Why the Traditional Marketing Funnel Is Breaking Down

By David Ronald 

For years marketers have operated with a relatively familiar model.  

A prospect discovers your company, visits your website, downloads something, becomes a lead, enters a nurturing campaign and eventually talks to sales.  

It was never quite that simple, of course, but the model gave marketers a useful framework for thinking about how buyers moved from awareness to consideration to purchase.  

That model is becoming increasingly difficult to recognize.  

Today’s buyers are doing more research on their own, using more sources and interacting with companies later in the process. Increasingly, AI is sitting between the buyer and the information they are looking for.  

The result is a fundamental change in how people discover, evaluate and select B2B vendors. 

In this blog post I explain why the funnel is becoming harder to see and what you can do to address this. 

Your Website Is No Longer the Front Door

The traditional buyer journey assumed that your website was one of the primary places where prospects learned about your company.  

In my opinion that assumption is increasingly outdated.

A potential customer might hear about your company in a LinkedIn post, ask ChatGPT which companies solve a particular problem, read reviews on G2, ask colleagues in Slack for recommendations, watch a product demonstration on YouTube or search for customer opinions on Reddit.

They may do all of this before ever visiting your website.

Artificial intelligence is accelerating this behavior because it can synthesize information from multiple sources and present a buyer with an initial answer, or even a shortlist of vendors, without requiring the buyer to visit dozens of websites.

The result is a fundamental change in the role of the website.

A strong website is still crucial, of course, but it is increasingly becoming a validation destination rather than the starting point of discovery. By the time someone arrives, they may already know who you are, what you do and who your competitors are.  

The question is whether your website gives them enough evidence to keep you on the list. 

The Shortlist Is Being Created Earlier

This may be the biggest change of all.

Historically, marketing often focused on generating a broad pool of potential buyers and then helping sales narrow that pool down.

AI can reverse that process by helping buyers create their own shortlist before they ever identify themselves to a vendor. 

A buyer can ask, "What are the best platforms for solving this problem?" The answer might contain five companies. They can then ask, "Which of these is best for a 500-person company?" and the list gets smaller. They might follow that with, "How does Company A compare with Company B?" At this point, the buyer is already evaluating specific vendors, even though none of those vendors may know the buyer exists. 

G2's 2026 research found that AI chatbots had become the leading source influencing which software vendors make buyer shortlists, ahead of software review sites, market research firms and vendor websites. 

This is a profound shift because marketing is no longer simply trying to create awareness and capture demand but needing to influence the conversation that happens before demand becomes visible. 

Introducing the Dark Funnel

There has always been a part of the B2B buying process that marketers couldn't see.

Buyers talk to colleagues, forward emails, mention vendors in meetings and ask friends for recommendations. Private Slack groups, direct messages, online communities, podcasts and peer networks can all influence a buying decision without producing a trackable marketing interaction.

This "dark" activity has always existed, but the amount of buyer research happening outside traditional marketing channels is growing.

AI adds another layer. A buyer can conduct extensive research inside an AI assistant without generating a website visit, clicking an ad or filling out a form.

From the marketer's perspective, nothing happened but, from the buyer's perspective, a lot happened. They may have developed an opinion about your company, compared you with competitors and decided whether you belong on the shortlist, all without creating a single identifiable marketing signal.

This creates an uncomfortable measurement problem. A marketing team may look at declining organic traffic and conclude that its content strategy isn't working. At the same time, the company's brand may be appearing repeatedly in AI-generated recommendations and influencing buyers who eventually arrive through direct traffic.  

The activity is real but the attribution simply isn't. 

Stop Measuring Only What You Can Track

This doesn't mean marketers should abandon traditional metrics.

Website traffic, leads, conversion rates and pipeline remain important, and companies still need ways to determine whether marketing investments are producing business results. But those metrics no longer tell the entire story, particularly when much of the buyer journey happens before a prospect becomes identifiable.  

In my opinion, marketing teams need to start asking different questions, such as: 

• Are we appearing when buyers ask AI about our category?
• What does AI say about us compared with our competitors?
• Are customers and industry experts talking about us in places we don't control?
• Are our most important claims supported by credible third-party sources?
• Are prospects arriving at our website already knowing what they want?
• And how often are we being included in buyer shortlists?

