The Human Side of AI: John Pehler on Context, Judgment and the Handoffs That Hold Marketing Back
ICYMI John Pehler catches up with 10 Minute Martech on investing in people, preserving context and improving how work moves between teams and systems.
“It’s not that the martech stack is the constraint, it is the handoffs.”
A team can have capable tools and still struggle to turn information into useful action. Research reaches the content team without the thinking behind it. Campaign results reach sales without enough context to support a conversation. AI produces an answer, but the next person doesn’t know what informed it.
In this episode of 10 Minute Martech, John Pehler, founder and principal of E3 Omni, joins host Sara Faatz to discuss the human and organizational changes that make AI useful.
Their conversation covers why AI investment needs to include people, how buyer expectations are changing and why judgment becomes more valuable as generating content and analysis gets easier.
John’s Big Idea: Invest in How People Work With AI
John sees AI transformation as a cultural change that requires organizations to examine how work gets done.
That includes the tools employees receive, the processes they follow and the information they pass to colleagues. Introducing AI into an existing workflow raises questions about responsibility, context and how people assess the results.
Drawing on the Digital Momentum project with Flywheel, John highlights a striking investment gap. He reports that 84% of companies across industries were investing in some level of AI, while only 14% were investing in the humans working with those capabilities.
For John, those findings suggest that too many organizations still approach AI primarily as a technology project. Buying access is only part of the work. Employees also need support to apply it to meaningful problems and make informed decisions about its output.
The starting point is understanding the problem and the people affected by it. Without that, an organization can introduce new technology without creating much value for its customers.
John Pehler’s 10 Memorable Moments on 10 Minute Martech
1. The Same Prompt Doesn’t Guarantee Shared Understanding
John opens with a challenge for teams working with AI: two people can use the same tool and prompt yet receive different answers.
He encourages organizations to examine what information was available to the AI and what informed its response. Sharing an answer alone may leave the next person without the background needed to interpret it. That matters when several people and tools contribute to the same piece of work.
2. Context Needs to Travel With the Output
When an AI-assisted task passes to another person, the handoff needs to carry enough context for the work to continue sensibly.
What problem was being addressed? What information shaped the result? What assumptions still need checking?
John warns that effectiveness can drift when those details disappear between steps. Passing along a document or answer is useful only if the recipient can understand its purpose and decide how to use it.
3. AI Changes How Work Gets Done
John returns repeatedly to the operating model: how work moves through an organization to support business outcomes.
AI adoption therefore involves organizational design as well as technology. Teams need to consider how responsibilities connect, where decisions happen and what colleagues need from one another.
He references researchers examining the difference between meaningful augmentation and mediocre automation, highlighting the importance of how a capability changes the work itself.
4. Smaller Businesses Can Make Meaningful Progress
John sees successful adoption in small and medium-sized businesses as well as larger organizations.
A common feature is leadership ownership. The CEO, president or another leader takes responsibility for helping the business become what he calls “AI-influenced.”
That can begin with practical changes: giving employees appropriate tools, establishing governance and celebrating successes. Sharing those examples helps others recognize where a similar approach could improve their own workflow.
5. Technology Investment Needs a People Plan
The Digital Momentum research combined surveys, individual interviews, group conversations and secondary research to examine how organizations sustain progress.
The gap John describes between AI investment and investment in people is central to his argument. Access to technology doesn’t establish whether employees can use it effectively.
Organizations need to consider how people will develop the understanding and confidence to apply AI, assess its contribution and connect it with the business problems they are responsible for solving.
6. Buyers May Research You Without Visiting Your Website
John describes AI as increasingly sitting between the buyer and the brand. It can interpret questions, assess available information and present an answer before someone visits a company website.
That changes how marketers think about findability. Product information remains useful, but an organization’s expertise also needs to be discoverable.
The question is broader than whether someone can find a product page: can they understand how the business might help solve their problem?
7. Your Website Visitor May Already Be Well Informed
Sara points out a potential disconnect: someone has an informative conversation with a generative AI tool, then lands on a website that makes them start their research again.
John suggests considering that visitor as another persona, or an extension of an existing one. They may arrive with substantial background knowledge and expect a more developed conversation.
Website experiences need to account for that starting point alongside the needs of people encountering the business for the first time.
8. Expert Content Can Connect Problems With Products
John describes a company whose natural search traffic remained steady during 2024 and 2025 while surrounding businesses experienced declines.
