Everyone loves a great interface: software that’s easy to use and helps you get things done. I should know, I made a career designing them. For a very long time, the value of a system was realized at the point of human-computer interaction. If no one could use the software, what was the point?
In the intelligence age, thinking in terms of application and human interface is killing your potential. Harnessing AI’s potential means focusing on the inner layer, the data layer:
- The cost of creating user interfaces is collapsing.
- Your system’s intelligence is only as good as the data thats feed it.
- AI agents will do most of the work
But that’s just the basics. The value is coming from self-improving factors a solid data foundation gives you:
- Lower software and license costs.
- Connected data
- Richer data
- Accelerated action cycles
The organizations ready to thrive in the AI era are the ones investing in their data layer today. When AI agents can act on your behalf and build interfaces on demand, your connected data layer becomes the operational asset that unlocks everything else.
The user interface is dead, long live the user interface!
For those who haven't experienced it yet, creating apps is now incredibly easy. A senior developer using tools like Cursor and Claude Code can build one in a few hours. It may not scale well or be easy to maintain, but it gets the job done. This changes the whole game; even designers like me now prototype working software as we design.
User interfaces don’t need to be maintainable. Think of them as throw-away code. If you need to interact with something, generate a UI for it, and move on.
This already impacts creation and marketing workflows. Want to create a new page by yourself? Create a page builder; it will only take a minute.
Scalability is another matter, and it comes down to data. A throwaway interface doesn’t need to be scalable, but the underlying data layer it accesses does. In fact, the more reliable and secure your data layer is, the more secure and scalable all those throwaway interfaces will be.
AI is only as good as its data layer.
Large language models like ChatGPT rely entirely on the information we give them. When context is missing, they hallucinate, which makes LLM poorly suited for enterprise use cases… Unless you provide LLMs with a strong data layer that is.
By building a foundation of reliable, well-organized data, you give AI systems what they need to deliver grounded results. Good data makes AI agents, chatbots, personalized experiences, and automation reliable, safe, and therefore valuable.
It all starts underground. The deeper your data roots, the higher your AI can grow.
Agent to Agent is the pathway to explosive productivity
Agent-to-agent interaction is where most automation will happen. AI agents transform user requests into task plans, execute or delegate tasks, and monitor progress as they go. AI agents are like users, but without eyes and hands. They can’t navigate visual interfaces with a mouse and keyboard, so they need their own way to access the data.
Imagine you’re launching a new product. Today, you would spend hours pulling specs from the PIM, updating product pages in the CMS and the knowledge base, and coordinating approvals with a dozen stakeholders. With agent-to-agent automation, you define the goal, and agents handle execution. A content agent drafts the page, and a support agent updates the knowledge base. A personal assistant agent drafts all the emails and coordinates the review. It won’t be perfect, but it can cut the tedious work in half.
Giving agents direct access to your tools is what makes the team more productive. It means each of us can focus on setting priorities and making decisions. We’re only at the beginning of understanding what that means.
Time to reassess your toolkit
Martech stacks grow organically. Someone buys email automation. Another team adds social tools, and so forth. Most of those tools were selected for their ease of adoption and implementation. With user-friendly interfaces and single-minded use cases. You can see where this is going. What made them valuable in the past is what made them a liability today.
The cost of building interfaces on demand is going to zero, and the opportunity cost of not having your own AI agents is only growing. So the role of legacy MarTech is changing.
The best way to see this is to treat your data layer as the durable, reusable part of your toolkit. The interfaces are the cherry on top, the replaceable stuff. Once you see it this way, you start to look at the platform you’ve built workflows around for years differently. Does it provide more value than storing your data and showing it back to you?
Moving forward, we will build composable systems with clear separation between data and UI. Selection criteria like data residency, reverse ETL support, and Bring Your Own Cloud will only grow in importance.
The companies with the cleanest data layer will move the fastest. This doesn’t mean ripping everything out. But applications and external systems need to stop being the source of truth. Data becomes powerful when it’s in one place and separated from the tools that use it.
Bring every silo into one place through shared meaning
Even when data lives in one place, it often speaks different languages.
