Shannon Ryan was one of the experts featured in the Contentful × Atlantic Insights whitepaper, When machines make marketers more human. This global study, which is based on surveys of 425 marketing decision-makers across different industries, company sizes and regions, explores how leading marketers are integrating AI into their core operations.
In this extended interview, we revisit Shannon’s original contributions to that report and expand on the ideas that continue to shape Valtech’s work — from bridging the execution gap to designing truly human-centered digital experiences.
The experience era
In an AI world where anyone can create unlimited, untethered content for any audience at scale, it’s difficult to believe your content is inherently more special than your competitor’s. The real differentiator will be the experience and engagement you craft around it.
— Shannon Ryan, When Machines Make Marketers More Human
Q: Why is that idea — experience over content volume — relevant right now?
Content creation will always matter. You still need to produce great stories, images and campaigns. But the idea that content alone differentiates you? I think that’s outdated. What matters now is the experience your customers, partners, employees and prospects are having around that content.
When a technology or process becomes ubiquitous, it loses its ability to be a differentiator. Everyone can produce content now. AI tools have made that part faster, cheaper and more accessible. So, if you’re betting your website copy or blog posts are what make you special, you’re probably losing sight of what actually drives differentiation.
We’re not writing novels. We’re trying to communicate clearly, to persuade quickly, to create connection and momentum.
When you break it down, experience is where you can truly compete — more than through technology or the sheer volume of content. That's the mindset shift we’re helping companies make right now: from focusing on output to designing meaningful, measurable experiences around it.
From data to action
We’ve been talking for decades about evolving from data to knowledge to action. AI can finally make that leap — interpreting the data, testing multiple variations, and picking the winner faster than any human team could.
— Shannon Ryan, When Machines Make Marketers More Human
Q: Can you explain how AI is connected to creativity and marketing maturity?
We’ve been sitting on mountains of data for years. But moving from insight to action has always been the bottleneck. AI can now interpret the data, run variations and pick winners faster than a human team could.
But that doesn’t mean creativity goes away. Quite the opposite. It becomes more important.
AI can give you the patterns, but it can’t tell you which idea feels right. It can’t sense when something’s off or when something really resonates with a human being. That’s still the human part.
I think of AI as a creative amplifier. It speeds things up, gives feedback and lets teams experiment faster. It frees humans from repetitive production so they can focus on higher-value ideas.
The collaboration between human creativity and AI capability is where the really interesting stuff happens. The trick is teaching creative teams to see AI as a partner, not a threat.
Closing the AI execution gap
Q: The whitepaper talks about the “execution gap.” Many organizations are investing in AI but haven’t yet realized its full potential. How does Valtech help clients bridge that gap?
This one’s always a bit delicate, because I never want to sound like I’m saying, “Slow down.” I’m not suggesting people should pump the brakes on AI. But I do think there’s a tendency to chase a kind of unicorn ROI with every initiative. And that’s not realistic.
If you’re genuinely trying to innovate, you have to expect that some things are going to fail. That’s the nature of innovation. You don’t get everything right, and that’s okay.
What we try to do at Valtech when we’re helping a client is to make sure the work starts from a meaningful place. Are you trying to solve a real problem? Have you identified it? Can you articulate it in a way that’s measurable? Because that gives you something tangible to test against.
When you start from a clearly defined problem, you can measure progress. You can quantify success or failure. That’s a much healthier way to build an AI roadmap than chasing whatever shiny new capability just dropped last week.
Many AI initiatives stall at the proof-of-concept stage because they’re not anchored to a clear, tangible outcome. To move forward, organizations need to focus on execution that’s measurable, scalable and aligned with the user experience — not just the technology. You can have the most advanced model in the world, but if people don’t trust it or can’t use it effectively, it won’t deliver real value.
Q: Many clients come to Valtech saying they want to “do something with AI,” but they don’t yet have buy-in across their organization. How do you help them navigate that?
That’s honestly one of the best situations we can be in. When a client comes to us and says, “Can you help us think about this?” instead of “Can you help us do this?” — that’s where the work gets interesting.
The “do this” scenario usually means they’ve already decided on a solution. It’s narrow. It presupposes the answer. The “think about this” scenario lets us be curious together. It gives us space to ask, “What’s the real problem?”
And quite often, what they believe is the issue isn’t actually the root cause. They might come to us saying, “We need a better PDP page.” But when we start asking questions, we discover that the real problem is seven steps upstream — in how they’re positioning themselves in the market, who their target audience really is and what kind of growth they’re chasing.
Are they trying to steal market share? Grow it? Reach a new segment? Those questions shape everything that follows, long before we ever talk about page design or AI tools.
