Tom Hundley🤖 Ghostwritten by Claude · Curated by Tom Hundley
This article was written by Claude and curated for publication by Tom Hundley.
The build vs. buy decision for enterprise AI represents one of the most consequential strategic choices organizations face today. With 92% of companies investing in AI but only 1% achieving full maturity, the stakes for making the right decision have never been higher.
A sobering statistic underscores the importance: 67% of software projects fail due to incorrect build vs. buy choices. For CTOs, this isnt just a technology choice—its increasingly an existential one that will determine which organizations thrive and which struggle to remain relevant.
Lets address the elephant in the room: the build vs. buy framing itself may be outdated. As CIO.com notes, your next big AI decision isnt build vs. buy—its how to combine the two.
The reality facing most CTOs is more nuanced:
Rather than asking build or buy?, ask these five strategic questions:
Build when: The AI capability directly enables what makes your business unique. If your recommendation engine is your product, building makes sense.
Buy when: The capability is table stakes. Every company needs fraud detection, but few compete on fraud detection excellence.
The litmus test: Would a competitor using the same vendor solution threaten your market position? If not, buy.
For CIOs, build vs. buy is intimately tied to data architecture maturity. If enterprise data is fragmented, unpredictable, or poorly governed, internally built agents will struggle. Buying a platform that supplies the semantic backbone may be the only viable path.
Honest assessment questions:
If you answered no to these, youre building on sand.
Consider your governance maturity aligned to frameworks like NIST AI RMF:
Vendors increasingly offer compliance coverage that would take years to develop internally—particularly for regulated industries handling PHI, PII, or financial data.
A common mistake is comparing 1-year subscription costs to 3-year build costs. Accurate decisions need aligned timeframes.
Heres what most TCO calculations miss:
| Hidden Build Costs | Hidden Buy Costs |
|---|---|
| Ongoing model maintenance | Integration and customization |
| Data pipeline operations | Data egress fees |
| ML engineer retention | Vendor lock-in switching costs |
| Incident response burden | Feature gap workarounds |
| Documentation debt | API rate limit scaling |
A surprising 65% of total software costs occur after original deployment—whether you build or buy.
Buying can lead to deployment in weeks, depending on integration needs. Building typically takes 6 to 24 months, considering development, testing, and iteration.
If speed-to-value determines success—perhaps a competitor is moving fast, or theres a market window closing—buying or blending wins.
Many CTOs today are choosing a hybrid approach. According to MarkTechPost, the recommendation for the majority of U.S. enterprise use cases is:
Blend: Pair proven vendor platforms (multi-model routing, safety layers, compliance artifacts) with custom last mile work on prompts, retrieval, orchestration, and domain evals.
This looks like:
Alex Tyrrell, CTO of Wolters Kluwer Health, exemplifies this approach: run experiments early in the decision process to test feasibility. Rather than committing to a build-or-buy direction too soon, his teams quickly probe each use case to understand whether the underlying problem is commodity or differentiating.
| Factor | Lean Build | Lean Buy | Blend |
|---|---|---|---|
| Competitive differentiation | High | Low | Medium |
| Data architecture maturity | High | Low | Medium |
| Governance/compliance maturity | High | Low | Any |
| Time to value pressure | Low | High | Medium |
| Available ML talent | Abundant | Scarce | Limited |
| Use case commoditization | Unique | Commodity | Mixed |
Scoring: If you have 4+ factors leaning one direction, thats your answer. Mixed results suggest a blend strategy.
The build vs. buy question isnt just a technology decision—its a statement about what your organization believes creates value. Companies that build everything often waste resources on reinventing wheels. Companies that buy everything often find themselves competing with identical tools.
The winning CTOs in 2025 are those who can clearly articulate: We build X because its our competitive moat, we buy Y because its commodity infrastructure, and we blend Z because thats where speed meets differentiation.
That clarity—more than any particular choice—is what separates organizations that achieve AI maturity from the 99% still struggling to get there.
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This article is a live example of the AI-enabled content workflow we build for clients.
| Stage | Who | What |
|---|---|---|
| Research | Claude Opus 4.5 | Analyzed current industry data, studies, and expert sources |
| Curation | Tom Hundley | Directed focus, validated relevance, ensured strategic alignment |
| Drafting | Claude Opus 4.5 | Synthesized research into structured narrative |
| Fact-Check | Human + AI | All statistics linked to original sources below |
| Editorial | Tom Hundley | Final review for accuracy, tone, and value |
The result: Research-backed content in a fraction of the time, with full transparency and human accountability.
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