Build vs. Buy: A Decision Framework for Enterprise AI

Every enterprise AI initiative hits the same fork in the road: should we build a custom solution or buy something off the shelf?

Get this decision wrong and you'll either waste months building something you could have bought, or lock yourself into a vendor that can't meet your needs.

Here's the framework we use with clients to make this decision clearly.

The Default Position

Let's start with a controversial take: your default should be "buy" for most AI capabilities.

Why? Because building AI systems is expensive, risky, and requires specialised expertise. Unless you have a strong reason to build, you're probably better off leveraging solutions that others have already invested millions in developing.

But there are important exceptions. The question is: when does "build" make sense?

When to Buy

Buy when the capability is a commodity.

If many vendors offer similar solutions and none require deep customisation to work for you, buy. Examples: document OCR, speech-to-text, basic sentiment analysis, standard LLM APIs.

Buy when speed matters more than fit.

If you need something working in weeks rather than months, buying is almost always faster. Even imperfect vendor solutions ship faster than perfect custom ones.

Buy when you lack AI expertise.

Building AI systems requires specialised skills: ML engineering, data engineering, MLOps. If you don't have these in-house and aren't planning to hire, buying makes more sense.

Buy when the vendor's scale benefits you.

Some AI capabilities get better with more data and usage. Vendor solutions that serve many customers often outperform what you could build alone, because they learn from a larger data pool.

When to Build

Build when the capability is a competitive differentiator.

If the AI system is core to your value proposition—something that makes you genuinely different from competitors—building gives you control and defensibility.

Build when your data is your moat.

If you have proprietary data that makes your AI meaningfully better than what's available elsewhere, that's a strong argument for building. But be honest: is your data really that special?

Build when vendor solutions can't meet requirements.

Sometimes your constraints (regulatory, security, performance, integration) simply can't be met by existing vendors. In these cases, building may be your only option.

Build when you need deep integration.

If the AI system needs to integrate deeply with your existing workflows and systems, and vendors can't provide that level of integration, building might be necessary.

The Hybrid Path

In practice, the answer is often "both."

Buy the foundation, build the differentiation.

Use vendor APIs for commodity capabilities (language models, vision, transcription) but build custom systems for the logic, orchestration, and domain-specific features that make your solution unique.

Start with buy, graduate to build.

Begin with vendor solutions to prove value quickly. Once you understand your requirements deeply, you can make an informed decision about whether to build custom solutions.

A Practical Scoring Model

Here's a simple model for scoring build vs. buy. Rate each factor 1-5 and sum the scores.

Buy indicators (+):

• Time to value is critical: +1 to +5
• Capability is commoditised: +1 to +5
• Internal AI expertise is limited: +1 to +5
• Maintenance burden concerns: +1 to +5

Build indicators (+):

• Competitive differentiation: +1 to +5
• Proprietary data advantage: +1 to +5
• Deep integration required: +1 to +5
• Vendor constraints unacceptable: +1 to +5

If Buy score > Build score by 5+, lean Buy. If Build score > Buy score by 5+, lean Build. If scores are close, consider hybrid approach.

Questions to Pressure-Test

Before you finalise your decision, ask:

1. What happens if we're wrong? If you build and it fails, what's the cost? If you buy and the vendor can't meet needs, what's the path forward?

2. What's our true timeline? Building always takes longer than you think. Is that timeline acceptable?

3. Do we have the team? Building requires ongoing maintenance. Do we have the engineers to support this for years?

4. What does "success" look like? Are we optimising for speed, cost, capability, or control? The answer shapes the decision.

The Bottom Line

Don't let ego drive the decision. Building custom AI sounds impressive, but it's only the right choice when the capability is genuinely differentiating and you have the team to support it.

For most capabilities, buying gets you to value faster with less risk. Save your building effort for the things that actually matter.