Build vs. Buy vs. Orchestrate: The Real AI Capability Decision

When organizations start looking at AI, the conversation often starts with the classic build vs buy AI question:

“Should we build it or buy it?”

From the discovery conversations I have been part of, I think that question is becoming too narrow.

Today, organizations have a third option: orchestrate.

The real question is:

What should we build, what should we buy, and what should we orchestrate?

Start with the problem, not the technology

One of the biggest mistakes I see in early AI discussions is starting with technology.

Which LLM should we use?
Should we build an AI agent?
Which AI platform should we buy?

These are important questions, but they should come later.

A good discovery starts with the business problem, the current process, the users, the data and the desired outcome.

Only then should we decide what technology approach makes sense.

Build when it is your differentiator

Build makes sense when the capability is strategic, highly customized or closely connected to proprietary processes and data.

But just because something can be built doesn’t mean it should be built.

Building also means owning the engineering, security, infrastructure, model management, monitoring and ongoing maintenance.

The better question is:

“Is this a capability we want to own?”

Buy when the problem is already solved

There are many capabilities that organizations don’t need to reinvent.

Enterprise search, document processing, customer service automation, code assistance and other common AI capabilities can often be purchased.

But buying a product is not the end of the decision.

During discovery, we need to understand how it fits into the existing ecosystem, how data is handled, how it integrates with current applications and what happens when the business needs something the product doesn’t support.

A great product in isolation may not always be the right enterprise solution.

Orchestrate when the answer is somewhere in between

This is where I believe the AI conversation is heading.

An enterprise may use a commercial AI product, a cloud-based model, its own business rules, proprietary data and internal applications — all as part of the same business process.

The value comes from connecting these capabilities.

That is orchestration.

In many cases, the right answer is:

Buy the commodity. Build the differentiator. Orchestrate the experience.

This is where discovery adds real value

From an Advisory perspective, discovery is not just about gathering requirements.

It is about helping the business make the right decisions before significant technology investment begins.

For an AI initiative, I would typically want to answer:

1. What problem are we solving?
2. What already exists?
3. What should we own?
4. What should we consume?
5. What needs to be connected?

The output should be more than a technology recommendation.

It should provide a clear future-state capability, architecture and roadmap.

Don’t choose the technology too early

One of the biggest risks is selecting an AI product first and then trying to find a problem for it.

The better approach is the opposite.

Understand the business problem.

Assess the current state.

Understand the data and technology landscape.

Define the desired outcome.

Then make the Build, Buy or Orchestrate decision.

Because ultimately, this isn’t just a technology decision.

It is a business transformation decision.

The organizations that get the most value from AI won’t necessarily be the ones that build the most AI.

They will be the ones that make the right decisions about what to build, what to buy and what to orchestrate.

And that decision starts with a good discovery.