Top Gun and the Lost Art of Close-Quarters Selling

Technology has increasingly allowed sellers to operate at ever greater distance and scale. In doing so, we neglected the skills required to sell at close-quarters, person to person.

Until a procurement AI buys from a sales AI, and with digital channels saturated, those skills are becoming valuable again.

We’ve Seen This Movie Before

Maverick, Goose, Iceman and Slider. Call signs of the leading characters in Tony Scott’s 1986 hit movie – Top Gun. Top Gun is the nickname for the United States Navy Fighter Weapons School (NFWS), established at Naval Air Station Miramar in 1969. It was established to improve air-to-air combat skills after disappointing performance in Vietnam. The Navy also described a need to shift from heavy reliance on radar toward more visually oriented tactics. In other words – more “close quarter” engagement.

Get where we’re going here? Previous aerial theaters proved the value of the close-quarters skill. In WWII there was no other way. New technology then persuaded people that those skills were becoming obsolete and unnecessary. Hence, those skills atrophied as more “stand-off” (or long distance) systems were deployed. Vietnam demonstrated that the technology hadn't eliminated the need for them after all. Topgun was created to restore them.

Something similar has happened in sales. We built technology for engaging buyers at scale. But somewhere along the way, we forgot how to engage them at close quarters.

The Industrialization of Prospecting

Maybe there wasn’t much difference between this and previous industrialization waves. As new business sellers (back in the day) we had some marketing support (generally events, sponsorships, advertising, sales collateral), but most of the activities of business development we did ourselves – cold calling, direct mail, and we even used to just walk into offices and find out who was who and what was what! 

Technologies emerged and pretty soon we’d got CRM (previously SFA), email, marketing automation, sales engagement, intent data, sequences, LinkedIn, RevOps and now AI. Every generation promised some combination of cheaper + faster + more + measurable. And broadly delivered it. So naturally sellers increasingly became operators of the prospecting machine.

We optimized the process around getting a meeting rather than getting good at the human interaction that happens before, during and after one. We want modern sellers (and marketers for that matter) to be able to “operate the tech stack.”

Then Four CIOs Said Something Interesting

I was at a tech event the other day and there was a panel featuring four CIOs from major sports brands or sports-related businesses. Someone asked, “How should people with interesting solutions get in front of you?”

All four agreed – “Come to events like this and talk to us.”

Their inboxes are clogged. LinkedIn is clogged. Automated outreach is ubiquitous. They don’t take calls from people or numbers they don’t recognize. AI will (probably) make the volume problem worse. It lowers the cost and effort involved in creating apparently personalized outreach.

The easier it became to contact someone, the harder it became to get their attention.

The Return of Close-Quarters Selling

Close-quarters selling isn't pitching someone beside the coffee machine.

It's being able to:

• approach someone you don't know;

• establish rapport without being clumsy;

• become interesting - present an insight or demonstrate some knowledge of their world

• ask questions and actually listen;

• read the other person;

• recognize whether there's something worth pursuing;

• leave with a natural reason to continue the conversation (or not)

These sound pretty basic. But we’re not developing these skills in modern sellers. We used to. I remember being sent on two-day body language courses and reading books by Edward de Bono!

Experience and “Know How” Matter

People who’ve spent time selling face-to-face have accumulated thousands of small interactions. They learned how to read a room. When to approach. When to shut up. When someone is interested versus merely polite. How to move from social conversation to business without making the transition awkward, and they did it more through trial and error.

Those “activities” aren't easily captured in a playbook. They’re hard to measure and therefore don’t fit the modern tech stack quarterbacking requirement.

Technology didn't make those skills obsolete. It simply created an environment in which we could apparently succeed without using them. As history continues its cycles of repetition, what was old becomes new again. Experience that looked old-fashioned suddenly becomes relevant.

Sales Needs Its Own Top Gun

I’m not saying you throw away the tech stack. What I am saying is you must find a balance, ( (and by the way, there is a role for technology in “close quarter selling.) Top Gun didn't teach pilots to reject technology. It restored capabilities that technology had caused the system to undervalue. Indeed, subsequent Navy air-combat performance improved dramatically.

Keep CRM. Keep intent data. Keep automation. Keep (and learn how to best use) AI. But we need to start deliberately developing close-quarters selling skills alongside them.

The next generation of great sellers won't be the ones who reject technology. They'll be the ones who can find the balance between being able to operate just as effectively when there's no screen between them and the buyer.

What D-Day Can Teach Us About Judgement in the Age of AI

Have you seen the movie Pressure with Brendan Fraser? If you haven’t, you should. It’s set in the tense days before the D-Day landings in 1944, when a decision about the weather could determine the fate of the invasion.

