Red Brick Labs | AI Adoption Deck
AI-native transformation for teams that need production systems.
We help companies adopt AI by building the workflows, tools, training, and operating model required to make it stick.
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Who we are
From scattered experimentation to real business execution.
Red Brick Labs identifies high-value workflows, builds AI systems around them, trains the team, and puts the right guardrails in place.
- Build production AI systems
- Automate high-friction workflows
- Train teams inside real work
- Create governance and adoption models
- Connect AI to existing tools and data
AI knowledge layer over 3.5M words of engineering docs. ~$1.3M/year estimated commercial impact across 10 FTE.
End-to-end recruitment pipeline automation. 3 hrs/day back across 8 people and 10-20x more candidate throughput.
What we see inside companies
AI usage is spreading faster than the operating model around it.
Teams are already experimenting with AI. The companies pulling ahead are the ones turning that activity into shared expectations, workflows, and accountability.
Top-down
Leadership is under-driving the change.
Bottom-up
Teams are unevenly enabled.
Missing middle
No one owns the AI operating model.
The missing middle
The missing layer is an AI operating model.
Leadership pushes down. Teams push up. But nothing in the middle connects strategy, tools, training, workflows, quality control, risk, and measurement.
The pattern we see most
The biggest near-term opportunity is often in go-to-market.
Companies missing sales targets are usually not maximizing customer intelligence.
The information exists, but it is scattered across calls, emails, CRM notes, decks, product feedback, support tickets, and individual rep memory.
- Every AE rebuilds context from scratch
- Customer insights do not compound
- Functions operate from different truths
- Key information gets lost between stages
- Conversion suffers when context does not travel
The fix
Create a shared customer intelligence layer.
Signals in
Inbound and campaign data
Sales calls and meeting notes
CRM and deal activity
Closed-won and closed-lost reasons
Product feedback and support signals
Customer intelligence agent
Context
Memory
Governance
A shared layer that carries account context across marketing, sales, product, and customer success.
Actions out
Sharper qualification
Better decks and follow-ups
Reusable account intelligence
Stronger objection handling
Product and marketing insights
Where AI creates impact
Three areas of impact.
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Operational Efficiency
Remove manual work, handoffs, rework, and duplicated effort.
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Productivity
Help teams produce higher-quality work faster by embedding AI into daily workflows.
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Adoption
Create the tools, training, guardrails, and ownership model that make AI usage consistent across the company.
Point solutions create isolated productivity gains. Operating models create repeatable business impact.
Recommended starting point
Stage 1: Sales & Marketing AI Discovery Audit.
Start with a focused audit of the sales and marketing motion.
- Where customer context is created
- Where context gets lost
- Where reps duplicate work
- Where AI is already being used without oversight
- Which workflows can be automated first
- Where guardrails and training are missing
- What can produce measurable impact quickly
A prioritized roadmap of AI workflows, quick wins, governance needs, and build opportunities tied to revenue impact.
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