Positioning, Messaging, and Branding for B2B tech companies. Keep it simple. Keep it real.
Tacit expertise is your advantage. That’s because AI scales what’s documented. If your secret sauce lives in heads, not docs, AI will scale your competitor’s average instead of your edge. Document tacit knowledge to capture the judgment calls that make your frameworks work. Then use AI to scale what’s true.
I’ve been following Rick Beato for years.
For those unfamiliar, Rick hosts an excellent YouTube channel for music professionals, amateurs, and geeks like me.
Being a musician and a marketer, Rick’s insights ring loud and clear with me when it comes to AI adoption.
In the video below, Rick said:
“A lot of musicians talk to me and they’re really freaking out about these [AI] programs.”
A lot of GTM teams are freaking out too.
You don’t need to be a musician to understand what Rick’s getting at. His real-world examples are not mutually exclusive to the music industry.
You may not know Max Martin, but you probably know the dozens of number-one songs he’s written or produced for Taylor Swift, The Weeknd, Ariana Grande, the list goes on.
He’s given fewer than five public interviews in the past decade. When he has talked, he shares high-level principles:
But the thousands of micro-decisions he makes in the studio? Those aren’t documented.
How does he know when to rewrite a pre-chorus for the 47th time? What makes him choose one word over another for sound versus meaning? When does he stop?
That level of expertise (the tacit, moment-to-moment judgment) isn’t captured.
Same with Serban Ghenea, who’s mixed most major pop hits of the past 25 years. He’s given maybe two interviews in twenty years. When he does talk, he says things like, “There’s no bag of tricks. The song dictates the tools.”
Rick’s point: Even when top practitioners share something, they don’t share the real how, the accumulated judgment that makes their work different.
That’s the nuance. That’s human.
And it’s not because you don’t have ANY documentation.
You probably have countless playbooks, specs, and guides.
But the micro-moves that separate your best performers from everyone else? Those aren’t captured.
That’s your Max Martin Gap.
The judgment calls that make your frameworks work in YOUR context.
Rick tested this with a simple example:
Why? Different training data.
ChatGPT trained heavily on Wikipedia. The other models trained on YouTube, where the answer exists in a popular video.
Same question. Different models. Different training data. Different results.
(If you want to see Rick break this down with the 52 factorial test, including his 9yo son reciting it years ago, watch the full video.)
Now think about your GTM motion:
If that’s not written down, AI can’t learn it.
More importantly: your next hire can’t either.
Let’s say you ask ChatGPT to draft a sales email. Build a positioning doc. Forecast pipeline. Whatever.
It spits out something fluent and confident.
Your team thinks, “Good enough.”
You ship it.
Except it’s generic. You just spent time and money to sound like everyone else.
(Ironically, B2B Tech Marketing has been doing this without AI for years!)
The trap isn’t that AI gets it wrong (it “can make mistakes”).
The trap is that AI gets it average, and average feels good enough when you’re moving fast.
I’ve fallen into this trap. Everyone does. But once you see the patterns, you self-correct (hopefully).
I use AI every day (like even this article). Not to write for me, but to help me ideate, research, fact-check, and provide structure.
(I have ADHD so every bit of structure helps... haha!)
I also acknowledge that I use AI as an assistant at the bottom of every article.
Instead of feeding AI generic prompts, train the model on actual documented process.
The “real how” from your best performers can help you stand out and scale faster than those who rewrite other blog posts and summarize frameworks they never use.
The companies that will win with AI aren’t the ones with the best prompts. They’re the ones who documented what makes them different before they handed anything to a model.
David Hurley said it best:
If we accept generic AI output because it’s fast, we’re accepting low-level standards... or no standards at all.
Rick’s video is a good reminder that the AI limitations in the music industry applies to any industry.
We already know how AI is replacing manual tasks (like every other major tech revolution).
But will it ever be able to replace the tacit nuance that comes from human creativity?
We don’t need to freak out.
We just need to be creative.
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Form fills and PDF downloads indicate curiosity, not intent. Treat forms as supporting evidence inside a broader pattern. Route on patterns for speed. Use causality to decide where to invest.
GTM teams are coming to realize buyers don’t move in a straight line.
As decisions get real, more time can pass and more stakeholders can show up.
The part many still miss: a contact form fill or a gated PDF download doesn’t change any of that. It’s a weak signal until it’s part of a bigger picture.