At its center are two meteorologists studying the same charts, the same forecasts and broadly the same evidence, yet reaching different conclusions about whether Eisenhower should proceed. One sees historical patterns and the outcomes they have usually produced. The other argues that there are simply too many interacting variables to assume that a historically similar pattern will produce the same result this time.

That distinction matters. The second meteorologist isn’t rejecting experience. He is using experience to understand the limits of precedent. He knows the history, but he also recognizes that the particular combination of circumstances in front of him may be different enough to demand a different judgement.

Their disagreement isn’t really about the data. It’s about its interpretation. And the most consequential part of Eisenhower’s decision was not simply that the invasion went ahead on June 6th; it was that he decided not to go on June 5th.

This isn’t really a story about weather. It’s a story about experience, judgement, confidence and leadership. It’s also a useful way of thinking about how AI is changing where humans create value.

AI May Make Experience More Valuable, Not Less

For most of my career, experience has been one of the most valuable assets people possess. We celebrate leaders with twenty years in an industry, consultants who have ‘seen it all’ and executives who have navigated similar challenges before. Boards recruit experience, recruiters sell it and resumes are built around it.

And for good reason. Experience is not simply an accumulation of facts. It is accumulated context. It is the memory of strategies that looked sensible but failed, decisions that worked for reasons nobody initially understood, personalities that changed outcomes, and small differences in timing or circumstance that turned out to matter enormously.

Until recently, however, experienced people also had to spend a huge amount of their time doing the work required before that experience could be applied. Understanding a market meant commissioning research. Competitive intelligence took ages to assemble. Customer evidence, financial data and internal information all had to be gathered and organized before anyone could begin to interpret what was happening.

Experience helped us bridge the gaps. We generalized. We looked for patterns. We borrowed from what had worked before. We leaned on best practice, case studies and familiar frameworks because information was incomplete and time was limited.

But that does not mean experience was merely a shortcut for missing information. Quite the opposite. The more information we can obtain, the more valuable genuine experience can become — because experienced people are better equipped to recognise which facts matter, which similarities are superficial and which differences could change the outcome.

The danger is not experience. It is treating experience as precedent: ‘I’ve seen this before, therefore I know what will happen.’ The better use of experience is as context: ‘I’ve seen things like this before, which helps me understand why this situation may be different.’

AI Has Changed the Economics of Thinking

This is where AI changes the equation. We have become accustomed to saying that AI saves time. That’s true, but it’s an understatement. What AI has really done is collapse the cost of acquiring, organizing and analysing information.

Tasks that once took days now take hours. Research that once required a team of analysts can often be assembled in an afternoon. Market summaries, competitor profiles, customer insights, financial analysis and strategic options can all be produced in a fraction of the time they once required.

AI can search, summarize, and compare. It can identify patterns across vast quantities of information. Increasingly, it can offer plausible explanations and directional recommendations. None of that is infallible, and it still requires validation and skepticism, but it can remove an extraordinary amount of the heavy lifting that used to consume human intellectual effort.

The most interesting consequence of AI is not that it diminishes the value of experienced people. It may do the opposite. If an experienced executive no longer has to spend most of the available time gathering and organising the evidence, that time and intellectual bandwidth can be redirected towards something more valuable: understanding what the evidence means in this particular situation.

AI can help tell us what has happened before, what usually happens and what the available evidence suggests. Experience helps us understand the significance of those patterns. Judgement decides how much of them applies here.

AI does not replace experience. It gives experienced people more opportunity to use it.

Context Is Everything

That brings us back to Pressure. The crucial disagreement is not between experience and inexperience. It is between two different uses of experience.

One view says that because a particular pattern has historically produced a particular outcome, it is reasonable to expect the same outcome again. The other says that historical patterns are useful only if we understand the variables that produced them — and whether those variables are sufficiently similar now.

Business is no different. Two companies can operate in the same market, sell similar products and face apparently similar problems, yet have different customers, cultures, leadership teams, competitive pressures, financial constraints, timing and appetites for risk. They can look remarkably similar from a distance and still be different enough for the same strategy to produce a completely different result.

That is why ‘best practice’ can be dangerous when it becomes detached from context. Six Sigma may have been highly effective at GE, yet the same discipline was widely criticised when applied at 3M because practices designed to drive consistency and efficiency could also constrain the experimentation on which 3M’s innovation culture depended. The framework was not inherently right or wrong. The context changed what it meant.

AI is extraordinarily good at finding analogies and surfacing what usually works.