He attributes that resilience to a substantial collection of expert content connected to relevant product categories. Visitors could learn about a problem and then understand which products might help.
The example illustrates the role of expertise in the customer experience. Useful explanations can help people make sense of their options, particularly when they lack specialist product knowledge.
9. Digital and Human Engagement Should Reinforce Each Other
John cautions against assuming that AI will replace relationships in B2B buying. These decisions are often complex, involve several stakeholders and depend on human interaction.
He sees an opportunity to make digital research and personal engagement support one another.
A buyer who has already learned about a problem online should be able to build on that understanding in a conversation. Preserving context helps the organization respond to where the buyer actually is.
10. Judgment Becomes More Valuable as Output Gets Easier
“One of the most important skills for users as well as for leaders in this is the skill of judgment.”
John notes that prompting is becoming easier while tools continue to change. AI can produce more content, analysis and recommendations, but people still need to decide what deserves their confidence.
Knowing when to accept an answer, question it or investigate further becomes essential. He connects that judgment with discovery: understanding the problem well enough to assess whether a proposed response helps solve it.
John’s Martech Hot Take
“The next unit of martech design really is the handoff.”
John challenges the tendency to optimize individual systems without examining what happens between them. Improving a CRM, marketing automation platform or content system may leave the underlying coordination problem unresolved.
His questions are practical. Does customer research reach the content team? Do campaign results reach sales with enough context to support a useful conversation?
A traditional handoff can transfer data while losing the intent and judgment behind it. John argues that organizations should examine those transitions before adding another platform.
That gives teams a concrete place to investigate performance problems: follow a piece of work across departments and check what the next person receives, understands and still needs to ask.
John’s Reading List
John references several researchers whose work informs his thinking about AI and organizational change.
Daron Acemoglu — John points to the MIT professor’s work when discussing meaningful augmentation and mediocre automation. The distinction helps frame questions about whether AI improves people’s ability to do valuable work.
Marco Iansiti and Karim Lakhani — John references their work on AI as an operating model change. That perspective supports his emphasis on examining how an organization functions when making the case for transformation.
Together, these references provide a starting point for thinking about AI through work design, human contribution and business outcomes.
Listen to the 10 Minute Martech Episode
Hear John’s full conversation with Sara about AI adoption, changing buyer expectations and the context that needs to travel between teams.
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Next Up in the 10 Minute Martech ICYMI Series
Harley Helmer, SEO Team Lead at Americaneagle.com, joins Sara Faatz to discuss tracking AI search visibility, building brand authority beyond your website and why marketers shouldn’t bet against Google.
Want to Keep Reading?
Here’s the full transcript so you can explore every insight from John’s conversation with Sara.
Sara: I’m Sara Faatz, and I lead community and awareness at Progress. This is 10 Minute Martech.
John: 84% of companies across industries are investing in some level of AI, but unfortunately only 14%, so one in seven companies, was taking the time and placing bets on investing in the humans that are operating with the tools and the capabilities.
It’s not that the martech stack is the constraint, it is the handoffs that we actually talked about.
Sara: That’s John Pehler, founder and principal of E3 Omni. Let’s get started.
Well, John, it’s awesome to have you here with us today. Thank you so much for joining. I just wanna dive right in. About a couple weeks ago, you had a post on LinkedIn where you were talking about humans plus AI pairs and context, and both losses and gains that come from that relationship. Can you talk a little bit more about where you were going with that?
John: Yeah, absolutely. I’m really excited about what we’re seeing from an AI perspective. But what I really see organizations struggle with is this recognition that everyone’s context is a little bit different from each other. And so you and I could actually both be working with the exact same version of an AI tool.
We can enter the same prompt, and we can get very different answers. And I think it’s important, especially as organizations are looking at this, Sara, to say, what information does the AI have? And then what did it utilize in order to produce that outcome? And then that outcome needs to be shared along with that context so that the next person’s AI in a workflow can understand the background then and the reasoning that it actually utilized in order to—
Otherwise, we see a significant amount of drift in terms of the effectiveness of the AI and its ability to solve real business problems.
Sara: Do you think organizations right now, especially on the martech side or in the marketing space, are they there yet? Are you seeing anybody that is actually operationalizing this approach to agents and human interaction?
John: So that is my actual favorite part of this world that we’re living in right now. It’s a recognition that it is about the operating model and it’s about the corresponding organizational design that needs to be in place in order to effectively utilize this. And it often comes down to people, process, and technology when we’re thinking through this, Sara.
