kyc_aml · finance · workflow
The AI Enterprise Adoption Curve: Lessons Learned — Credal's observations on enterprise AI adoption stages, strategies, and common barriers
Enterprises attempting to move AI prototypes into production face compounding challenges: data security and compliance concerns when integrating company data, difficulty identifying which use cases deliver real value, fragmented tooling across teams, legal and regulatory barriers, employee resistance, and the unpredictability and debugging difficulty of RAG-based search systems.
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 · Executive excitement and prototyping
Executives or an AI task force drive initial excitement while engineers experiment with open-source libraries, vector databases, and third-party LLMs to prototype workflows.
Tools used
CredalAzure OpenAIChatGPT EnterpriseGithub CopilotSourcegraph's CodyCodiumMixtral-8x7BLLaMA 2GPT-4Claude 3Claude 1.0Defog
Outcome
(not stated)
What failed first
Enterprises that build internal wrappers around a single LLM provider find employees bypassing them via personal devices, while those that buy point solutions face rapid obsolescence as frontier models evolve.
Results
Time savedroughly 50%
Volume75%
Cost replaced$40-$60 per user per month
Grounding & classification
Source type: listicle or blog summary
28 fields verified against source quotes.
enterprise searchragcall recordingmetric backedsource backedtools describedworkflow describedfinancial servicesautomation ratelisticle or blog summarykyc amlsales ops