by Christina | Mar 12, 2026 | 2026, Blog
AI rewards original insight, proprietary data and firsthand experience over length and polish. Here’s how content strategy must evolve.
There was a time when content marketing followed a predictable formula: pick a keyword, write 2,000 words around it, sprinkle in some headers and wait for Google to notice. It worked. Pages that said very little but said it at great length climbed the rankings and stayed there.
That era is ending, and most content teams haven’t realized it yet.
When we read web pages, we start at the top, skim the introduction and decide whether the author sounds smart. Google’s AI processes content differently. It breaks content into small semantic units – individual claims, definitions, data points and explanations – and evaluates each one on its own clarity and usefulness. A 3,000-word article that circles the same idea for 20 paragraphs doesn’t look comprehensive to an AI. It looks redundant.
This is a fundamental shift in how value gets assigned to content. Length used to be a proxy for depth. Now it’s just noise unless every section carries its own weight.
The long, keyword-circling blog posts that once dominated search are quietly losing ground to something leaner and more specific. AI Overview panels, featured snippets and conversational search results all pull from content that answers questions directly. They don’t reward buildup. They don’t care about your brand voice. They care about whether a specific paragraph contains a specific, helpful answer.
The old content playbook – where you’d research what competitors wrote and then write a slightly longer, slightly more polished version – is becoming a dead strategy. If five sites all paraphrase the same general knowledge, they’re not sources. They’re echoes. AI is getting remarkably good at telling the difference.
If you’re not a source, you’re a remix
If you’re not publishing original research, proprietary data or genuine firsthand insight, you’re not creating source material. You’re remixing what already exists. Remixes don’t get cited.
Think about how a large language model builds its responses. It synthesizes information from across the web, but it gravitates toward origin points – the study that produced the statistic, the company that ran the survey, the practitioner who documented what actually happened. Everyone downstream who rephrased that information is, from the AI’s perspective, a less reliable copy.
This isn’t speculation. We can already see it happening. Sites that publish original benchmarks, case studies with real numbers and first-person accounts of specific processes are showing up in AI-generated answers at disproportionate rates. Meanwhile, the ultimate guides that aggregate other people’s findings are getting compressed out of the picture.
Your customers search everywhere. Make sure your brand shows up.
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The new content strategy
The path forward is more straightforward than most people want to hear. Stop trying to sound authoritative. Be the source of the information.
That means running your own experiments and publishing the results, even when they’re messy. It means sharing internal data that your industry would find valuable – conversion rates, timelines, costs and failure points. It means writing from experience rather than just research, because experience is something AI can’t fabricate and can’t find anywhere else.
It also means getting comfortable with shorter, more focused content. A 400-word post that introduces a single original insight is worth more in this new landscape than a 4,000-word guide that synthesizes 10 other people’s ideas. One is a source. The other is a summary.
This doesn’t mean writing quality is irrelevant. Poorly structured, confusing content still fails. But the competitive advantage has shifted. Clear thinking matters more than elegant prose. Having something to say matters more than saying it beautifully.
Add something new or don’t publish
The content teams that will thrive in an AI-driven search environment are the ones that treat publishing as a knowledge contribution, not a marketing exercise. Every piece should add something to the conversation that didn’t exist before – a number, result or perspective earned through doing the work.
The question to ask before you hit publish is no longer “Does this rank?” It’s “Would an AI cite this?” If the honest answer is no, you’re not writing content. You’re writing filler.
by Christina | Jan 20, 2026 | 2026, Blog
Most of your best buying signals show up late in the sales cycle, but they’re invisible if the right contacts never make it into your CRM.
This problem has plagued sales and marketing organizations for as long as these functions have existed. Companies invest massive amounts in Martech stacks and sales databases, only to see them underperform – not because of the technology itself, but due to poor input.
Specifically, the issue is qualified, highly engaged contacts held tightly – like clutched pearls – by the sales force.
For years, the prevailing theory has been that sales doesn’t want marketing anywhere near its most valuable relationships. Sales executives often attribute the issue to competing priorities or a general lack of interest in “data entry.” Interpret that however you’d like.
The visibility gap
I’ve encountered this problem repeatedly when trying to map content consumption to the buying journey. Typically, we’re only able to connect 10%–15% of sales contacts to any measurable marketing engagement, such as content downloads, event attendance, or other interactions.
Recently, however, we had the opportunity to take a closer look under the hood.
A client shared their contacts, intent data, engagement data and – most importantly – sales email correspondence tied to active opportunities across more than a dozen accounts. The data covered hundreds of emails exchanged over a seven-month period. In some cases, we observed opportunities at inception; in others, we jumped in midstream and followed them through to close.
We mapped the emails chronologically and tracked every individual included in the conversations. It was only after reviewing the full arc of these communications that the real reason sales reps don’t enter new contacts into the database became clear.
Where are all these names coming from?
The first question we wanted to answer was simple: Where do these new contacts come from – and why?
What we found was remarkably consistent. As deals progress, new contacts tend to appear at three distinct points in the sales process:
- Demo requests: These typically expand the buying group by an average of seven to 10 people.
- Trial setup: This stage typically introduces an additional three to five contacts, often including stakeholders from other geographies within global organizations.
- Final presentation: Procurement and finance frequently enter the picture at this stage, and if the presentation is on-site, even more participants tend to appear.
Why don’t reps enter the names?
Contrary to popular belief, this isn’t about laziness or disinterest. It’s about focus.
As opportunities near closure, activity between the prospect and the sales rep increases – sometimes dramatically. Last-minute trial configurations, contract negotiations and master services agreements consume nearly all of the rep’s time and attention.
The excitement of a potential win – like the smell of blood in the water for sharks – puts reps into a sales frenzy. Their behavior becomes almost entirely reactive.
New contacts who aren’t directly participating in the email threads are viewed as peripheral. In practice, they become invisible. This blind spot is especially pronounced at the very moment when insight matters most.
Why enter them at all? What’s the upside?
What most reps don’t realize – given their narrow focus on closing the deal – is that these late-stage participants are often scrambling to get up to speed.
They visit the corporate website.
- They search for case studies.
- They download white papers.
- They watch on-demand videos.
Their goal is simple: become informed enough to influence the final decision.
That behavior is precisely what makes them valuable.
If – and it’s a big if – reps take the time to enter these contacts into the database, their sudden spike in activity can surface powerful intent signals.
A real-world example
In one opportunity, a CEO entered the buying process shortly before an on-site presentation. The decision came down to the incumbent vendor and our client.
That CEO searched for a specific term more than 35 times over two weeks.
Because the contact was identified, that insight surfaced. The sales team redesigned the final presentation to focus heavily on that topic and directly connect it to the client’s value proposition.
They won the deal.
The fix is cultural, not technical
This isn’t a Salesforce problem.
It isn’t a HubSpot problem.
And it certainly isn’t a marketing problem.
It’s a process and mindset problem.
The most valuable buying signals often appear late in the sales cycle, introduced by stakeholders who weren’t part of the early conversations. When those contacts never make it into the system, organizations lose visibility at the exact moment insight can influence outcomes.
Sales teams don’t need more tools – they need a clearer understanding of the upside. Capturing late-stage contacts isn’t about helping marketing run better reports. It’s about giving sales an unfair advantage: real-time visibility into what decision-makers care about most.
When those contacts are entered, intent data lights up. Content consumption becomes visible. Messaging can be adjusted. Presentations get sharper. Win rates improve.
Until organizations address this blind spot, marketing will continue to look ineffective, intent data will appear incomplete, and sales teams will unknowingly leave leverage on the table.