But the operating model is the important piece of how work gets done within an organization, and how it travels across in support of the core business problems or the core business outcomes that organizations are trying to drive. There are a number of researchers that have done some phenomenal work in this area.
You’ve got Daron Acemoglu, I hope I say his name right. He’s an MIT professor, and his distinction is that you can’t just have meaningful augmentation.
John: The difference between that and mediocre automation is really useful for us to understand because it is really about how the operating model does the work.
And then I think Marco Iansiti and Karim Lakhani both did some really good work in helping us frame AI as that operating model change, which became the business case for the transformation that needs to occur, and it’s not a technology transformation, in my opinion, Sara. It is truly a cultural transformation.
Sara: Are you seeing any organizations that are doing that well today that you work with?
John: That was a good question that I kind of skipped through. I am seeing organizations that are doing it well, and believe it or not, it’s not just the big organizations that have been successful in a lot of other transformations.
What I see is that there are small to medium businesses that, the CEO or the president or the leader in an organization is taking ownership for making sure that they are going to be an AI-influenced organization. And I threw the influence in there because it’s not just about being an AI organization.
You don’t have to become a fully native organization in order to be able to leverage it. Let’s be influenced by it, and let’s make sure that organizations understand that it’s not just about the big picture things that need to happen, it’s small changes. Let’s make sure that our employees have tools.
Let’s make sure that we’ve got a governance model in place. Let’s make sure that we’re celebrating successes and taking those wins that we have, and helping people identify how they can replicate that change within their own workflow in order… Yes, I am seeing a number of organizations that are really successful, and I like the fact that it’s not just the big organizations, Sara.
Sara: How are you thinking about digital transformation? If we take a step back. I know you’ve been very involved in the Digital Momentum project with Flywheel as well.
John: Yeah, so when we started the Digital Momentum project, we wanted to make sure that we married primary research with some of the secondary research, and we wanted to make sure that we went across industries in order to understand what does it take in order to sustain the momentum in an organization so that it’s not just about moving at the same speed, that you can start to operate with that flywheel effect, right?
Where you start to operate faster and faster in order to produce greater outcomes. So we spent a significant amount of time doing surveys, having one-on-one conversations, having group conversations, and my favorite statistic from this was that 84% of companies across industries are investing in some level of AI.
But unfortunately, only 14%, so one in seven companies, was taking the time and placing bets on investing in the humans that are operating with the tools and the capabilities. And so when I think about that, it’s like only one in seven companies realizes that it is about the people and the process, and then it’s about the technology.
They’re starting with it’s another technology endeavor. And I think that’s very similar to the notion if you don’t start with an understanding of the problem, you’re going to potentially solve something that is not creating a burden for your customers, and therefore, is it really creating that value?
So the Digital Momentum project has been a great learning. We’ve got a ton of other data that we’re leveraging in order to inform across industries.
Sara: That’s really, really interesting. Where would you recommend if you have a small to medium sized business or even a large enterprise that says, “Yeah, I think you’re right, I need to upskill,” where do you start? What is that? And I’m not giving you any context, so maybe that’s not a fair question if we go back to context. No, that’s okay. But where would you start?
John: Well, I think we tend to discuss AI as a collection of tools or prompts or even data. Well, I think that when we look at buyers within the B2B space, I think buyers experience it very differently, right?
It’s changing how they discover information, it’s even changing how they frame questions, how they look at alternatives, and how they decide really which sources they wanna trust. And so when I think about it, for example, at a marketing perspective, right? Marketers are typically optimized for someone typing in keywords into a search engine or clicking through to a website, but the AI system sits kind of between the buyer as well as the brand or the potential problem solver for them, right?
It can interpret the questions, it can evaluate the available information and produce an answer. Well, I think sometimes what we’re finding out, at least aspirationally, is that the buyer’s not visiting the company website when they’re doing research, but yet they’re still doing research online.
So I think that changes the meaning of findability and how we need to think as organizations about how to marry not just the products that we sell, but the expertise that we bring. Now, I don’t wanna overcorrect and assume that AI is gonna replace that relationship. I don’t actually believe that to be true.
I think the B2B buying process, because it’s so complex, Sara, right, and it’s got multiple stakeholders and it’s relationship dependent, I think the opportunities to make the digital and human engagement reinforce each other, and so that’s where I would start.
