifth question is particularly important.

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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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AI Will Not Fix Sales Problems Built on Fragmented CRM Data

Most sales leaders are asking the wrong question about artificial intelligence.

They ask which AI tool to buy, which platform has the best features, which automation will save the most time, or which sales technology will help their reps move faster. Those questions matter, but they are downstream from the real issue.

The more important question is: Does your CRM provide AI with enough trusted context to make useful recommendations?

If the answer is no, the next tool will not solve the problem. It will accelerate the confusion.

AI cannot reason well from fractured data. If account history lives in email, proposal tools, LinkedIn messages, spreadsheets, call notes, support tickets, and half-completed CRM fields, the AI is not operating from a complete commercial picture. It is guessing from fragments. A faster guess is still a guess.

That is why the CRM must evolve from a passive system of record into an active system of action. The old CRM was built to store yesterday’s activity. The modern CRM has to help shape tomorrow’s decisions.

A strong CRM foundation gives sellers a complete account context before a call. It helps managers understand pipeline risk without relying only on rep opinion. It allows AI to recommend next steps because the recommendation is grounded in actual customer history, not generic sales theory. It gives the organization leverage because the patterns learned in one deal can improve the next similar deal.

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Compelling Events: Shorten Sales Cycles & Improve Forecasts

Deals move when the buyer’s business calendar forces a decision.

A real compelling event is the operating discipline that separates pipeline from possibility. It gives urgency a business reason, attaches dates to consequences, and forces both sides to decide whether the opportunity deserves serious time, resources, and executive attention.

Many salespeople confuse need with urgency. That mistake creates bloated forecasts, stalled proposals, and too many “just checking in” follow-ups. A prospect can have a real need and still have no reason to act now. They may need

  • better integrations,
  • stronger reporting,
  • reduced churn,
  • tighter compliance,
  • faster workflows,
  • a cleaner technology stack.

Those needs matter, but they can live on a roadmap indefinitely.

A compelling event changes the conversation because something meaningful happens by a specific date.

  • An audit is scheduled.
  • A contract expires.
  • A board commitment has been made.
  • A market launch is tied to revenue.
  • A facility lease ends.
  • A regulatory requirement becomes enforceable.
  • A major customer is at risk.

These events create pressure because delays have consequences beyond the buying team’s preferences.

That is the standard. A compelling event has a date, an owner, and a consequence.

The Difference Between Interest and Commitment

Interest sounds productive in a sales conversation. Commitment behaves differently.

Interested buyers will schedule meetings, request demos, review capabilities, and discuss future-state improvements. Committed buyers will help you understand the decision path, expose internal constraints, validate timing, and clarify what happens if the outcome is missed.

The difference matters because your forecast depends on the customer’s decision reality, not your sales activity.

A compelling event gives you that reality. It tells you why the buyer is engaged now, who owns the risk, what business outcome must be protected, and which internal processes must be navigated to get there. Without that clarity, the opportunity may still be real, but it should be treated as unproven.

Sales leaders should inspect this with discipline. “They are excited” is not a compelling event. “Budget season” is not enough. “They want to modernize” is too soft. The better question is: what changed in their business that makes inaction costly?

That question protects your time and the buyer’s time.

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From CRM Debt to a Cognitive Revenue Engine: Reclaiming Selling Time with AI

Most B2B sales teams don’t have a talent problem. They have a capacity problem.

Administrative drag is quietly stripping selling time: CRM updates, stakeholder mapping, duplicate cleanup, meeting summaries, and the constant “what should I say next?” work that should not be consuming a senior seller’s day. The downstream damage is bigger than annoyance. Forecast accuracy declines, coaching becomes reactive, and revenue management turns into a negotiation with incomplete data.

Artificial intelligence can fix this, but only if you use it with the right operating model.

Benjamin Todd’s article “How not to lose your job to AI” makes the point that AI doesn’t simply eliminate jobs; it shifts where value concentrates. As routine tasks become cheap, the remaining human bottlenecks become more valuable. Todd’s ATM example is the cleanest version of the idea: ATMs reduced the need for “money counting,” but the overall demand for human banking roles didn’t collapse. The job shifted toward customer-facing work and higher-leverage conversations.

In B2B sales, our “money counting” is CRM entry, list building, and manual research. Our high-leverage work is business acumen, strategic influence, stakeholder alignment, and value selling. The problem is that most teams have it backwards: humans do the hardest input work (research, logging, hygiene), then AI writes the customer-facing messages. That combination produces drained sellers and generic messaging.

A better model is: Automate the input, humanize the output.

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Instant Follow-Up in Field Sales: How AI Eliminates Post-Meeting Lag

Field sales doesn’t lose deals in the meeting. It loses deals after the meeting when a buyer asks a high-stakes question, you promise to “get back to them,” and the response shows up after the moment has passed. That delay kills momentum and quietly downgrades you from advisor to administrator.

In 2026, the buyer often has access to comparable information. Your differentiation is contextual insight delivered with speed. If your follow-up arrives hours later (or worse, it arrives days later), you’re not doing value selling, you’re doing cleanup. That’s the Administrative Tax: notes, recap emails, CRM updates, and retrieval work that should not be done manually by your highest-paid revenue generator.

Artificial intelligence changes the operating model. The goal isn’t “better summaries.” It’s an Instant Field Response: capture what matters in the room, retrieve the right internal assets, and draft a precise follow-up while you’re still in the parking lot. When AI handles the science (capture, entity recognition, semantic search, and drafting), you reclaim the art: listening, reading intent, and leading the decision.

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The Producer Mindset: Tech-Led, Human-Centric Selling for Faster Pipeline Velocity

Administrative drag is not an inconvenience. It’s a structural failure in modern B2B sales that quietly taxes performance, slows pipeline velocity, and degrades your ability to show up sharp for buyers.

The pattern is predictable. You earn a hard-won meeting with an executive. You know you need a tailored deck that speaks to their priorities. Then reality hits: marketing is backlogged, design is unavailable, and you’re left formatting slides at night like a part-time desktop publisher. That’s the sales tax: time and energy spent on non-selling work that steals capacity from revenue generation.

This is the Tollbooth Effect in action. You build momentum in discovery, then you hit the system’s plaza: CRM updates, meeting notes cleanup, searching old folders for case studies, and wrestling with presentation software. The deal cools while you “pay.” Your edge dulls, not because you can’t sell, but because the operating model forces you into manual labor at the worst possible moment.

The fix isn’t working harder. It’s changing the role you play in the workflow.

In the Producer Mindset, your highest value isn’t typing, formatting, or slide layout. Your highest values are judgment, strategy, and human connection, and those can’t be automated. Technology should lead on mechanics while you stay accountable for truth, tone, and impact. This is a tech-led, human-centric approach: AI accelerates the work, but you control the meaning.

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