Kyc aml · Production

The AI Enterprise Adoption Curve: Lessons Learned — Credal's observations on enterprise AI adoption stages, strategies, and common barriers

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
Executive excitement and prototyping
trigger
“It begins with tremendous excitement, typically driven by executives or an AI task force. A handful of engineers experiment with open-source libraries, maybe try a vector database, and use third-party LLMs to prototype some workflows.”
2
Production readiness blockers surface
validation
“As soon as they want to move that prototype into production, several common challenges rear their head: privacy, security and compliance when integrating company data on the one hand, reliability and maintaining high quality performance …”
3
AI strategy selection
routing
“In practice, enterprises seem to go with one of two AI strategies”
4
Mature sophisticated use cases deployed
output
“Over time you might see a few more sophisticated use cases; for example, using LLMs to accelerate KYC/ML processes, or parsing sales calls for customer feedback”
Reported outcome

(not stated)

Reported metrics
AI tooling adoption within 8 weeksroughly 50%
AI tooling adoption after one year75%
enterprises purchasing ChatGPT Enterprise260+
ChatGPT Enterprise cost per user per month$40-$60 per user per month
Reported stack
CredalAzure OpenAIChatGPT EnterpriseGithub CopilotSourcegraph's CodyCodiumMixtral-8x7BLLaMA 2GPT-4Claude 3Claude 1.0DefogSlackTeams
Source
https://www.credal.ai/blog/the-ai-enterprise-adoption-curve-lessons-learned-so-far
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

(not stated)

What tools did this team use?

Credal, Azure OpenAI, ChatGPT Enterprise, Github Copilot, Sourcegraph's Cody, Codium, Mixtral-8x7B, LLaMA 2, GPT-4, Claude 3.

What results were reported?

AI tooling adoption within 8 weeks: roughly 50%; AI tooling adoption after one year: 75%; enterprises purchasing ChatGPT Enterprise: 260+; ChatGPT Enterprise cost per user per month: $40-$60 per user per month (source-reported, not independently verified).

What failed first in this deployment?

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.

How is this kyc aml AI workflow structured?

Executive excitement and prototyping → Production readiness blockers surface → AI strategy selection → Mature sophisticated use cases deployed.

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