Integrating AI into your business takes two things. The first is one organized source of data about how your company actually works. The second is a set of skills that give AI enough context to act on that data without step-by-step instruction.
At TJ Digital we run roughly 40 to 50 client campaigns, and about 70 to 80% of the hands-on work is now done by AI, mostly Claude. Within a year I expect that to reach 90 to 95%. We got there by building the agency around AI at a structural level.
Most businesses are somewhere earlier in that process. They’ve identified a handful of tasks to hand over, and maybe written a few skills. If you sense AI could be doing far more in your business, you’re probably right.
The gap is almost always those same two things. Here’s how to build both.
What Does It Mean to Fully Integrate AI Into a Business?
Fully integrating AI means giving it the same standing context a senior employee would have, so it can pursue a goal without being walked through the work. Handing AI discrete tasks is a different activity with a much lower ceiling.
Think about the difference between two kinds of hires. A junior team member needs step-by-step instructions for everything they touch. A senior team member needs context and goals, then works out the process on their own.
The newest frontier models are the second kind of hire. Treating them like the first kind is what caps most businesses at a handful of automated tasks.
I want to be clear about one thing, because this gets misread constantly. Our rule at TJ Digital is that a task only moves to AI if AI can do it better. Speed alone is not a reason.
| Handing AI tasks | AI built into the business | |
| What you give the model | Instructions for one task | Context, data, and a goal |
| Who designs the process | You | The model |
| Where information lives | Repeated in every prompt | One document, referenced everywhere |
| Output ceiling | How fast you can write prompts | How well the model can plan |
| Maintenance | Rewrite prompts as things change | Update one file |
| Setup effort | Minutes | Weeks of documentation |
What Does AI Need From Your Business to Work Well?
AI needs two things from your business. Data is the hard information about how your company operates. Skills are the written instructions that tell AI how your company thinks about a given domain.
Data covers products, pricing, customers, positioning, voice, and history. Skills cover philosophy, standards, and the internal processes that shape how work gets done.
Neither part works alone. Data with no skills gives you an AI that knows your business and has no idea what you want. Skills with no data gives you an AI that follows your process using generic information.
How Should You Organize Your Business Data for AI?
Store your business data as structured markdown files in one central location, then point every AI system at that location. I recommend following something like Google’s Open Knowledge Format and keeping the files somewhere like GitHub so they’re easy to connect to any model.
OKF is a specification for representing organizational knowledge as a directory of markdown files with YAML frontmatter. Google Cloud’s OKF announcement describes it as a vendor-neutral standard that any agent can read without a runtime or SDK. I’ve written a fuller breakdown of the Open Knowledge Format if you want the full detail.
We build this for every client and call it their Brand Ambassador. It’s an AI project holding around a dozen markdown documents that cover the company overview, products and services, brand voice, content guidelines, competitors, and important pages. Every piece of work we produce for that client runs against it.
The format matters more than people expect. A markdown knowledge base is far cheaper for a model to parse than HTML or a stack of PDFs. It also survives being moved between tools.
How Do You Write AI Skills for Frontier Models?
Give the model context and goals, then let it work out the process itself. A skill is just a set of markdown files containing instructions for AI. What’s changed is that the newest models no longer need the task broken down for them.
So write down the context the model needs to act as a representative of your business. That’s the same background you’d give a senior hire before handing them a goal.
Spelling out step-by-step instructions actively hurts your output at this point. You’re preventing the model from finding a better process than the one you had in mind. In most cases it will find one.
Here’s the practical test I use. If a line in a skill explains how to perform a step any competent professional would already know, cut it. If it explains why your company does something a particular way, keep it.
This is also why writing AI skills has become one of the most valuable assets we own. A good skill is a written record of how your company thinks.
Why Should Each Piece of Information Have Only One Home?
Because the moment the same fact lives in two skills, one of them starts going stale. Every fact, process, and standard should live in exactly one skill. Any other skill that needs it should point back to that skill by name.
Programmers call this DRY, short for “Don’t Repeat Yourself.” It’s the single most useful principle to hold onto as your skill library grows.
Our team uses Notion for task management. So we have one comprehensive skill that spells out how to do anything in Notion, where everything lives, what every field means, and how our internal processes work inside it. Any time Claude needs to do something in Notion, it refers to that skill.
Here’s the important part. No other skill ever describes where things are in Notion. When the Notion setup changes, we update one file and every workflow in the agency stays correct.
Skipping this is the most common way AI systems quietly rot. You end up with six skills that each contain a slightly different version of the same instruction, and no way to tell which one’s current.
Which Skills Should Your Business Build First?
Start with the domains where your company has an opinion that a generic model wouldn’t share. For our agency, that produced a set like this:
- Task management. How our Notion workspace is organized and how work moves through it
- Website optimization. Our general philosophy on what actually improves a page
- Authority. How we think about authority and the way it affects rankings
- Keyword research. How we pick topics worth writing about
- Brand voice. The writing standards every piece of client content has to meet
- Reporting. What we measure, what we ignore, and how we present results
Each one is a written explanation of how we think about a domain. That’s the part a model can’t infer on its own.
The list looks different for every business. A contractor might need skills for estimating, scheduling, and warranty policy. An ecommerce brand would need something else entirely, probably covering merchandising, returns, supplier communication, and seasonal pricing.
Does Using AI This Way Mean You Need Fewer People?
No. A year from now I still expect our agency to be very people-heavy, even with AI doing 90 to 95% of the hands-on work.
People multiply the value produced by AI. The more value AI produces, the more valuable your humans become. They’re the ones setting direction and catching what the model gets wrong.
The roles change shape. The people who do well are the ones who can articulate a process clearly, which turns out to be a rarer skill than it sounds.
How Long Does It Take to Document Everything?
Longer than you want it to. Documenting and organizing all your data and processes is slow work. There’s no shortcut that gets you the same result.
For a sense of scale, we build a client’s first Brand Ambassador in the first two weeks of their campaign. That covers the data half for one business. The skills library is the part that keeps growing.
It’s worth it. The documentation compounds, because every skill you write makes the next one faster. Every model release also makes your existing library more capable.
Start with one domain and leave the rest of the business alone for now. Write the skill, use it on real work for a week, then have the model fold what went wrong back into the file.
What Else Should You Know Before You Start?
Which AI model is best for building business skills?
Claude has been the best option for this work in my experience, largely because of how well it handles very large amounts of context in a single prompt. The major models all support skills in some form now, so the format itself is portable.
Do you need a developer to write AI skills?
No. A skill is plain markdown text explaining how your company approaches something. If you’ve written a detailed prompt before, you’ve already done most of the work.
How many skills does a business need?
Fewer than most people build. One comprehensive skill per domain beats a dozen narrow ones, because narrow skills tend to duplicate information and drift out of sync.
What is an AI brand ambassador?
A Brand Ambassador is the AI project that holds all the structured data about a business, including its voice, services, audience, and competitive position. It’s the data half of AI integration, and skills are the layer that acts on it.
What Is the Best First Step to Take?
Pick the one domain where your company’s opinion differs most from generic advice, and write that skill first. Put your core business facts into markdown files at the same time, so the skill has something to work against.
The businesses pulling ahead right now are the ones treating documentation as infrastructure. They’re writing down how their company thinks, once, in a format a model can read.
We build this system for every client at TJ Digital, and it’s the first thing we set up in a new campaign. Send me a message and I’ll walk you through what an AI-integrated version of your business would look like.