Experienced leaders can use those analogies far more intelligently because they bring years of context to the comparison. But neither the analogy nor the experience makes the decision for them. That is where judgement begins: deciding which similarities matter, which differences matter more, and what all of it means for us, here, and now.

Interpretation Is Becoming the Scarce Skill

If AI gives us more information, more quickly, and experience gives us the context to understand it, the next question is what we do with both. Isn’t that interpretation?

Interpretation isn't simply analysis. AI is already becoming remarkably good at analysis. It can compare options, identify patterns, even challenge assumptions and suggest plausible explanations.

Interpretation is deciding which of those things matter in our particular circumstances or given a particular context. An experienced leader has accumulated thousands of observations, decisions, successes, failures and surprises. Previously, much of their time was still consumed acquiring and processing the information needed to make the next decision. AI changes all that.

It gives experienced people more intellectual bandwidth to do something potentially much more valuable: bring what they've learned to bear on the specific combination of circumstances in front of them.

But experience alone still doesn't provide the answer. In fact, used badly, it becomes a trap. "I've seen this before" can quickly become "I know what happens next." The meteorologists in Pressure illustrate the distinction perfectly. Both are experienced. Both understand the historical patterns. The difference is in how they interpret their relevance to the conditions confronting them now.

Perhaps that's where human value is moving. Not away from experience, but towards the ability to apply experience while maintaining one’s objectivity? AI can expand what we know, experience can deepen our understanding, and judgement is still required to decide what it means — here, now, and for us.

The New Skill Is Knowing What to Ignore

Judgement in an information rich environment has become about three things:

Relevance — Which information actually matters to the decision we're making?

Significance — Which differences in our circumstances are important enough to change the likely outcome?

Weight — How much importance should we attach to each piece of evidence?

Experience helps with all three.

Someone who's actually lived through market entries, acquisitions, sales transformations, product launches or whatever else we're considering may be much better equipped to spot the seemingly minor variable that really matters.

Experience has a dark side. It can also cause someone to overweight familiar information because it confirms a pattern they already recognize, creating a bias.

So the skill isn't simply having experience. It's knowing when your experience is illuminating the current situation and when it's distorting it.

Perhaps the executive who asks the best questions, recognizes the most important differences and exercises the strongest judgement will outperform someone with twice the experience but who relies too heavily on historical patterns. Maybe second guessing ourselves will become a positive!

In an age of information scarcity, knowing more was an advantage. In an age of information abundance, knowing what is a greater one.

Conclusion: What Matters Now?

The lesson from Pressure isn't that one set of experiences failed. Both meteorologists were experienced. Both had access to the evidence. The difference was how they judged what was relevant, how much weight they gave to historical patterns, and how they interpreted the particular combination of circumstances in front of them.

AI is rapidly removing many of the constraints that once limited our access to information. It can research, compare, analyze and identify patterns at a scale and speed that was unimaginable a few years ago. That doesn't make experience less valuable. It makes it more valuable. But it does change how we need to use it. How so?

The advantage will belong to the person who can decide what information matters, recognize which differences are significant, and apply their experience without being trapped by it.

For decades leaders have used frameworks designed to help them organize information, recognize patterns and make better decisions, in theory.  But if AI can increasingly perform much of that work for us, where should we be spending our time instead?

AI isn't replacing human thinking. It's changing where human thinking creates value. And increasingly, that value lies in knowing what matters, why it matters, and what to do about it.

Things were Great...Right Up Until They Said Said "yes."

The hidden psychology behind why confident buying decisions unravel —and what great salespeople do differently.

I was at a party the other day, and one of my friend’s sons (who had just graduated from college) asked me for some advice for him in his sales career.  Other than telling him to get out of it and go into plumbing or some such, I said “don’t expect to not get a few “no’s” in any deal.” Everyone get’s obsessed with “yes”, but the reality is getting to “yes” means having to go through a few “no’s.”

But that's not the whole story is it? There's a fallacy to the “yes” response these days - maybe there always was!

Every experienced salesperson has lived it. Things have gone great, from the start. Meetings go brilliantly, the prospect is enthusiastic, they tell you they're excited. They even say things like: "This is exactly what we've been looking for." "I think we're going to move forward." "Leave this with me—I just need to run it past the team."

They may even tell you when they’re going to sign off on the contract or SOW. Then...? Nothing. Silence. You don’t hear anything back. The day of their “signing” comes and goes. You leave it a day or two, drop them a text or email that also gets no response. Nothing. Nada.

You second guess yourself, you fight off your boss’s insistence that you “check in” with them. A week later you hear: "We've decided to delay,” or "We've decided to wait until next year," or “we (or “the team”) just need to check a couple of things.”

