by Christina | Jun 10, 2026 | 2026, Blog
This post is taken from the upcoming book The Hidden Buyer Journey for more information on the book see this link.
There is a question that every sales and marketing leader should be asking right now, and almost none of them are: What kind of selling relationship are we actually in?
Not which CRM you use. Not which cadence tool you’ve deployed or which AI platform you’re evaluating. The relationship itself – the fundamental nature of how your organization connects with buyers. Because that question, more than any tool or technology decision you’ll make this year, determines whether you win or lose the deals that matter most.
There are four selling relationships that now define B2B commerce. Three of them are scaling faster than anyone predicted. One of them is quietly disappearing. And it happens to be the only one that has ever reliably closed a complex deal.
Machine-to-Machine
The first relationship requires no human involvement on either side. Algorithms are buying from algorithms. Automated procurement systems are evaluating, selecting, and transacting with automated selling systems. No relationship is built. No trust is earned. No human judgment is involved.
This relationship is efficient, scalable, and completely devoid of the connection that built commerce in the first place. For renewals, replenishment, and transactional purchases, it works. For anything complex, anything that requires a buyer to take a real risk with their organization’s money and their own reputation, it falls short of what’s needed.
Machine-to-Human
The second relationship is what greets most buyers before they ever speak to a rep. The automated email sequence. The personalized ad served by an algorithm that knows their job title and their browsing history. The chatbot that answers their first question. The triggered nurture campaign that follows them through a journey the selling organization designed but doesn’t actually see.
By the time a human seller enters the conversation, the buyer has already formed an impression – shaped entirely by machines that know what the buyer does but nothing about who they actually are. Their personality. Their personal risk. Their motivations. Their fears. None of that is captured in the data feeding the machine.
Human-to-Machine
The third relationship is where most sales reps actually live – and it’s the one that gets talked about the least. The rep is technically in the process, but they’re selling into a machine rather than to a person. Entering data into a CRM. Working system-generated call queues. Following algorithm-determined priorities. Submitting proposals through procurement portals. Responding to automated RFP systems.
The rep’s judgment, intuition, and ability to read a room have been systematically replaced by process. They’re executing a workflow rather than building a relationship. And the machine on the other end doesn’t trust, doesn’t feel, and doesn’t stake its reputation on anything.
Human-to-Human
The fourth relationship is the one that built every great sales organization in history. It’s where trust gets built, where personality gets read, where a buyer decides whether the person across the table is worth staking their reputation on. It’s the relationship where a rep earns the right to be chosen – not because their product scored highest in the evaluation matrix, but because the buyer believes in the person behind the promise.
And it’s being squeezed into whatever time is left over after the other three relationships have consumed the rep’s day. Which, for most reps, isn’t much.
The Uncomfortable Truth
We gave up on it. Not intentionally. Not all at once. But incrementally, deal by deal, quarter by quarter, as performance metrics declined and the industry kept reaching for the same answer – more automation, more volume, more technology. When email open rates fell, we sent more emails. When win rates dropped, we added another tool to the stack.
What we never stopped to ask was whether the problem was our understanding of the human side of the equation. Not because humans were failing, but because we had built systems that were blind to everything that makes a human buyer tick.
The hidden motivations. The personality driving the decision. The personal risk attached to every significant purchase. None of that appears in a lead score or an engagement metric. And because we couldn’t measure it, we stopped looking for it.
The research is unambiguous. Buyers are more emotionally driven than any of our systems acknowledge. The factors that actually determine whether a deal closes – trust, credibility, personal connection, and a genuine understanding of what the buyer is risking – are human factors. They always have been.
AI will accelerate all of this. The first three relationships will scale in ways we can’t yet fully predict. But here’s what the data shows, across thousands of buyers and hundreds of real deals: the human-to-human relationship is still the one that closes.
The sellers who win in the age of AI won’t be the ones who automate the most. They’ll be the ones who are most irreplaceably human. That turns out to be the most competitive advantage left in modern B2B selling.
The question is whether you’re investing in it.
This post came from the upcoming book The Hidden Buyer Journey for more information on the book see this link.
by Christina | Apr 29, 2026 | 2026, Blog
I went to a developer conference and accidentally learned something profound about human nature. It started innocently enough – the All Things AI Conference in Durham, NC had a title too good to pass up.
What I didn’t expect was to be the only marketer among 2,500 developers, nodding along as whurly, CEO of Strangeworks (one name, all lowercase), dove deep into quantum computing and AI. I was in over my head. But sometimes that’s exactly where the best insights hide.
It was until Luis Lastras, Director of Language and Multimodal Technology at IBM began talking about “small models” that I finally found something I recognized. Luis said something that struck me that I didn’t realize – and I think I’m not alone – “hallucinations are intentional.” Say what?
According to Luis hallucinations are a way for developers to learn how models work. Because the models operate autonomously they don’t filter out what they output – at least not yet. Think of letting your grandfather who lost his filter loose at a dinner party.