Sara: I definitely agree that I think more people are doing their research off of your property.
But doesn’t that also change the journey when they land there? Because right now all of our journeys are designed two ways. One, they’re designed for discoverability, and if you’ve come here and you don’t know who we are, you have our homepage, and this is our front store and all of those things. But on top of that, they’ve just gone through an experience with a generative AI tool typically, or where they ask a question and they get a real answer and usually citable, and they come to your site and it’s like being transported back in time because now you’re starting from scratch, you’re at the front door, and you now have to select menus and you have to understand the navigation.
I believe, and I’m curious what your thoughts are, that there is an opportunity for us to start rethinking that digital experience so that it works both for the person who does happen upon your site for the first time and needs to research on the site, but more importantly, the person who is coming in at a very, very different entry point [00:09:00] from the journey, and they’re gonna want an experience that is far more modern than drop-down menus and navs and all of that.
John: I agree with you, Sara, and I think that there’s an opportunity for us to consider that as another persona, or maybe it’s an extension of an existing persona that we have. Many organizations are doing well just to be able to have a website that allows you to transact, and I think about that as, oh, great, so they’ve got products, they’ve got…
But what about the expertise? What is the unique value proposition that they bring to that? And I believe that you need to start thinking about the way that some of the advanced personas maybe is a way to think about it, and that’s not an insult. It’s more of, say, they’re users that have done a lot of research, and they’re expecting that same level of information and that same experience on your website when they land, and you need to be able to start telling those stories.
I had a company I was working with that when all the other businesses around were actually seeing significant drops in their traffic, we’re actually seeing it maintain, and this goes back from all of '24 and all of '25. They saw a maintaining level of natural search, traffic coming to their websites.
And when we really dug into it, it’s because they had a robust set of expert content, right, on their website that sat in front of majority of products, and then it did a really good job of actually connecting, not just to, right, a specific product, but into a category of products that can be used in order to solve those problems.
And that, for me, is what—A—is gonna drive great traffic, but it’s also an experience that someone is gonna come, especially when they’re not the expert, they’re not always the expert on the products.
Sara: Since somebody even advanced, on that advanced journey, which I think is one of the things you’ve also touched on, right? [00:11:00] It’s not just it’s the subject matter expertise, but it’s also having that, making that accessible in a different way, which is really an interesting time where all of these key and core principles that we live by are all kind of merging together. And you’re starting to see the world is evolving in a really interesting way, and it’s exciting to me to think about—
John: I would agree, and I think what’s really nice is that it’s also reinforcing some of the skills and capabilities that have always been important, and maybe even amplifying them.
And I think one of the most important skills for users as well as for leaders in this is the skill of judgment, right? Because prompting’s become easier over the course of time. The tools that we’ve used, many of them have changed. Some of them are still emerging on a day-to-day basis as we’re in the marketplace.
So I think that AI capabilities are gonna help us produce more content, additional analysis, as well as great recommendations and a path forward. And when we use it as a thought partner, right, that judgment of when to accept it and when not to is really, really valuable. And so I think that our great skills around discovery is how we actually develop that judgment and how we actually put it in place to solve problems for our business.
Sara: Love that. Love that. Do you have a martech hot take?
John: Yeah, I do have a martech hot take, and when I think about this, it’s not that the martech stack is the constraint, it is the handoffs that we actually talked about. So most companies are optimizing for individual tools, right?
They think about, “Hey, let’s improve the CRM,” or the marketing automation platform, or even the content system, the analytics environment. But the customer experience is within the spaces between those systems, right? And so what I’m always gonna challenge people in is does the insight from the research, does it reach the content development team?
Does the campaign response and the results reach sales with enough context to support that useful conversation, right? That traditional handoff that transfers, right, the document or the data, it loses the intent, the judgment, and the context they produce. And so that’s why I believe the next unit of martech design really is the handoff. When work moves from one person or team or system to another, does that necessary context move with it? And companies that are fixing those transitions before adding the platform, those are the ones that are demonstrating success on a regular basis, and that’s the common theme that I see.
Sara: I love it. I love it. Well, John, thank you so much. I really appreciate your time today.
John: Sara, thank you very much for having me, and I wish you incredible success.
Sara: Listeners, thanks for tuning in. Make sure you like and subscribe wherever you get your podcasts. Until next time, I’m Sara Faatz, and this is 10 Minute Martech.
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