Most sales methodologies explain this as poor stakeholder management. I don't think that's the real reason. The real reason is that enthusiasm isn’t the same as decision confidence.

Deep down all good sellers know that hearing nothing but “yes’s” is as bad as one of the junior members of the team exuding how the prospect “loves us…we’re definitely going to win this.” They’re both the kiss of death. But still most sellers don’t do anything about this nagging doubt, because sellers have been conditioned to do what when they hear “yes?” That’s right…”shut up.” Well maybe that used to be the way, but it isn’t anymore. On the way to the final “yes” you’re going to hear an assortment of “no’s” AND “yes’s.”  We need to pre-empt them.

This is how we help the prospect confront risk and build confidence. The quality of a decision is measured not by the enthusiasm in the meeting, but by its ability to withstand the meetings that follow. In other words, we need to equip the buyer to convince everyone else – there’s more “selling” to be done.

The Myth of the Final "Yes"

Most people think a buying decision is complete when someone says yes. It isn't. It has only reached its first test. Every important business decision has another meeting or more. The ones you're not invited to. The ones where the decision is challenged.

The Meeting You'll Never Attend

There’s a whole list of internal conversations, some more formal, some less formal. Finance asks about cost. Operations questions implementation. IT raises integration and resourcing concerns. A board member remembers a previous failed initiative. A less informed investor recently read something and suggests alternatives.  Nothing about your solution has changed. Only the confidence surrounding the decision is being assaulted by doubts (some real, some imagined, and some purely theater.)

Here's the Truth Most Executives Learn the Hard Way

Decision makers and executives can delegate for and seek advice  But what you can’t delegate is accountability. Everyone will have an opinion. Everyone will influence the discussion. But once the outcome is known, nobody says: "Finance made that decision." "The board made me do it." "Operations talked me out of it."

The responsibility always comes back to the person who owned the decision, whether it flies or falls. Declaring you were talked out of that one into this one won’t suffice.

The Stories that stay with you

We were on a two-week training course in Woodstock, Connecticut. Nice place, but a bit “middle of nowhere” to us young sellers keen to see Beantown and The Big Apple. We were in a simulated sales pursuit – the culmination of which was that we’d convinced the “buyer”, and he’d admitted to our solution’s superiority, but he was getting downward pressure to go with an existing vendor. Their solution was inferior and he lacked confidence that it would succeed, but what was he to do? So, we told him – “if you think ours is the best solution, then go with it. And the reason to do that (other than the obvious) is if you go with the alternative (which you think will fail) and it does fail, do you think all these other people will shoulder the burden of that failure? Of course not – it was your decision, and you’ll own it. The closest they’ll get to accepting any blame, will be admitting to making “a few suggestions.  Some alternatives to be considered.”   

The Dangerous Illusion

Many decision makers believe that changing direction because others disagree somehow reduces their personal accountability. It doesn't. If the revised decision fails......they still own it. Influence can be shared. Accountability cannot.

This Is Why Confidence Matters

Let’s understand something here. Confidence isn't about becoming stubborn. It's about reaching a conclusion that has been properly tested. Listening to advice. Welcoming and expecting challenge. Exploring alternatives. Then making the decision you genuinely believe is right. That is executive leadership. And as sellers we must help this leadership explore the trade-offs, compromises, alternatives and likely challenges they will face.

The days of avoiding those are over.

The Seller's Job Isn't to Win the Meeting

This is where Decision Selling differs from traditional sales methodologies. Your objective isn't simply to persuade the buyer. It's to prepare the buyer for the conversations that happen after you've left. Can they explain the business case? Can they defend the investment? Can they answer objections? Can they justify the timing? Can they explain why doing nothing is the greater risk?

If they can't...The deal is still vulnerable.

Every Decision Must Survive Its Next Conversation

Great sellers don't create excitement. They create durable confidence. Because the real test of a buying decision isn't whether someone says “yes.” It's whether that yes survives every conversation that follows, and in this era of “managing by consensus” there’s  a lot of these “conversations.”

Final Thought

I know this stuff makes sellers uncomfortable. Why? Because I’m telling you to actively encourage your prospect to consider the alternatives, consider doing nothing, and walk through the likely “opinions” and challenges they will face. We’ve been taught and conditioned over the years to “not” behave this way. This is why most sellers fail. The injustice of that is that most are doing exactly what they’ve been trained to do.

You’ve got to divorce the decision from the prospect and treat the decision as it’s own entity. The strongest buying decisions aren't the ones made without challenge. They're the ones that remain intact after challenge. That's what Decision Selling is about. Not helping buyers make decisions. Helping them make decisions that can withstand scrutiny. Decisions they can confidently defend.