These are harder questions to answer than "How many people downloaded our whitepaper?"

They are also increasingly important.  

Marketing measurement needs to expand beyond activities that can be neatly attributed to a campaign and start considering whether the company is influencing the broader market in which buying decisions are being made. 

Content Has a New Job

This shift also changes what good content looks like.

For years, marketers created content primarily to attract traffic, generate leads and support SEO. And, sure, those goals still matter, but content now has another job: helping buyers understand why your company matters.

This requires more than publishing generic articles filled with keywords. Companies need content that clearly explains what problem they solve, who they solve it for, how their approach differs, where they are particularly strong and where they may not be the best fit. 

They also need credible customer evidence, useful comparisons, clear explanations of their category and specific proof that supports their claims.

The goal isn't simply to produce more content but to create content that provides enough useful information for a buyer to understand the company and accurately represent it.

In other words, content needs to be useful even when the buyer never becomes a lead.

And that's an important mindset change.  

The objective isn't always to get someone to click, fill out a form or download something. Sometimes the objective is to make sure that when someone asks, "Who should I consider?", your company has a credible answer. 

Marketing Has to Become More Distributed

The old model placed much of marketing's responsibility on a relatively small number of controlled channels: the website, email, advertising and perhaps events.

The new buyer journey requires a broader approach because buyers are forming opinions across a much larger ecosystem. Your brand needs to show up across the places where customers actually learn, compare and validate companies.

That includes search and AI platforms, obviously, but it also includes industry publications, review sites, communities, social networks, podcasts, analyst reports, customer stories and third-party conversations.

This makes third-party credibility more important, not less. You can tell buyers that you're innovative, but a customer saying it is more powerful. You can say you're the market leader, but independent analysts, customers and industry experts can provide evidence. You can publish a comparison page explaining why you're better than the competition, but buyers will still look elsewhere for validation.  

The winning brands will increasingly be those that are easy to understand, easy to validate and easy to recommend. Marketing therefore has to think beyond what the company says about itself and focus on the broader body of information that buyers encounter while researching the category. 

Conclusion

So, what does all of this mean for marketers?

Well, it means the job is getting bigger.

Marketing can no longer think exclusively about generating demand that can be captured and measured. It has to influence demand that may remain invisible, make the company discoverable in AI-driven research, establish credibility in third-party conversations and make the company compelling when buyers finally arrive at the website.

This also means marketing has to work more closely with sales, product, customer success and even customers themselves.

The story being told across the market needs to be consistent, understandable and credible, whether that story comes from your website, a salesperson, a customer review, an industry publication, a LinkedIn post or an AI-generated answer.

Okay, so the traditional funnel isn't dead... 

But the assumption that marketers can see buyers moving through it is. 

The modern B2B buyer may spend days, or even weeks, researching your company without ever raising their hand. By the time that buyer contacts sales, much of the decision may already have been made.

The question for marketers was once, "How do we get buyers into our funnel?" Now it's, "How do we influence buyers before they ever enter it?"

And that is the new B2B buyer journey.

Thanks for reading my blog post.

Are you interested in discussing how to reinvent your marketing funnel? If so, feel free to get in touch. My email is david@alphabetworks.com – I look forward to hearing from you.

How to Write Technical Blog Posts that People Actually Want to Read

By David Ronald  

Okay, let’s be honest.  

Technical blog posts have a problem.  

Too often, they're written as if the primary objective is to prove that the author understands the technology. 

The result is technically accurate, thoroughly researched, and about as fun to read as a software manual.  

But it doesn't have to be that way.   

The best technical content does two things at once: it makes the reader smarter and gives them a reason to keep reading. 

In this blog post I explain how complex ideas can be written about in a way that feels clear, relevant and, ideally, enjoyable.

Let the Writing have Personality

Character comes from small things: a strong opinion, a little humor, a short sentence for emphasis, or an observation that makes the reader think, "Exactly." 

We've all seen the architecture diagram with 47 boxes and approximately 900 arrows, and that kind of observation can communicate more personality than another paragraph about "increasing architectural complexity." 