Consider someone who downloads your whitepaper. Marketing calls them a lead. When they book a demo, Sales creates a prospect, and Customer Success opens an account. Same person, three systems, three definitions. The data doesn’t share meaning, so you hit a wall when trying combine all this into a single customer view.
The solution is an ontology, a shared set of definitions that explains what your terms mean and how they relate. It’s like onboarding a new teammate. You don’t hand them database tables and expect them to figure it out. You sit down and explain what’s what. Even smart people make bad decisions without good context.
With an ontology, AI systems can understand how your business works.
- Translate across systems. Finance and ops see the same revenue numbers.
- Query naturally. Find out why EMEA margins dropped without being a SQL guru.
- Find related concepts. Search for customer complaints and surface support escalations as well.
Bringing data together shows clearer signals through the noise. Correlation shows the structure behind the data, what relates to what, and how. It turns loose information into a coherent picture and shows you the shape of things.
The unstructured goldmine hiding in plain sight
Most marketing analytics focus on structured data (clicks, conversions, revenue, etc.) It’s the bread and butter of analytics, how we measure performance. This is what most people would identify as data, but this is only a tiny sliver of what data is.
Structured data is great for telling you what happened, but it rarely explains why. The why lives in unstructured data, the messy, hard-to-quantify information that organizations generate constantly but analyze rarely.
This information is the good stuff. Like a customer explaining in their own words why they almost canceled. A sales rep who describes the objection that keeps coming up. Or a social media thread where your target audience debates your category.
Extracting insight from unstructured data used to be expensive and required large teams of expert. So most organizations have given up on it. The ROI just didn’t justify the effort.
AI has changed the economics of unstructured data.
Large language models can read, summarize, and spot patterns across text at a scale that wasn’t realistic a couple of years ago. Ten of thousands of unstructured data points that once took months to comb through can now be processed in hours.
Magic happens when those new data sources connect to your structured data. More data, richer data, means better correlation. Familiar data sources become much more valuable resulting in a steady stream of insight you couldn’t access before.
Super fast decision cycle
How long does it take your organization to move from data capture to action?
Discarding tactical incremental updates, from finding out that something isn’t working, understanding why, and deploying the change.
For most, the honest answer is months. A dashboard flags a problem. Meetings happen. Analysts pull data. Researchers dig into the why. Decks get built, presented, and debated. Then you pitch for budget approval for weeks on end.
What’s frustrating is that it isn’t a people problem. Smart, capable professionals staff every step of this process. The time it takes to transform data into action exceeds the shelf life of the insight.
Human judgment matters. The job is clearing everything that slows it down. The key is to automate the steps between signal and decision so the right information reaches the right person at the right time, with clear options for what to do next.
This takes connected data, shared meaning, and AI agents that can move across your entire data, but it also needs a change in mindset. The bottleneck is usually approval workflows built for a world where changes were expensive and hard to reverse. We’re not in that world anymore. You can test, learn, and iterate in hours. The old safeguards have become a constraint.
Before you know it, steps that used to take a week take hours. What once took a quarter takes days. Each cycle teaches you something. Each lesson feeds the next decision. The gap between knowing and doing disappears, and what’s left is pure forward motion.
Conclusion
Value used to live at the surface. That made sense when building interfaces was expensive to build and maintain, but the cost of creating interfaces is collapsing. Everyone can create a throw-away app in an instant. The value lies underneath the surface.
AI made this inevitable. These systems are only as good as what you feed them. Clean, connected, well-structured data gives you intelligence you can trust. And as agents take on more of the work, they need clear pathways to the data.
This is why the MarTech stack everyone built over the years is now working against you. Tools chosen for friendly interfaces and quick adoption often lock your data away in silos. The very things that made them easy to buy make them hard to connect. When you add shared meaning through an ontology and unlock unstructured data, the potential just keeps expanding.
You don’t need to tear everything down tomorrow. But it’s worth looking at your systems with fresh eyes. What’s durable and composable? What improved the data layer instead of working against it?
Once you start asking those questions, things click into place. What felt like a tangle of tools and workflows is starting to look like raw material for something much better.
Your most valuable asset has been there all along, beneath the surface. It’s time to build on it.
Start with the data layer.