It’s one of things that IBM is working on. Small models validate outputs and commands at various stages in the process to reduce hallucinations.
Anyone who’s worked with AI has experienced hallucination from made up sources to statistics that are just plain wrong. But what Lastras shared was something I didn’t realize, it’s the little extra pieces of information intended to be helpful that AI tools add in that weren’t asked for in the prompt.
For example, he showed a demo of a prompt asking how many moons Mars has and the response came back with two and their names, with the added extra – the distance from Earth which was not requested.
The distance between the planets may have been right but it requires another step to validate which then triggered a fascinating article I had read over the weekend.
In a study by Elon University conducted with 500 AI users (US adults) last year, almost 70% believed that AI models are at least as smart as they are, with 26% believing that they are “a lot smarter.”
What is more concerning is that we believe that AI is thinking like humans. As the article in the Wall Street Journal article Why Even Smart People Believe AI is Really Thinking goes on to say “our cognitive biases developed to help us survive in complex social environments…evolved to view linguistics fluency as a proxy for intelligence, engagement and helpfulness as indicators of trustworthiness.”
The same tendency innate to humans that leads us to trust social creatures who must cooperate for survival are leading us to trust systems that appear to listen, understand and want to help us.
The more AI tools and bots act like humans, the more likely we are to trust them. Which brings us back to the hallucination. The more AI tools act like they’re being helpful, the more likely we are to miss that “little extra” piece of information that wasn’t requested.
The convergence of intentional hallucinations and our deeply wired human instinct to trust fluent, helpful communicators creates a perfect storm of misplaced confidence.
As AI tools grow more sophisticated and human-like, our evolutionary instincts will only make it harder to maintain the critical distance needed to catch the errors, embellishments, and unrequested additions that slip through.
The good news is that awareness is the first step. Whether it’s IBM’s small models validating outputs in real time or simply slowing down to verify what AI hands us, the antidote to a cognitive bias millions of years in the making is something refreshingly simple – a healthy dose of human skepticism.
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.
The SEO toolkit you know, plus the AI visibility data you need.
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 | Jun 26, 2025 | 2025, Blog
Over a decade ago, I joined my first adtech company after kicking off my career in the traditional advertising agency world. And for approximately that same amount of time, I’ve been writing bylines for executive thought leaders at a multitude of companies about three things:
- The cannibalization of the adtech industry
- The death of the cookie
- The impending AI boom
At a certain point, it all became white noise. The industry news equivalent of Bill Murray’s character waking up to “I’ve Got You, Babe” for the hundredth time in Groundhog Day. “Yes”, all of us marketers said to ourselves, “The cookie will die, AI will take our jobs, the industry will continue to consolidate until it forms a hulking monolith where creativity goes to die. In the meantime, how can I prove the quantitative value of our latest brand awareness campaign?”
And that very line of thinking, dear reader, is the reason marketing is not dead (neither, incidentally, is the cookie). Because while we balance the simultaneously ever-changing and yet ever-static news of our industry, we also still have work to do. As Hitchhiker’s Guide to the Galaxy reminds us, we really only have one job amidst the chaos: Don’t Panic. And ideally, we can take that one step further and not only resist panic (or worse, indifference), but also embrace curiosity.
We’ve woken up in Punxsutawney again. How will we change things up?
Bust that Black Box Wide Open
Succeeding in today’s adtech landscape isn’t necessarily about being the best. It’s about innovating at the fastest pace (as a wise CEO once told me, the fast eat the slow), and being willing to put your assumptions to the test with a truly objective eye and be radically transparent about what you find. Once upon a time, it was acceptable for adtech companies to operate in a black box, waving clients off with a pat on the head and a “you don’t have to worry about that”. But now, with AI democratizing analytics at breakneck speed, the black box needs to be replaced with a crystal clear swimming pool.
Go ahead, invite your customers to dive right on into the data. Let them play with it, understand it, ask questions about it. This is a critical shift away from the profoundly overused “proprietary” workings of organizations just a few short years ago. Successful organizations, and successful marketers, should now hang their hats not on secrets kept, but on knowledge shared.
This is really just a natural progression of the transparency that came for consumers with GDPR and CCPA. While we have been regulated into greater transparency for the ultimate audience of our media, there is still a substantial amount of gatekeeping between adtech companies and the organizations they serve.
The best, easiest, and most criminally back-burnered way to stand out and create greater transparency is with a Customer Advisory Board. Adtech is no longer standing on the mountain with a megaphone yelling down to others at base camp what it’s going to be doing. This is a serious two-way conversation, and organizations that invite that conversation with their customers, rather than ignore it, will come out on top.
So, talk to the folks who love your product, the folks who hate it, the folks who gave you that criminal “6” rating on your CSAT. Invite them into a conversation, actually utilize the amazing product marketers you probably just have making decks and one-pagers right now, and build a program that breaks you out of the monolith and puts you on the map as the rarest of all things: an adtech company that cares what its customers have to say.
