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Choosing whether to build or buy AI solutions is one of the most important strategic decisions organizations make. The framework for the most consequential AI investment decision most businesses never think through properly.
JEDAM DIGITAL
October 2025
8 min read
Every AI initiative eventually faces the same fork in the road. Do we build something ourselves — custom-developed, owned outright, adapted precisely to our requirements? Or do we buy — adopt an existing platform, SaaS tool or vendor solution, configure it to our needs and pay ongoing fees for access?
Both options have contexts where they’re clearly right and contexts where they’re clearly wrong. The mistake most organisations make is not applying the wrong choice — it’s applying the right choice for the wrong reasons, or not asking the relevant questions before committing.
This article is the framework I use when helping clients think through this decision. It’s not a formula — there’s no scoring matrix that makes the choice for you. But there are questions that, honestly answered, make the right direction clear for almost every use case.
The single most important factor in the build vs. buy decision is how central this AI capability is to your competitive position.
If the AI capability you’re building is a core differentiator — something that will give you a measurably better outcome than competitors in a way customers value — then owning it is usually essential. Buying means your competitive advantage is also available to every competitor who signs the same vendor contract. The vendor’s incentive is to sell to as many customers as possible, which means the capability you thought was distinctive becomes table stakes.
If the AI capability is operational infrastructure — something that improves your internal efficiency but isn’t directly visible to customers or meaningfully differentiating in your market — then buying is usually the smarter choice. You don’t gain competitive advantage from building your own expense management AI or your own email scheduling tool. You gain efficiency from using mature tools that work well, are actively maintained, and cost a fraction of what a custom build would.
The framework: differentiation warrants building, operations warrant buying.
What happens if the vendor raises prices, changes the product, or gets acquired? This is not a theoretical risk. The AI vendor landscape is consolidating rapidly. A tool you’ve integrated deeply into your operations today may be discontinued, significantly changed in pricing, or acquired by a competitor in 24 months. The question is: if that happened, how long and how expensive would it be to extract your data, retrain your teams and migrate to an alternative? If the answer is “very long and very expensive”, your vendor dependency is a risk you should price into the decision.
What data does the vendor have access to? Many AI tools are trained or improved using customer data. For operational data this may be acceptable. For sensitive customer data, proprietary business information or content that represents competitive intelligence, vendor data access may be a deal-breaker — not just for commercial reasons but for GDPR and EU AI Act compliance reasons.
Can we actually maintain a custom build? Building custom AI is not a one-time project — it’s an ongoing commitment. Models drift. Data distributions shift. The underlying AI infrastructure evolves and requires updates. A custom build without internal AI engineering capability or a maintenance contract with an external team becomes a liability: working at launch, degrading over time, unmaintained.
What’s the realistic total cost of each option over three years? Custom builds have high upfront costs and lower ongoing marginal costs. SaaS tools have low upfront costs and ongoing subscription fees that compound. For most organisations, the three-year total cost comparison is more informative than the headline project cost — and it often surprises people which option is cheaper over a meaningful horizon.
In practice, most well-designed AI systems are neither pure build nor pure buy — they’re a hybrid. A foundation model from a major provider (OpenAI, Anthropic, Google) is the core reasoning layer. Custom engineering wraps it with company-specific logic, connects it to proprietary data, implements the specific workflow, and builds the interface. This captures the best of both approaches: the mature, continuously improving core model without the massive training cost, combined with custom logic and data ownership that creates genuine differentiation.
The proportion of build to buy in this hybrid shifts based on the competitive importance of the specific capability. The more central it is to how you win, the more custom engineering is warranted around the bought core.
If you’re evaluating a significant AI investment and haven’t yet worked through the build vs. buy question explicitly — do it before the technology conversation. Get the strategic and operational questions answered first: how important is this to our competitive position, what’s our vendor risk tolerance, do we have the internal capability to maintain a custom build, and what does the three-year cost picture actually look like for each option.
The technology decision flows from those answers. Approached the other way around — technology first, strategic questions later — organisations consistently make the choice that’s hardest to reverse.
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