Paula Skaper, over at 33 Dolphins, posted this LinkedIn comment on a recent article:
“Would love to see a follow up on buying signals. Something tells me ‘filling in a form to download a lead magnet’ is guaranteed to be on the list.”
She’s right.
An eBook gate, by itself, is weak. It proves someone traded an email for content. It does not prove buying intent.
Some forms can be strong: demo request, pricing/config quote, security questionnaire, RFP upload. They get stronger when surrounded by hotter activity in a short window.
Buyers don’t march left-to-right when we want them to.
They loop. They backtrack. They add and remove people. They rinse and repeat on their time, not ours.
Read the pattern across people, content, and time, not a sequence.
Our job is to spot momentum, not enforce a linear path.
Routing rule (make it binary):
Everything else goes to nurture until the pattern appears.
“We need to stop tossing early interest over the fence. Marketers must own that signal until it’s contextualized, confirmed, and validated.”
Kerry Cunningham, 6sense
Patterns are correlation. That’s fine for routing fast. When it’s time to fund programs, you need causal analysis.
Simple play:
For more detail, check out the 6-Part Causal CMO series I did with Mark Stouse.
If a signal doesn’t move win rate or forecast quality, downgrade it.
Keep the list short. Keep it honest.
Adopt the trigger for 30 days. Tag “Signal of Record” on every opp.
Share the lift.
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Contrary to what martech tools like HubSpot say, marketing has never been predictable or linear. And it doesn’t drive revenue directly. It multiplies sales effectiveness, shortens the path to “yes,” and protects future growth. Here’s what every B2B tech leader should know.
Last week, HubSpot sent out this email:
The gist of it was to promote their new AI-powered features, as if AI will make marketing predictable like it once was.
“Marketing used to be predictable and linear. Not anymore.”
Really?
Um, Marketing was never predictable. Never linear. Not in B2B, not in B2C, not anywhere.
And framing it this way is exactly why companies waste time and money chasing shortcuts.
For the past 15–20 years, B2B marketing has been hooked on “performance marketing” because of promises made by software companies (HubSpot is not mutually exclusive here).
Marketers were sold a bill of goods: predictable funnels, lead-to-revenue models, and real-time multi-touch attribution displayed on pretty dashboards.
Hey, I’m just as guilty. I drank the Kool-Aid too.
But the results? Nothing to be excited about.
Research and data analysis have proven the opposite:
The idea that marketing once marched buyers in a straight line from awareness to deal? That's a fairy tale.
And the idea that AI will restore predictability? Wishful thinking.
Here’s the real distinction every executive should know:
Marketing creates the conditions for sales to succeed:
But it does not “drive” revenue on its own. Presenting it that way sets CMOs up against an impossible benchmark, one the CFO and board will never buy.
Dale W. Harrison explains this clearly in How Marketing Creates Revenue.
His model shows the incremental effects of sales, performance marketing, and brand marketing combined:
This proves that marketing is a non-linear multiplier of a linear function like sales. It produces zero direct revenue, but it radically amplifies sales.
PS: Dale’s The Mythology of Brand Growth is an insightful follow-up to Byron Sharp’s book, How Brands Grow (mentioned above). Definitely worth reading.
Mark Stouse and I have discussed this many times.
He reinforces this reality with two important points:
This is CFO-friendly language: delayed, compounding effects that expand efficiency and probability.
The “predictable funnel” story keeps getting pushed for a number of reasons.
This last point matters. When we stop questioning, we stop leading. And when we outsource our thinking to vendors, we reduce marketing to a support function instead of a growth function.
The messy reality? It’s inherent in the system.
But that’s okay! Because the real job of marketing is to:
This is how CEOs and CFOs should frame marketing internally: as a capital investment in future efficiency, not as a vending machine for leads.
If you lead a B2B tech company, here are three shifts worth making:
1. Stop asking for linear attribution.
2. Treat marketing outcomes as probabilistic not deterministic.
3. Listen to Peter Drucker: Reframe marketing as capital allocation, not cost.
And here’s another important point:
Marketing must raise the bar. We’ve grown complacent. Too often we accept whatever martech vendors tell us instead of doing the harder work of insight, strategy, positioning, and brand-building. Marketing can only reclaim its full power when we start asking tougher questions again.
Marketing is not predictable or linear. It never was.
It’s complex and messy.
Why? Because people are complex, irrational, and messy.
The power of marketing lies in its ability to compound over time, multiply sales, and shape the likelihood of revenue.
