Build vs. buy AI agents: What 1,000+ enterprise deployments taught us about the real costs
Companies deploying AI agents consistently underestimate the true cost of building in-house, facing hidden burdens in time-to-value, compounding maintenance, and opportunity cost diverted from core business.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Leadership faces build-or-buy decision
Every leadership team eventually faces the same question: should we build AI agents ourselves or buy a platform?
Companies that adopted Dust achieved rapid deployment and broad employee adoption: Wakam hit 70% adoption in two months with 136 agents deployed and 50% reduction in legal contract analysis time; Ardabelle evaluated 50% more deals in the same timeframe; CMI Strategies achieved 95% adoption with 60-70% time savings on commercial proposals.
What failed first
Doctolib's internal ChatGPT-based tool DoctoGPT rapidly attracted 800 active users but became unsustainable as feature requests overwhelmed the team. Wakam's data science team built a working RAG chatbot prototype but found that keeping pace with the AI market would require tripling team size.