You don't need to make every sentence funny, either. In fact, please don't. A technical blog shouldn't read like a stand-up routine written by an engineer who just discovered coffee. 

The goal is simply to sound like a person rather than a documentation generator. 

Conversational language, varied sentence length and occasional humor can make even complicated technical subjects feel much more approachable. 

Begin with an Idea

Don't begin by defining the technology. 

Begin with something interesting that creates a question in the reader's mind. Instead of saying, "Artificial intelligence is transforming manufacturing," try something like: "The most expensive data in a semiconductor fab may be the data nobody can find." Now you've created curiosity, and the reader wants to know why that might be true. 

That's what a good opening should do. 

It shouldn't immediately tell readers everything they need to know; it should give them a reason to continue reading.  

A strong observation, surprising fact, provocative question or counterintuitive argument can be much more effective than the traditional opening that tells us a technology is "rapidly transforming the landscape." 

At this point, the landscape has been transformed so many times that it probably needs a break.

Have a Point of View

Technical content becomes much more interesting when the author believes something.  

Don't simply explain that companies are adopting multimodal AI; tell readers what you think about it and why it matters.  

For example: "Adding another AI model isn't going to solve the problem of fragmented manufacturing data. The real challenge is giving AI the context to understand what that data means." 

Now there's an argument.  

The reader can agree, disagree or keep reading to find out whether you're right. That's much more engaging than simply presenting a collection of facts and hoping someone makes an interesting conclusion from them.

Explain Why the Technology Matters

Technical explanations are most effective when they begin with a real problem rather than a definition. 

Rather than immediately explaining how retrieval-augmented generation works, start with the problem: an LLM can be remarkably good at answering questions, and remarkably confident when it doesn't know the answer.  

Then explain how RAG addresses that problem. 

The technology becomes easier to understand because the reader already knows why it matters. A useful formula is problem → why existing approaches fall short → technology → practical consequence. 

This approach provides technical depth while keeping the reader focused on the bigger picture instead of making them wade through three paragraphs of terminology before discovering why any of it matters.

Tell Stories and Use Examples

Even highly technical subjects benefit from storytelling. 

Instead of saying, "Engineers need to correlate information from multiple sources," describe the engineer investigating a failed component: opening a failure report, searching historical test results, examining an image, checking a spreadsheet and asking whether anyone has seen the same problem before. 

Now the reader can see the problem rather than simply being told about it. 

Once they understand the situation, they're much more receptive to an explanation of how technology can help. 

Concrete questions, workflows and scenarios are particularly effective because they turn abstract capabilities into something readers can visualize. "AI can analyze multimodal data" is abstract. "Can we find every previous failure that looks like this one?" is a question a real person might actually ask.

Don't confuse complexity with expertise

Technical credibility doesn't come from using the most jargon or explaining every possible technical detail.  

It comes from knowing which details matter and leaving the rest out. A good test is simple: Am I explaining this because the reader needs to know it, or because I want to demonstrate that I know it? 

If it's the second, cut it. The strongest technical writers understand that expertise isn't demonstrated by making something complicated; it's demonstrated by making something complicated understandable. 

That's especially important when writing for business audiences, who usually care less about how a technology works in isolation than what it enables them to do.  

The goal is to make technical writing more human. The best technical blog posts leave readers thinking two things: "I learned something," and, just as importantly, "I actually enjoyed reading that." 

If you can accomplish both, you've done something considerably harder than explaining the technology. You've made someone want to read the next paragraph. 

Thanks for reading my blog post. 

Are you interested in discussing how to write blog posts that people actually want to read? If so, feel free to get in touch. My email is david@alphabetworks.com – I look forward to hearing from you.

Why Your Content Strategy Needs to Change

By David Ronald  

So, for years, content marketers have followed a simple strategy. 

Identify the keywords customers are searching for, create content around those keywords, optimize the content for search engines, drive traffic to a website, and convert that traffic into leads.

But something fundamental is changing…

Buyers are increasingly using AI to discover information, research problems, compare solutions and decide what to explore next.

And that means your content strategy needs to change with it. 

 

In this blog post I explore how to make the most of the changes and re-tune your content strategy. 

Search Is Becoming a Conversation

We marketers know that traditional search is based largely on keywords.  

