For the past year, organizations have been asking whether they should build their own AI capabilities or buy them from technology providers.

It’s become a familiar debate, one often framed as a measure of innovation. If you’re building proprietary AI, you’re leading. If you’re buying, you’re somehow behind. I don’t think that’s the right question.

For Chief Data Officers (CDOs) and other data leaders at the center of this debate, the more important question is whether building creates a real advantage for their business and whether that advantage is worth the responsibility that comes with it.

Innovation isn’t defined by how much technology you own. It’s defined by knowing where customization creates value and where it doesn’t.

Getting that distinction right can determine whether AI investments become long-term assets or expensive distractions.

AI Is Becoming Infrastructure

We’re reaching a point where many AI capabilities are becoming infrastructure.

Drafting assistants. Productivity tools. Knowledge search. These capabilities are increasingly embedded in the software organizations already use every day. They improve constantly, benefit from enormous investment by technology providers, and become more capable without individual businesses having to maintain them. That’s an important shift.

When a capability is broadly available, continuously improving, and relatively inexpensive to adopt, building your own version rarely creates a lasting competitive advantage.

The pace of AI development also makes this increasingly difficult.

A model that feels state of the art today may be surpassed in a matter of weeks. Technology providers are investing billions to keep pace with that evolution. Few organizations can realistically match that level of investment while also focusing on running their core business.

In many cases, the smartest decision isn’t to build a better drafting assistant or internal chatbot. It’s to adopt proven technology and redirect internal resources toward solving problems that are unique to your business.

The Cost Nobody Talks About

Building an AI solution is often the easiest part. Owning it is considerably harder.

Every model deployed into production begins accumulating maintenance responsibilities the moment it’s launched. Data changes, business processes evolve, customer expectations shift, new security risks emerge, and regulations change. Model performance needs to be monitored, evaluated, and, in many cases, retrained. Meanwhile, the broader AI landscape continues advancing.

Supporting all of that ongoing work requires specialized talent across data science, engineering, cybersecurity, governance, and compliance, all of which remain highly competitive and expensive. 

These ongoing costs are rarely reflected in the original business case, and that’s the part I see leaders underweight the most.

I saw this directly when my team modeled a five-year total cost of ownership for an AI pricing capability, comparing a partner-built solution against a subscription platform. In the first year, buying looked like the easy call. Over five years, once we accounted for recurring implementation and integration services on the subscription side, the gap narrowed considerably. Cost stopped being the deciding factor, and the conversation moved to what actually mattered: who controls a decision that sits right next to revenue. 

Operating an AI system can also become more complex as it becomes embedded in the business. The system may need to be reworked when a new data source is introduced, an existing system changes, or security requirements evolve. There also may be restrictions on where data can be stored and who can access it.

As regulators continue to tighten rules around AI and data, requirements for oversight, access, and accountability add another layer of work and increasingly shape what’s practical to build or buy. For data leaders, these constraints belong at the start of the build-vs-buy conversation, not at contract review. 

None of this necessarily means an organization should buy instead of build. It means the initial cost of building is only part of the equation. The longer-term cost is keeping the system secure, compliant, and effective as the business and technology around it change.

The reality is that AI is not a one-time technology investment. It’s an ongoing operational commitment.

Competitive Advantage Doesn’t Come From the Model

As foundational AI capabilities become increasingly accessible, competitive advantage shifts somewhere else.

It comes from what only your organization can contribute: proprietary data that competitors don’t have, operational workflows refined through years of experience, decision-making processes built around how your business actually operates, and deep industry expertise that helps identify the right problems to solve, not simply the easiest ones to automate. These are assets that cannot be downloaded, licensed, or replicated overnight.

In logistics, for example, the value isn’t simply applying AI to transportation. It comes from combining AI with decades of operational knowledge, shipment patterns, customer expectations, and real-world decision-making. Understanding why a dispatcher makes a particular decision, how customer commitments affect routing, or where operational trade-offs occur is often more valuable than the underlying model itself.

I’ve seen this tension play out firsthand in freight. Most of the AI capabilities we looked at made sense to buy. Service management, productivity assistants, and similar tools are the same whether we use them or a competitor does, so building would have cost us time and money without creating an edge. 

Pricing was the exception. Those decisions sit about as close to revenue as anything in our business, and the logic behind them reflects years of knowledge about customers, lanes, and market swings. Running that logic through a product our competitors could license just as easily meant giving away the part that makes our pricing ours.

The result was a middle path: an outside partner handled the model engineering while the data, business rules, and judgment behind pricing stayed with us. The lesson I took from it is that the question isn’t only who builds the model; it’s who owns what matters when the relationship changes. That ownership is where organizations create something competitors can’t easily copy.

A Better Framework for AI Investment

Instead of beginning every conversation with “Should we build this?” leaders should work through a different set of questions. In my experience, these are the factors that determine whether a build creates value or just creates work. 

How close does it sit to revenue or the customer?

The nearer a capability is to how you win business, price work, or serve customers, the more it may be worth controlling. Back-office and productivity use cases are less likely to clear that bar. Pricing, underwriting, routing, or anything customers experience directly often does.

Is our data actually the advantage?

Proprietary data only differentiates if it’s usable. Data leaders are best placed to answer this honestly. If the data is fragmented, poorly governed, or missing history, the first investment may be in the data itself, and building a model on top of it too early just automates the mess.

What does it cost over five years, not just one?

Build the business case on the full life of the system, including retraining, monitoring, integration changes, security reviews, and the people needed to do all of it. Do the same for the buy option, including recurring services, price increases, and the cost of adapting your processes to someone else’s product. The two numbers often end up closer than the first-year view suggests.

Who will own it after launch?

Every built system needs a named owner, a team with the right skills, and a budget line that survives the next planning cycle. If nobody can say who will be running it in year three, that’s a strong signal to buy.

What happens if we need to leave?

Whether you build with a partner or buy from a vendor, be clear on who owns the model, the training data, the business rules, and the outputs. Ask how you would move to another provider and what you’d lose. Your exit options are part of the value of the decision.

What do regulation and data residency allow?

Privacy, sector, and AI-specific rules can take options off the table before cost or capability even enter the discussion. Asking where data can be stored, who can access it, and how decisions must be explained can quickly narrow the field.

Could our competitors purchase essentially the same capability tomorrow?

This is the final test. If the answer is yes, building it yourself is unlikely to create meaningful value. If the answer is no, because success depends on proprietary data, specialized workflows, or institutional knowledge, then investing in a custom solution may be exactly the right decision.

Finding the Right Balance

The answer is rarely a clean build or buy. Often, the right structure is a mix: buy the commodity layers, partner for the engineering you can’t staff, and keep ownership of the data and decision logic that make you different.

Some capabilities make sense to buy as they become standard infrastructure, while others warrant strategic investment and control because they reflect something unique about how the business operates. Knowing the difference is becoming one of the most important technology decisions CDOs will make.

The organizations that succeed with AI won’t necessarily be the ones building the most models. They’ll be the ones that invest where AI amplifies what already makes them different and let the market provide everything else.