Build AND Buy: The Enterprise Agentic AI Strategy That Actually Works

TL;DR: The build-vs-buy AI debate isn’t actually an either/or choice. Winning enterprises buy the infrastructure that takes a decade to build (verified brand data, competitive intelligence, direct publisher distribution) and then build differentiated agentic workflows on top of it, connecting their LLM of choice through Yext MCP.

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In the AI era, most marketing leaders have two big questions.

The first is “how is our brand showing up in AI search?”, because that's the one their CEO or board is asking. The second then becomes, “what can we do about it?”

That's where most teams get stuck, in part because they frame what comes next as a binary: build the technology solution ourselves or buy a platform.

But the notion of “build versus buy” forces a false trade-off between control and capability. It keeps marketing and IT fighting for the same budget instead of building something together. And as AI has changed both the marketing world and the software landscape, the solution is no longer about picking a side. Instead, it's about choosing which layer to buy and what to build with it.

Notably, even “what to build” doesn’t look how it used to: there’s now the possibility to combine toolsets in a way that up until very recently, was not possible without Model Context Protocol (MCPs).

Why a build-only approach stalls at the data layer

Let’s start with the big picture.

The tools most marketers are using were built for a world where humans manage every step of the workflow: diagnosing the problems, choosing the strategies, and measuring results — then lather, rinse, repeat.

But internal AI projects built on that same model often have the same problem: the data layer.

When an organization attempts to deploy AI agents without the right data underneath, it can easily erode brand authority; an AI agent reasoning and acting from disparate sources or unverified web data will make mistakes. But the infrastructure required to do it right— including integrating verified brand data at scale, competitive intelligence across AI models, and direct publisher relationships — takes a decade to build. Not a budget cycle.

In other words, building the right infrastructure independently is too time-consuming and expensive for the vast majority of brands. And yet cutting corners isn’t an option — unless you want to allow AI to work with customers and take action on risky, unverified data.

Why buy-only strands your AI investments

That said, the “buy-only” trap for brands is just as real.

Gartner research has named one key problem directly: agent washing. They estimated that only about 130 of the thousands of agentic AI vendors in the market today are “real” — and the rest are vendors slapping an "AI" label on the same dashboards and automation tools that have existed for a while. This relabeling of existing automation as agents (without changing the underlying architecture) has made buy-side skepticism rational.

But even a legitimate agentic marketing platform can carry a hidden cost. You end up with another tool your team has to log into, pull data from, and manually carry into every other system they use. You open a dashboard to answer one question, and three more come up that it can't answer. Meanwhile, the LLMs your company has already invested in — Claude, ChatGPT, whatever you're using internally — can't see any of that data. It's trapped inside the platform you just paid for.

That's the real question underneath build vs. buy: if a dashboard is providing the analysis and your LLM is the one making recommendations and talking to your team, what does the software (and the data that powers it) need to look like?

It’s not about buying another dashboard. It's getting the right data layer that both your agents and your LLM can actually talk to.

Build AND buy: the architecture that connects your LLM to Yext’s data layer

The answer to the “build vs. buy” debate is, well, both: buy the infrastructure that took a decade to build, and then build your differentiated workflows on top of it.

Buy the infrastructure. For example, Scout is the data foundation for Yext: 10 billion competitive signals, 12 million locations tracked, 150 visibility drivers, citations monitored across ChatGPT, Gemini, Perplexity, and Claude — plus a verified Knowledge Graph and direct distribution to 200+ publishers with no aggregator in the middle.

Then, build the workflows through an MCP. MCP (model context protocol) is an open standard that lets an LLM connect directly to different data sources, so you can find real insights from combined data instead of copying, pasting, and switching between tools. It also makes it easy to connect your company's own LLM instance, like an internal deployment of Claude, to other internal or external data sets. That combination can surface opportunities that might have taken years to find, or act on, before.

That's why we launched Yext MCP: connect your LLM of choice (Claude, ChatGPT, Gemini, and more) directly to your CRM, demographic data, POS signals, hyperlocal competitive intelligence, or any other data source, all in one place your model can query.

Without MCP, a question like this means pulling data by hand or waiting on an engineering team to wire up a custom integration for every source. With Yext MCP, a marketer can open their own window of Claude or ChatGPT, ask "Where are we losing to [competitor] in [this location] in AI search, and what should we fix first?" and the model answers on its own, no pipeline required. It queries Scout's competitive data and recent citation activity in real time. It can then route the recommended actions directly to Action Center for a local team to execute. The difference isn't a chatbot with a sharper prompt: it's an LLM with a standing connection to your verified data.

This is what makes Yext a true enterprise agentic marketing platform: your model, our data, your workflow. It gives you the ability to combine the best of ‘building’ custom workflows with ‘buying’ the right foundation to get you where you need to go — faster.

The transformation investment your board actually wants to see

Software is evolving from a destination where work happens into a background layer where work is already done. So, the question your leadership should be asking isn't really "build or buy" — it's whether you're building something durable.

Build and buy gives you that answer. You're buying infrastructure your competitors would need a decade to replicate, and you're building differentiated workflows on top of it — with your data, your models, and your governance in place. That's the transformation investment worth reporting.

Get access to Yext MCP or watch our agentic AI vision session on demand.