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B2B marketing and sales systems have been built around funnels, gated content, and MQLs. Yet that’s not how marketers or salespeople buy themselves. Research from Gartner, The CMO Survey, 6sense, and TrustRadius shows that buyers constantly move in and out of non-linear journeys. Most prefer self-serve information and make decisions based on brand familiarity. To fix the disconnect, B2B leaders need to rebalance short-term lead gen with long-term brand building, align metrics to real buying signals, and simplify the path to purchase... just like how they would buy!
Marketers don’t buy the way they market.
Salespeople don’t buy the way they sell.
And yet this is the system they keep building. Funnels. Gates. Attribution reports. MQL handoffs.
If we wouldn’t buy this way ourselves, why do we expect our customers to?
For the past 15-20 years, B2B Marketing has drifted from strategy to tactics.
The 4Ps still exist, but most teams only control one of them: Promotion.
Pricing lives with Finance. Place lives with Ops. Product lives with Product.
According to MarketingWeek, just over a quarter of marketers (25.8%) influence pricing, and only 7.2% play a role in distribution.
That vacuum gave SaaS a chance to redefine marketing as lead generation. “Demand gen” became another name for filling forms. Forrester and 6sense's Kerry Cunningham have both argued that MQLs don’t reflect how people actually buy.
Funnels and attribution models look pretty on a dashboard. But buyers don’t move in straight lines.
Research from Gartner shows the B2B journey is non-linear (hasn’t it always been?!?). Buyers have always looped, revisited, and often repeated steps out of order over weeks, months, and even years before creating their Day 1 list.
And in complex B2B deals, 77% of buyers say their latest purchase was “very complex or difficult”, with 6–10 decision makers involved and most research done before speaking to sales (AdvertisingWeek).
So while GTM teams celebrate form fills, most real buying happens elsewhere.
For CEOs: Wasted spend. Growth slows when funnels don’t reflect reality.
For CFOs: Inefficiency. Chasing MQLs inflates CAC and distorts ROI.
For CMOs: Frustration. Defending metrics everyone knows don’t map to revenue.
There is a way forward.
Brand + Buying Signals + Sales Alignment = Better Buying Experience
The good news is many seasoned B2B Marketing leaders are calling out the elephant and getting back to basics.
The mood is changing.
For more, dive deeper with the End Of MQLs series.
in a nutshell, Marketers don’t buy the way they market. Salespeople don’t buy the way they sell.
And just like the buyers they are marketing and selling to, they never have either!
It’s time to stop forcing buyers through systems we wouldn’t tolerate ourselves.
The best time to reset was yesterday.
The second-best time is now.
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By Gerard Pietrykiewicz and Achim Klor
Achim is a fractional CMO who helps B2B GTM teams with brand-building and AI adoption. Gerard is a seasoned project manager and executive coach helping teams deliver software that actually works.
AI adoption gets stalled by leadership gaps: confusing policies, employee fear, and leaders who say “go” but don’t show how. If this feels a bit like Groundhog Day, you’re not alone. We’ve seen similar adoption challenges with desktop publishing, the Internet and World Wide Web, and blockchain. The technology is ready, but organizations stumble on the people side. This article looks at what leaders can do right now to remove those barriers and make adoption a little less stressful.
Jim Collins, in How The Mighty Fall, describes how once-great companies decline: hubris born of success, denial of risk, and grasping for silver bullets instead of facing reality. AI adoption sits at a similar crossroads. Companies that wait and assume their past success buys them time risk sliding down a similar path.
Reset (5 min):
Decisions today (10 min):
Guardrails (10 min):
Metrics (5 min):
Employees avoid tools they don’t understand. If your AI usage rules look like a legal brief, adoption will stall. It's hard for any company to have a policy loose enough to allow for easy adoption and experimentation, yet restrictive enough to prevent critical data leakage.
Large corporations often have the budget, legal teams, and even their own data centers to set up AI policies and infrastructure. That gives them speed at scale.
Smaller companies are technically more nimble, but without sufficient resources, they often default to over-restriction, sometimes banning AI entirely out of fear of risk. That means lost productivity and missed learning opportunities.
Opportunity: Make policies visual, clear, and quick to navigate. The goal isn’t control. It’s confidence. Guidance like the NIST AI Risk Management Framework shows how clarity enables trustworthy, scalable use (NIST).
Employees fear what they don’t understand. And one-size-fits-all training doesn’t help.
