Your AI still writes generic emails (here's why)
You saved hours on that one project with AI, and the quick win felt amazing. After months of use, you’ve also likely experienced the frustration of getting generic outputs that don’t sound like you and stumbling into endless loops that waste time and yield poor results.
The gap isn't between AI users and non-users anymore. BCG found 74% of companies fail to achieve tangible AI value, yet according to Verizon, most small businesses are using it. The real gap lies between those who engineer the context and those who just prompt.
After systematizing operations for dozens of boutique agencies, we see a pattern: Our peers aren't buying enterprise tools. They're building lightweight AI systems with proper context engineering – a systematic approach to using any AI.
The costly mistake everyone makes
Most treat AI like Google. Short queries. Minimal context. Hope for magic.
But AI works like a research assistant who needs onboarding. You wouldn't hand a new team member complex analysis without context about your business, best clients, or success patterns.
Yet we type "analyze this data" and wonder why we fall down the wrong rabbit hole.
Context changes everything
Context engineering means feeding AI your business reality upfront.
Instead of: "Find me new clients" Try: Feeding it your three most successful projects. Ask: "What patterns connect these wins?"
For example:
'Client A was a 50-person consultancy struggling with manual proposal processes, paid $15K for automation, saw 40% time savings.
Client B was a 30-person agency with scattered project data, paid $12K for centralization, improved client reporting dramatically.
Client C was a 40-person firm losing revenue to inefficient follow-up, paid $18K for systematic CRM, increased conversion 25%.
What patterns do you see?'
Same AI tool, completely different (and more valuable!) result.
Make context stick
Paid AI plans let you store context permanently. My custom instructions span 5 pages: positioning, target market, successful client patterns, team make up, and red flags.
Every conversation starts with AI already knowing our systematic approach to operations for small teams, instead of having to start from scratch.
Create separate projects for Sales, Marketing, Delivery, and Strategy. Pre-load each with specific context and frameworks. My sales project includes:
"Compare this prospect to our best three clients"
"Flag qualification criteria from this discovery call"
"Identify operational challenges from their website"
My content project includes:
Voice, style, tone, and canonical examples (high-performing pieces)
Frameworks I like to use to structure my content
Do's and Don'ts (see our Context Engineering Primer for more on coaching your AI)
Tip: Build these by learning from successful AI conversations (h/t RAPPEL Prompt Framework)
Templates ready. Context loaded. Ready for your next call.
Your next move
Our Context Engineering Primer shows you how to get started.
This isn't another generic AI guide, it's the methodology I'm using to systematize my own client assessment process, packaged so you can implement it in your business.
You might also like our Revenue Enabler program—where we apply these AI capabilities to your entire revenue operation. Newsletter subscribers get 50% off the one-hour kickstart workshop using discount code: LIFTOFF
See you on the web,
Malcolm
P.S. The Revenue Enabler Kickstart is 11am NZT on the 6th of November (5pm EST on the 5th). Join now and get the second session free plus direct implementation support.