So, when clients come to us without a fully formed brief, just an intent or a challenge, that’s ideal. It lets us engage at a strategic level, where we can bring curiosity, data and creativity together to find the right problem to solve.
Integration and trust
Q: The whitepaper calls this stage “integration fatigue.” What’s your take on that?
I think that’s true to a point, but maybe not in the way people mean it. Everyone’s chasing this idea of seamlessness, trying to make dozens of systems talk to each other perfectly. But that can create its own kind of complexity.
My hunch is that we’re going to see a bit of pushback against the idea that everything has to connect to everything. As people learn to trust AI more, we’ll see more discrete applications doing discrete workloads.
Sometimes it’s better to have a focused solution that does one thing exceptionally well, rather than an elaborate ecosystem that’s stitched together so tightly it becomes fragile.
Take agentic shopping, for example. People love the idea of an AI agent that can find you three pairs of shoes in your size, predict which one you’ll like and then, because it’s connected to your bank account, buy it for you.
It’s clever, but there are a few leaps of trust in there. When we’re talking about real money, real data and real identity, most people aren’t ready for a world where AI makes those decisions automatically.
So, I think we’ll live in this in-between stage for a while. Where we continue integrating, but we keep a human in the loop for oversight and trust. Sometimes friction is healthy. It’s where confidence and control live.
The road to personalization
Q: What does the journey from using AI for content creation to achieving true personalization look like?
Let me paint you a picture of Nirvana:
True one-to-one marketing. One piece of content created for one specific person.
If you go back in time, when we printed direct mail, you made one piece and hoped you got it right. Then, when the web came along, we started creating personas — six to ten types of users — and that took us from one-to-many to one-to-few.
Now, AI brings us to the edge of true one-to-one. You start with the key messages you want every potential customer to understand, and the AI tailors those messages to each individual based on what it knows about them — their past interactions, their preferences, their prompts.
That’s the holy grail: true personalization at scale.
But it also means organizations need to get their content house in order. You need a content strategy that’s structured and intentional, where every asset has a clear purpose and a defined audience.
Too often, we see content management systems full of pages that exist just because they’ve always been there. “Who we are,” “What we do,” “Our clients.” No one stops to ask why. AI is going to force that reflection. Why are we creating this content? Who is it for? What do we expect it to do?
That’s going to be a good thing. It’ll make companies think more deeply about their storytelling and structure.
Breaking barriers: People, process, technology
Q: What are the biggest hurdles to achieving that kind of personalization maturity?
- People: That’s the hardest one. True marketing strategy, the kind that connects the purpose of the content to the needs of the audience, is hard work. It takes curiosity, empathy and critical thinking. Those are human skills that can’t be automated.
- Technology. This part has historically been the bottleneck. Creating content, tagging it properly, setting up the logic so the right thing appears at the right time — that’s not easy, especially at scale.
- Process. Even when you have the right people and the right tech, you need a rhythm that keeps things moving. A flywheel where you create, publish, learn, refine and repeat. That’s hard because technology is always changing. You finally get good at one system, and then four years later, the platform changes.
None of this is as easy as people make it sound. But if you build processes that can adapt, you’ll stay ahead.
Rethinking the front door of the brand
As your homepage stops being the front door to your brand, you need to amplify your presence inside communities — where your customers are already talking — so they become the voice of your brand.
— Shannon Ryan, When Machines Make Marketers More Human
Q: What does that shift mean for brands today?
For a long time, marketing has been about control: control the message, control the brand, control the experience. But we’re entering an age where you have to get comfortable letting go of some of that control.
You can’t control how a large language model ingests your content, reframes it and sends it back to a user. That’s unsettling for a lot of CMOs. But it doesn’t have to be.
The most authentic version of your brand is still what people say about you when you’re not in the room — in communities, in conversations, in real life.
When you go out for a run and realize everyone around you is wearing Nike, that’s brand storytelling. That’s authenticity.
Websites are still important, but they’re not the front door anymore. The front door is wherever your customers are already talking — social channels, online communities, peer groups. The opportunity is to amplify your presence there, not try to drag everyone back to your homepage.
A more human future
Q: Looking ahead, what gives you optimism about the future of AI and marketing?
What excites me is that we’re finally seeing marketing become more human again, not less.
AI is taking away a lot of the manual work, the production, the repetition, and freeing people up to focus on creativity, empathy and strategy.
That’s the real promise here. It’s not about replacing people. It’s about elevating what they do best.
If we can use AI to remove friction and give people more space to think, experiment and connect, then we’re not losing control of the craft — we’re rediscovering it.
Final thought
AI should make humans more human — more curious, more creative, and more capable of delivering experiences that matter.
— Shannon Ryan
Care to continue the conversation? Contact us to schedule a call with Shannon.