When people see AI applied to their specific role (automating a report, simplifying customer emails), that fear turns into enthusiasm.
Pilot programs work. Early adopters can demonstrate real use cases, and their wins spread fast inside the org.
Opportunity: Treat those early adopters as internal champions. Prosci’s research shows “change agents” accelerate adoption (Prosci). Then turn those wins into short internal stories and customer-facing examples. That’s how adoption builds brand credibility, not just productivity.
When executives hesitate, teams hesitate. The reverse is also true: when leaders use AI themselves, adoption accelerates.
Research on organizational change is clear: active, visible sponsorship is a top success factor (Prosci). It signals that experimentation is safe and expected.
And there’s an external benefit too. Leaders who show their own AI use give customers and partners confidence. It’s a market signal.
Opportunity: Leaders can’t delegate this. They need to be participants, not just sponsors.
To make AI adoption successful, leaders must create an environment where experimentation feels safe and useful.
The parallels to earlier waves of tech adoption are uncanny: the ones who figured this out first didn’t just get more efficient. They were remembered as the ones who defined the category because they were more effective adopting the tech.
The risk of waiting isn’t just lost productivity. It’s losing the perception battle before you even start. Credible stories and visible leadership shape buying decisions and long-term trust (Edelman–LinkedIn).
Leaders: simplify, experiment, participate, and share your wins. Your teams and your customers will thank you.
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I read an interesting article on TechRadar by John-Anthony Disotto. He used the 80/20 rule to create a prompt that “makes learning skills incredibly easy.” It’s not mathematically deep or overly technical. So for fun, I gave it a go using B2B GTM. It worked, but not the way I expected.
The prompt is dead simple:
“Give me the 80/20 of X.”
Want to learn how to kickflip, cook sugo, or serve a tennis ball? Ask your favorite GenAI tool for the 80/20 and it will spit back a decent analysis based on Pareto’s famous principle.
That said, as with all “absolutes”, take it with a grain of salt and pull out the nuggets you need.
But it got me thinking:
Could this actually work for something as messy and misunderstood as B2B tech GTM?
I gave it a shot.
I first asked ChatGPT this prompt:
“What’s the 80/20 of B2B tech GTM in 2025?”
The answer came back fast using typical AI phrasing.
80% of teams are stuck in the past:
20% are evolving:
The gist wasn’t wrong, but a few things were headscratchers, like “Using Causal AI” and “Marketing drives strategy.”
It sounded too binary. Too neat.
So I then asked ChatGPT:
“Rigorously fact-check your 80/20 assessment for accuracy and credibility.”
The revised output was a bit more grounded.
20% of teams are winning because they:
80% are plateaued by:
OK, better. But I still wasn’t convinced.
Still sceptical, I sent the same prompt to Claude.
Claude came back with a softer read. It said it’s more like a 60/40 split. Most teams are adapting, but slowly and inconsistently.
Claude made some good points:
I went back and forth with Claude and ChatGPT. Both made valid calls and pointed out where they aligned and where they differed.
ChatGPT pushed back on Claude’s assessment:
“Claude’s response is solid—and mostly accurate. But Claude’s framing underplays the issues.”
After more back-and-forth, we landed somewhere in the middle.
60/25/15 is a more accurate split.
60% are stuck in no man’s land:
25% of B2B teams are doing the work:
15% are way behind:
Which one sounds like your Monday morning standup?
If you’re in the 60%, pick one:
Every modern B2B GTM team should be asking:
AI, brand, content, buyer insight—none of it works in isolation.
What separates the top 25% isn’t access. It’s consistency. They’ve operationalized what the rest are still experimenting with.
The “80/20” prompt worked, but not how I expected.
It won’t give you a perfect framework. That’s OK. It doesn’t need to be perfect.
A yardstick that validates what you already know and uncovers some new truths is more than good enough. Whether the actual number is 65.7% instead of 60% doesn’t really matter.
The point is, most GTM teams in 2025 know what to do. They’re just not doing it strategically or consistently. And they’re not proving it works.
That’s the gap.
The teams pulling ahead aren’t chasing leads or trends. They’re going back to the basics, putting insight and strategy ahead of tactics. And they’re doing it better, faster, and with accountability.
They treat brand as a signal, not decoration. AI as infrastructure, not novelty. GTM as shared responsibility, not departmental silos.
And they measure what matters, not what’s easy.
They do the hard part first.
Where does your GTM sit?
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