Every enterprise AI conversation starts the same way: should we build it ourselves or buy a solution? It's the wrong question. The organisations getting real value from AI have moved past this binary and into a more practical framework, one that asks what you need to own versus what you should rent.
The Short Version
- The build-vs-buy question is misleading. The real framework is own, rent, or leverage
- Own what differentiates you (proprietary models, institutional knowledge). Rent commodities (LLM APIs, cloud infra). Leverage partners for accelerated capability with knowledge transfer
- 67% of enterprises already use a hybrid approach ; the pure build or pure buy path rarely works
- The costliest mistake is letting a vendor own your competitive intelligence, with a $2.1M average switching cost when you realise too late
- An AI foundation underneath makes every own/rent/leverage decision compound
67%
of enterprises use a hybrid build-and-buy approach for AI
Source: Gartner, AI in the Enterprise Survey 2024
Why the Binary Doesn't Work
The build-vs-buy framing assumes AI is a product you acquire. But enterprise AI isn't a product. It's a capability. And capabilities don't fit neatly into procurement categories.
Build everything and you're looking at 18-24 months before production value, a dedicated ML engineering team, and infrastructure costs that rival your ERP investment.
Buy everything and you get fast time-to-value but zero differentiation. Your AI does exactly what your competitors' AI does, because you're all using the same vendor platform.
The enterprises that succeed don't pick a side. They build a framework for deciding what to own and what to rent, and they revisit that decision as their capabilities mature.
The Own-Rent-Leverage Framework
We use a three-tier framework when advising enterprise clients:
Own: Your Competitive Moat
These are the AI capabilities that directly differentiate your business. The things that, if a competitor had them, would threaten your market position.
Examples:
- Proprietary data pipelines that create unique training datasets
- Domain-specific models trained on your operational history
- Workflows that encode institutional knowledge
If it's your competitive advantage, you need to own the IP. That doesn't mean building from scratch. It means retaining ownership of the models, data, and logic that make it yours.
Rent: Commodity Capabilities
These are capabilities that every enterprise needs but that don't differentiate you. The plumbing.
Examples:
- Large language model APIs (GPT-4, Claude)
- Cloud infrastructure (AWS, Azure, Google Cloud)
- Standard document processing and OCR
Nobody wins by building a better PDF parser. Rent the commodity, own the intelligence layer on top.
Isaac Rolfe
Managing Director
Leverage: Partner Capabilities
The middle ground. Capabilities where a partner brings domain expertise or accelerators, but you retain the output and the option to bring it in-house.
Examples:
- AI strategy and architecture design
- Custom model fine-tuning with knowledge transfer
- Integration frameworks that become yours on completion
The Decision Matrix
Ask four questions about each AI capability:
- Differentiation: Does this capability directly create competitive advantage?
- Data sensitivity: Does this involve proprietary or regulated data?
- Rate of change: How quickly does this capability need to evolve?
- Internal capability: Do you have (or can you build) the team to maintain this?
The Ownership Test
If the answer to questions 1 and 2 is yes, you need to own it. If the answer to 3 is "very fast" and 4 is "no," you need a partner who transfers capability, not just delivers output.
Common Mistakes
Renting What Should Be Owned
The most expensive mistake is letting a vendor own your competitive intelligence. We've seen organisations spend $500K+ on AI platforms where the vendor retains the fine-tuned models, the training data pipelines, and the domain-specific logic. When the contract ends, the enterprise is left with nothing.
$2.1M
average cost of AI vendor lock-in when switching platforms
Source: Forrester, AI Platform Economics 2024
Building What Should Be Rented
The second most common mistake is investing engineering time in commodity capabilities. Building your own LLM inference infrastructure when API costs are falling 40% year-on-year isn't strategy. It's ego.
Ignoring the Leverage Layer
Many enterprises think in pure build-or-buy terms and miss the partner model entirely. The right partner brings accelerators that cut 6 months off your timeline while transferring the capability to your team.
What This Looks Like in Practice
A typical enterprise AI programme might look like:
| Capability | Approach | Rationale |
|---|---|---|
| Customer insight model | Own | Trained on proprietary customer data, core differentiator |
| Document processing | Rent | Commodity capability, multiple good vendors |
| AI strategy & architecture | Leverage | Partner expertise with knowledge transfer |
| LLM API access | Rent | Commodity, falling costs, easy to switch |
| Internal knowledge system | Own | Institutional knowledge is irreplaceable |
| Cloud infrastructure | Rent | Commodity, scale on demand |
The Foundation Approach
The own-rent-leverage framework works best when there's a shared foundation underneath. An AI foundation (shared data pipelines, common model infrastructure, governance frameworks) means every new capability builds on the last.
Without a foundation, each build-or-buy decision happens in isolation. With one, each decision compounds.
- Should startups build or buy AI capabilities?
- Startups should almost always rent commodity capabilities and focus build effort on their core differentiator. Speed to market matters more than ownership breadth at the startup stage.
- How do you prevent vendor lock-in with enterprise AI?
- Three safeguards: retain ownership of all training data and fine-tuned models, ensure all integrations use standard APIs, and include knowledge transfer in every vendor engagement. The goal is to always have the option to switch.
- When should an enterprise bring AI capability in-house?
- When the capability is core to your competitive advantage, when you have the data volume to justify custom models, and when you've built enough internal understanding to maintain it. For most enterprises, this is 12-18 months into their AI journey.