Last week, we talked about Agentic Workflows – how building smart automation on a broken foundation is a fast track to AI chaos. You can't automate what you can't articulate, right? And you're tired of re-solving problems you've already solved.
This week, let's peel back another layer: Even if your workflows are perfectly mapped, why does your AI agent still struggle to connect the right dots? Why does it often miss the nuance that makes or breaks a client project?
It all comes down to something called vectors. But it's not just about the vectors themselves; it's about the meaning you build around them.
And no, you don't need a math degree for this. I promise.
The "Meaning" Gap: When AI Doesn't Speak Your Specific Client Language
You've got an AI assistant. You ask it to "Draft a proposal for a client needing lead generation in a niche industry."
It spits out something okay. But does it sound exactly like your winning proposals? Does it weave in the specific market insights you've gathered over a decade? Does it know that "lead gen" for your ideal client actually means "qualified MQLs with budget for XYZ solution"?
Probably not.
Your AI might find documents with the words "lead generation." But it misses the context. The nuance. The unwritten rules of your consulting practice that deliver results. It's like giving your assistant a dictionary and expecting it to understand your unique consulting methodology and client rapport. It knows the words, but it doesn't get the true meaning that drives your success.
Vectors: How AI Finds "Nearby" Meaning
Think of every piece of information in your business – every client success story, every framework, every project debrief, every email – as a dot in a massive, invisible landscape.
These dots aren't random. They're placed based on their semantic meaning relative to each other.
If a dot representing "client onboarding process" is close to a dot for "successful client retention strategy," it means the AI understands they are semantically related. Even if they don't use the exact same words.
These dots are vectors. The "landscape" is a "vector space."
Paul Signac, the pointillist painter, understood this intuitively. He didn't just slop paint on the canvas. He placed each tiny dot with intention, understanding its relationship to every other dot. The complete picture – the light, the mood, the scene of "The Port of Saint-Tropez" – emerged from the positioning and relation of those individual dots.
That's what vectors do for AI: they give it a way to find position + semantic similarity. It's how AI connects concepts based on their linguistic relationship.
Why Generic AI Isn't Enough for Your Expertise
Most off-the-shelf AI tools and their agents use pre-trained vectors. They've learned "meaning" from the entire internet. Which is fantastic for general knowledge, but often completely misses your specific consulting nuances.
"Client success" for your practice might mean a specific ROI metric and a testimonial from a particular kind of founder.
"Project risk" for your business might include specific client personality types, not just budget overruns.
Your AI agent might find documents containing the words, but if those words are "placed" differently in your unique professional vocabulary, the agent will miss the critical connection needed for truly intelligent action. It's trying to navigate your bespoke service delivery using a generic map of all business operations. It won't see your specific, proven shortcuts.
Shaping Your AI's "Meaning-Space"
This is how you move beyond basic workflow automation to truly intelligent, context-aware AI agents that act like an extension of your brain.
You get your AI to "think" more like you do not by creating custom vector models (that's heavy-duty ML), but by strategically:
Populating a vector database with YOUR proprietary data: Every proposal you've ever sent, every client debrief, every internal playbook, every insightful email. This is what you feed into the system using those pre-trained vector models. Now, the AI can find things semantically similar to your unique content.
Structuring your content with your specific definitions: Think about how you categorize clients (e.g., "Growth-Stage SaaS" vs. "Legacy B2B"). How do you label project outcomes (e.g., "Revenue Spike," "Process Efficiency," "Client Retention Win")? When you consistently apply your labels and organize your content by your logic, you're teaching the AI to understand the explicit connections that vectors alone only imply.
Refining results: When your AI agent gets it wrong, you give it feedback. You show it the right connections, subtly adjusting its "map" of your meaning-space. This makes its retrieval and reasoning more precise.
The goal? To get your AI agent to "see" and connect information like your most experienced, knowledgeable self does. To understand the subtle nuances and explicit connections that make your specific consulting business tick. This is how your agentic workflows become truly smart and actionable.
Small Exercise for This Week:
Pick one type of client engagement or problem you solve often (e.g., "track client relationship," "monthly content strategy").
Now, think about:
What are the 2-3 key stages or deliverables in your process for this?
What are the 2-3 most common challenges you always encounter with this type of client?
What are the 2-3 most common successful outcomes you achieve?
Start consistently tagging your relevant notes, documents, and client communications with these specific terms. This simple act starts to create those explicit connections in your data, laying the groundwork for a truly intelligent AI assistant.
Next week, we'll dive into Context Windows – why giving AI too much (or too little) information can totally screw up its output, even if you've got your "meaning-space" nicely organized.
Go connect some dots,
Tracey
P.S. If your internal documents feel like a chaotic abstract painting, you're not alone. But clarity is possible. Reply to this email if you want to chat about how to turn your expertise into a sharper, more efficient operation.