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When facing a build-versus-buy decision, the key filter is whether the initiative deepens your competitive moat with customers. If a project doesn't leverage your proprietary data or capabilities to strengthen this moat, it's better to partner or buy a solution, even if it seems core to the business.

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When deciding to build or buy, the key factor is strategic importance. Never cede control of technology that is core to your unique value proposition to a vendor. Reserve outsourcing for necessary but commoditized functions that don't differentiate you in the market.

In previous tech waves, proprietary technology was a key differentiator. Now, with powerful AI models widely available, the advantage shifts to deeply understanding customer problems. The question "Should we even build this?" is more critical to creating a moat than the technology itself.

Before engaging external partners, decide your tech strategy. 'Build' in-house for a core competitive advantage. 'Buy' off-the-shelf enterprise solutions for broad utility. 'Borrow' expertise from agencies for specialized projects where you want to upskill your team.

As AI makes building software features trivial, the sustainable competitive advantage shifts to data. A true data moat uses proprietary customer interaction data to train AI models, creating a feedback loop that continuously improves the product faster than competitors.

The traditional wisdom to "build what's core" to your business is becoming obsolete for AI. The immense cost and rapid advancement of foundational models by major labs mean most companies are better off buying or partnering for core AI capabilities rather than attempting to build them in-house.

The decision to build or buy software has evolved. Companies should buy commodity infrastructure (e.g., dialers, CRM plumbing) but must own the "intelligence" layer—the unique business logic for things like ICP definition or lead scoring. This allows for customization and portability, preventing vendor lock-in.

RAMP built its AI platform in-house because they view internal productivity as a competitive moat. Owning the tool allows them to move faster, deeply understand user pain points, and leverage internal learnings to inform their external customer-facing products.

When deciding whether to build or buy an AI tool, purchase stable, undifferentiated infrastructure (like a dialer). In-house resources should focus on building proprietary intelligence that creates a unique competitive advantage, such as a custom pre-call research model tailored to your specific customer profile.

When deciding to build versus buy, tech-enhanced services companies should only build software that codifies their unique strategic opinions and subject matter expertise. Commoditized features, even if core to the workflow, are better bought or rented, preserving engineering for true differentiation.

For AI projects, decide whether to buy or build using a 2x2 matrix plotting business differentiation against implementation complexity. You should build projects that are highly differentiating but complex. Conversely, you should buy solutions that have low-differentiation and low-complexity.

Decide 'Build vs. Buy' by Asking if It Deepens Your Proprietary Customer Moat | RiffOn