For example, a buyer might search, "Best customer data platform for B2B companies."

But AI-powered search allows that same buyer to ask something much more complicated, such as, "We're a 500-person B2B SaaS company with a fragmented customer data environment. What should we look for in a customer data platform, which vendors are strongest for companies our size, and what are the biggest implementation risks?"

And that's a very different search experience.

Google describes this as a more conversational approach to search, allowing users to ask longer and more complex questions and continue with follow-up questions.

For we marketers, this creates an important shift…  

The goal now is no longer simply to rank for a keyword but to become one of the sources an AI system considers valuable when answering a customer's question. 

It's About Answer Optimization

Now, I’m not suggesting that keywords no longer matter.

They do. 

But we marketers need to think beyond individual keywords and start thinking about the questions behind those keywords.

For example, a company selling sales enablement software shouldn't just create content around "sales enablement software."  It should answer questions such as: 

• What is sales enablement?
• When does a company need sales enablement software?
• What should you look for in a sales enablement platform?
• How much does sales enablement software cost?
• What are the most common implementation challenges?
• How does sales enablement software compare with a traditional LMS?
• Which sales enablement metrics should companies track?
• What should a company do before purchasing sales enablement software?

These questions address different stages of the buying journey.

And collectively, they provide something much more valuable than a collection of keyword-optimized articles: a comprehensive body of knowledge around a customer's problem. 

Commodity Content Won't Cut It

AI is making it easy to create mediocre content, and a marketer can ask an AI tool to write an article about virtually any topic in seconds.

As a result, the internet is becoming increasingly saturated with generic articles that repeat information already available elsewhere. 

And this creates a problem for marketers.

If your article simply says what everyone else has already said, why should an AI system, or a human buyer, consider it particularly valuable?

Which is where original thinking becomes a competitive advantage. 

Authority Matters More Than Ever

There's another major change marketers need to understand. 

AI systems can draw on information from across the web. That means your company's visibility on third-party websites, industry publications, communities, podcasts, analyst sites and other authoritative sources can influence how your company is understood.

This makes traditional content marketing increasingly interconnected with brand building and digital authority.

Consider this scenario: 

• Company A publishes 100 articles on its own website but has almost no independent recognition.
• Company B publishes fewer articles but is frequently mentioned by customers, industry publications, analysts, podcasts and other respected sources.

Which company is more likely to be perceived as an authority? 

Don't Abandon SEO

None of this means SEO is dead. I’m not saying that. 

In fact, Google's own guidance makes the opposite point: many established SEO practices remain relevant for AI-powered search. 

But SEO should become part of a larger strategy.  

 And, at the end of the day, I like to think of it this way: great SEO helps search engines find and understand your content. A strong content strategy gives them something worth finding. And brand authority gives them a reason to trust it.  

That's a much more powerful combination in my opinion. 

Measure More Than Traffic

Finally, AI search requires marketers to rethink measurement.

Website traffic will continue to matter. But it shouldn't be the only metric.

We marketers should increasingly pay attention to questions such as: 

• What topics are we associated with?
• Which competitors are being recommended alongside us?
• What sources are AI systems citing when discussing our category?
• Are customers and third parties reinforcing the same positioning we communicate
• Is our content influencing pipeline?

Some of these measurements are still emerging.

And that's okay because, after all, the behavior of buyers is changing faster than the traditional marketing measurement stack. 

Conclusion

Some of the people that I speak with  view AI search as a threat to organic traffic.

But I think that's too narrow a perspective…  

In my opinion the companies that will mean in this new era are going to be the ones that develop the strongest point of view. This means: 

• They'll understand the questions their customers are asking.
• They'll create content that answers those questions better than anyone else.
• They'll build authority both on and off their websites.

And they'll make sure their brand is consistently associated with the problems and categories they want to own.

The AI search revolution is about creating content that is so useful, distinctive and authoritative that both machines and humans recognize its value.

And that’s a much bigger, and much more interesting, content marketing opportunity.

Thanks for reading my blog post.

Are you interested in discussing how to change your content strategy to take advantage of the Ai search revolution? If so, feel free to get in touch. My email is david@alphabetworks.com – I look forward to hearing from you.