Your first AI app is not a moat. It's a lever

The venture‑capital echo chamber is a simple place. When every deck shows the same hockey stick, founders reach for any advantage that looks like defensible ground. Enter the mantra du jour: “speed is the only moat.” In a world where open‑source models compress product cycles and the barriers to entry are low, there is some truth to the idea. Yet truisms become dangerous when they slip into corporate lexicon unchecked. 

 A few high‑profile investors have declared that whoever ships fastest wins, and vendors have turned this into a marketing cudgel. Enterprise buyers, already anxious about falling behind, are now being told that any delay in adopting generative AI means irrelevance. The result is a rush toward the wrong layer of the AI stack: organisations pile on agent‑powered chatbots and point solutions while their data remains fragmented. Instead of building a moat, they are digging a moat around sand.

The three layers of enterprise AI

Madrona describes three defining layers in the agent infrastructure stack – tools, data and orchestration. Tools (or applications) sit at the top, data is at the bottom, and orchestration binds them together. 

But why is there a rush to do "POCs"?

Here is the blunt reality: An enterprise selling fasteners neither knows data lakes nor has the team to get their hands around it. An app is intuitive for the board to understand. If data cleanliness is hard to comprehend, we can safely assume that orchestration is just black magic.

In other words, mid-markets don't know what they don't know. AI companies sell what they can sell easily.

Why app‑first AI hurts

The “speed as moat” argument flattens nuance. It encourages enterprises to adopt off‑the‑shelf AI applications before they have a mature data foundation. Data silos become the predictable outcome. In the context of generative AI, these silos are like compartments where the most interesting insights remain locked away. 

When a team buys a chatbot for customer support, another buys a marketing assistant and a third uses an agent to summarise contracts, the enterprise ends up with three different data islands and inconsistent definitions. The immediate effect is confusion: duplicate code, conflicting customer records, inconsistent metrics. The longer‑term consequence is strategic paralysis. Decision makers cannot trust the output because the underlying data is fragmented. This problem compounds quickly. 

Let's talk in process terms. Input determines the output quality. If a line item in an invoice need to be fixed upstream in a separate CPQ system but all the AI from "Invoice AI" vendor can do is flag the issue, the work moves from the agentic workflow to human-run emails. Even if the SKU is fixed in the CPQ system, the "why" behind it is lost. Now imagine delays when the "accounts payables" team on the other side sees that the line items are different between successive invoices but no explanation to back it. This is how work breaks.

Speed at the app layer simply shifts and throws bottleneck grenades outside its perimeter. As AI tools proliferate, each becomes a new interface to maintain and a new repository to govern. The initial “speed” is erased by the drag of integration and compliance. The engineering team spends its time stitching APIs together rather than improving models. The compliance team wrestles with audit trails from dozens of point solutions rather than building a unified governance framework. Customers see inconsistent experiences because one agent has no memory of what another agent promised. At some point, the enterprise becomes a collage of AI demos with no cohesive intelligence.

The boring work that matters

Cleaning data and building pipelines is unglamorous, and few executives want to champion a project that looks like plumbing. Yet this is where the real moat lives. Enterprises that invest early in data cleaning and cataloging can iterate later without being constrained by missing or conflicting information.

I know this is not the first time someone is saying this. I know you are not incapable. It is the "how" that matters here. Cataloging data, cleaning it, building ETL pipes, and data models is where a mid market data transformation stalls. If I may toot our own horns here, we know the answer is not a $250K data transformation project. We have an automated system that does all of these in half the cost and one-third of the time.

It's a bad use of dollars to build apps because data is hard. Champions of progress inside an enterprise need better guidance and support than that. Moative's perspective is that it should take the same time to clean the data as it takes to launch an app. Speed is moat but at the data layer. We have a way for it.

The second layer, orchestration, is equally overlooked. AI agent orchestration is “the process of coordinating multiple specialized AI agents within a unified system. When enterprises own orchestration, they ensure that the AI system remains consistent with their processes, policies and governance. Orchestration becomes the conductor of a symphony of specialised models rather than a patchwork of soloists.

In simple terms, orchestration reduces waste. How many PDF data extractors does an enterprise need? One. How many do they get? Fifteen. Why? Every AI system in an enterprise needs to read documents. Is an app vendor going to build it or ask you to bring your own PDF data extractor?

So at some level are apps wasteful by definition? Yes, that is the world we are moving towards, if your house is not making rockets or using AI to clean the inside of boilers.

It is in the orchestration layer that speed truly compounds. Once data pipelines are robust and agents can call one another’s skills, new applications become mere configurations. The marketing team can spin up a campaign bot using the same underlying customer data that drives the support bot. The finance team can call upon the risk‑scoring agent to validate a loan decision without building another model from scratch. Each new application reuses the same data foundation and orchestrated skills, reducing marginal development time. This is the type of speed that becomes a moat: not because you shipped a demo overnight, but because you can reliably assemble new workflows without accruing additional complexity.

Using quick wins to build moats

Moderation is key. 

Enterprises need quick wins to build trust and secure budget. It is acceptable – even advisable – to deploy a point solution that demonstrates value. Moative’s own field work shows that an AI pilot should prioritize showing that it works, then proving reliability, then embedding governance. Early demos can use synthetic data to accelerate buy‑in. But those wins must be reinvested into the lower layers. The pilot is not the moat; it is the lever that persuades stakeholders to fund the data cleanup and orchestration that follow.

To make this concrete, imagine a utility company that deploys an AI‑driven billing assistant. The team might build a prototype quickly using a generative model and synthetic data. Customers love it, and call centre load drops. At this point, “speed” has served its purpose. If the company stops there, it will end up with a single bot and a new data silo. Instead, it should use the goodwill to invest in integrating the billing data with other systems, cleaning the underlying customer records, and building an orchestration layer that allows the same agent to answer outage queries or create payment plans. Over time, the assistant becomes one of several applications built on the same foundation.

Speed as outcome, not input

The seductive simplicity of “speed is the only moat” masks a deeper truth. In AI, speed is not a moat; it is an outcome. Enterprises that go slow on data and orchestration will be forced to scramble later, spending millions to reconcile duplicated schemas and manage integration failures. Enterprises that build a strong data layer and orchestrate agentic skills can move quickly when it matters. Speed then emerges from a disciplined sequence: show value quickly, invest in foundations, reuse the foundation across many applications, and accelerate without sacrificing trust. The layer‑cake metaphor captures this sequence. Build on data, insert orchestration, and the application layer becomes a thin glaze at the top – the satisfying icing rather than the entire cake.

If you replace data and orchestration with slides and demos, you may feel fast today. The antidote is to own your data, coordinate your agents, and treat applications as configurations. In that world, you will find that speed is no longer a trick; it is the natural consequence of doing the boring work well.

Enjoy your Sunday!