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Build vs Buy in the AI Era

For two decades the answer was usually "buy." AI just dropped the cost of building enough to change which side of the line a lot of decisions fall on and created a genuine third option in between. This guide lays out the new decision math: build, buy, or assemble, chosen per capability instead of by reflex.

From Pilot to Production: Scaling Enterprise AI

"Buy by Default" Stopped Being the Safe Answer.

  • Where two decades of buying led: the average enterprise now runs hundreds of SaaS apps, nearly half unmanaged, more than half of licenses idle, with rising consumption-based pricing and lock-in an expensive, sprawling estate, not a cheap, safe one.

  • What AI changed: custom development got cheap and fast enough that "assemble" AI-generated code plus APIs plus components is now viable for problems that used to be strictly buy-only, making this a real three-way decision.

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The Numbers That Make This a Board-Level Conversation

342 apps
average SaaS portfolio per enterprise with ~48% unmanaged and only ~45% of licenses actively used (Productive)
~$21M
wasted per organization each year on unused SaaS licenses, up 14.2% year over year (Zylo, 2025)
~35%
of teams have already replaced at least one SaaS tool with a custom AI build; 78% plan to build more in 2026 (Retool — directional)

The Three Options, Honestly

Buy

Best for commodity capabilities you don't differentiate on payroll, email, expense. Fast and vendor-maintained, if you govern the spend. Its real costs: sprawl, rising and unpredictable pricing, and lock-in.

Build

Best for capabilities core to your differentiation, where owning the software is a strategic asset. Maximum fit and control, no lock-in at the price of the maintenance decade AI made cheaper to start but not to own.

Assemble

The new middle: AI-generated code plus APIs plus components, composed into a custom-fit solution. Much of the control of building at much less of the cost best for internal tools, workflow automation, and glue between systems.

The New Decision Framework - 4 Questions

Step 1 — Is this core to your differentiation?

Owning it is an advantage → build or assemble. A commodity every company needs identically → buy. Don't build payroll.

Step 2 — Does a good-enough product already exist?

A mature tool that fits without heavy customization → buy (for non-core needs). Everything on the market forces your process to bend → that misfit is a reason to assemble or build.

Step 3 — What's the total cost of ownership, not the sticker price?

For buy, count sprawl, consumption/AI pricing, and lock-in. For build/assemble, count the maintenance decade, not just the AI-accelerated first version. The cheap-looking option often isn't.

Step 4 — If you build or assemble, will you govern it?

The failure mode of cheap building is a swamp of unmaintained tools. Decide who owns each thing before you make it — or you've traded SaaS sprawl for shadow-IT sprawl.

The Math Changed. The Need to Do the Math Didn't.

"Buy by default" is now wrong often enough to retire — but "build everything because AI is cheap" just trades one reflex for another, and ignores that AI lowered the cost of the first version, not of ownership. Make a genuine three-way decision per capability: buy the commodities, build what's core, assemble the custom-fit middle, and govern whatever you make.

Frequently Asked Questions

No. AI lowered the cost and time of the first version of custom software, not the cost of maintaining it for years — which is where custom projects most often fail. It widens the range of decisions worth reconsidering; it doesn't make "build" automatic.



Less than it was. The average enterprise runs ~342 apps with nearly half unmanaged and about $21M a year wasted on unused licenses, amid rising, less predictable pricing. Buying is still right for commodities — if you govern the spend.

CTOs, VPs of Engineering, and technology leaders making build/buy/assemble decisions as AI reshapes the cost of each.

Composing a custom-fit solution from AI-generated code, third-party APIs, and existing components rather than building from scratch or buying finished. Most AI builders already work this way — generating discrete pieces they integrate.

Governance. Decide who owns and maintains anything you build or assemble before you make it. Cheap building without ownership is just shadow IT under a new name.