Balance the Long and Short of It
Another thing that never changed in my entire adtech life? The pressure to balance short-term quarterly goals with long-term, sustainable company growth. On the one hand, as Groundhog Day reminds us, nothing that you do in a single day matters if the day is simply doomed to repeat itself again. Hello, Sisyphus.
This is how it can often feel when launching a new program without any guarantee that you’ll be able to run it long enough to produce results. “This webinar didn’t work” is something I often heard, despite the reality that a single webinar never works. An ongoing webinar program does. Yet it can be hard to see the forest through the trees when the arguments from one side of the house for short-term needs are concrete, and the value of longer-term programs can come across as theoretical.
So, what’s a modern marketer to do? Hedge your bets, and back your opinions with data. The best advances always come from a test and learn approach that allows you to share progress (whether good or bad) at a consistent cadence and demonstrate the changes you’re making along the way. Sprints of two weeks to one month for demand generation activities gave me the boost I needed toward short-term goals while also buying me the breathing room to focus on the long game.
As we all know, the best laid strategy will always be better received with objective data to back it up. My personal favorite marketing chart [below] details the manner in which sales and demand-focused activations can lead to a shorter term boost in sales, but ultimately it is brand awareness that leads to sustainable success over 12+ months.
If I had a nickel for every time this chart appeared in a deck and helped me get more budget for experimentation and a test and learn approach, I would have at least enough nickels to buy a coffee for the person who originally shared it with me.
The Day After Groundhog Day
While there is no guarantee of escaping the certain inevitable loops of any industry, there is always a path to innovation, experimentation and improvement. When you pair radical transparency across your customer base with a data-driven, test-and-learn approach that equally balances long and short term internal goals, you’ll find yourself in solid fighting shape to survive the cannibalization of adtech, the impending coup of our AI overlords, and – if it ever actually were to happen – the death of the cookie.
Rachel Peterson is a former marketing executive specializing in enterprise software with a track record of scaling multiple B2B companies to $100M+ in ARR. She now works as an author and consultant.
by Christina | May 7, 2025 | 2025
Your best copy might sound like AI to clients. See why creative value is under pressure and what it means for agencies.
AI is changing how clients view creative work. Even the best human efforts are starting to be questioned — not for quality, but for authenticity. I recently saw this firsthand.
The feedback I was not expecting
Below is recent feedback from a client on some content we created.
“I do have a piece of feedback for them. I’m not sure which AI writing tool they’re using to create these, but they may want to take a second pass… a lot of these pieces of copy are clearly first pass AI generations…”
The problem is that our copywriter didn’t use AI and would be offended by the feedback. He’s an award-winning creative with a big agency background. As you might expect, he’s vehemently opposed to using generative AI.
Where is the feedback coming from? It’s from a company whose marketing department strongly embraces generative AI for content creation. I guess they assume their agency is using it as well.
That will be a significant issue if you are on the agency side. Perhaps it already is, and I’m just now experiencing it. Our copywriter is very talented and has a lot of great experience. As a result, he is not inexpensive.
He warrants the rate he receives, now threatened by clients who will discount the value, assuming a tool is responsible for his output.
As more clients adopt generative AI tools for marketing, questions will arise regarding the outputs and cost of agency services, particularly creative agencies.
In this example, it’s content. However, the same scenario could apply to any creative service: creative concepting, AI image generation, media planning, AI audience segmentation, etc. AI is everywhere.
The real problem behind the perception
The reality of AI tools is that they are handy and efficient. As a result, we face the challenge of protecting the craftsmanship of our creative resources while leveraging the tools’ value.
I’ve always believed that good copy or content is an art. But what happens if no one appreciates the art? What will be its value?
Many creatives use AI tools for ideation, content refinement, editing, etc. However, to date, I’ve not seen any evidence that AI-generated or modified outputs perform any better than what humans have created in the past.
Dig deeper: What AI means for the future of agency-brand partnerships
AI vs. human-generated content
Maybe it’s not a choice of “either-or,” but knowing the advantages and disadvantages of each approach (see below) and when to use them situationally to our advantage.
AI-generated content
Human-generated content
Dig deeper: https://martech.org/riding-the-ai-tsunami-harnessing-creativity-and-efficiency-in-the-digital-age/
The blended future of creativity
After pulling this together, I realized where the client feedback came from in our latest content round. Over the last few months, we have been hand-crafting emails for announcements, follow-up emails to events and other marketing outreach activities.
This most recent round, and what generated the feedback, was a change in our approach. We profiled the personalities of the target audience and found that the most dominant personality type was one that prefers concise, to-the-point, no-nonsense content.
We removed the “emotional depth and empathy” mentioned as a strength of human-generated content. As a result, the content had an “emotional flatness,” making it sound like a machine wrote it. The blending of machine and man is not going away. The only real question is, will it improve our results?
Dig deeper: The AI-powered marketer’s roadmap to the future of content
