Buy a tool for this process, or build around our own knowledge?
Proprietary domain logic and evaluation suites survive underlying model churn, while agentic AI disrupts traditional make-or-buy economics. Leaders risk falling behind if they don't build their differentiated expertise.
The question
A vendor sells a tool that covers most of this process out of the box, but our differentiation comes from internal expertise the vendor's product does not encode. Do we buy the tool and accept the generic workflow, build around our own knowledge, or buy the commodity layer and build only the differentiated part?
Counsel's position
Buy the commodity layer and build only the differentiated part, leveraging agentic AI for accelerated, open-system development.
Verdict
The verdict: Buy the commodity layer and build only the differentiated part, leveraging agentic AI for accelerated, open-system development.
Proprietary domain logic and evaluation suites survive underlying model churn
Given your decision between buying a generic workflow or building around internal expertise, the optimal path is buying the commodity infrastructure while building the differentiated domain logic.
Closed products restrict internal AI-assisted development compared to open systems
If you choose to buy the vendor's out-of-the-box tool, be aware that closed systems hinder your engineering team's ability to use coding agents for future customization.
Agentic AI shifts in-house building to a hybrid governance model
When evaluating whether to build your differentiated workflow, factor in that AI-assisted development fundamentally changes the cost and structure of building in-house.
General-purpose model capabilities provide diminishing competitive differentiation as performance converges
This market shift confirms that your internal expertise is your true differentiator, supporting the strategy to build only the proprietary layer.
70% of AI proofs-of-concept fail to reach production
Whether you build or buy, ensure your differentiated internal expertise is tied to a concrete business outcome rather than general AI experimentation.
Read another verdict
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- How do we measure the return on an AI workflow — and what baseline is honest?
- Our best people's know-how isn't written down — can AI even use it?
- Automate this workflow, or redesign it before we automate?
- Which process should we point AI at first?
- Our AI pilot works but nobody uses it — fix the workflow or kill it?
- Rent AI from a vendor, or run your own?
- Let non-developers ship AI-generated code?