Field notes from the Cited Digital team on AI adoption, AEO strategy, and what actually works for businesses under 50 people.
We charge $450 for our AI Readiness Assessment. Here's exactly what that gets you, why it's priced where it is, and what cheaper alternatives typically miss.
Most AI measurement frameworks track the wrong things. Here's the leading vs. lagging indicator distinction that actually tells you whether your AI investment is paying off, and when the data says it's time to change course.
Most businesses try to 'add AI' all at once and stall out. After helping dozens of companies through this process, here's the phased deployment approach that leads to lasting adoption rather than expensive experiments that get abandoned.
Retainers are good for consultants and bad for clients. Here's the philosophy behind how we scope and price our work, and why it changes the relationship.
The fastest way to spot AI-generated content is that it sounds like every other AI-generated content. Here's how to build a voice training system that makes AI output sound like your business, practically and consistently.
The AEO score at citeddigital.com/aeo isn't a black box. Here's the exact methodology behind each scoring dimension, how to interpret your number, and why the industry average sits well under 40.
The frameworks from McKinsey and Gartner are written for companies with dedicated AI teams and eight-figure budgets. Here's what actually works at fifteen employees.
Schema markup sounds technical, but the concept is simple: it's structured data that tells search and AI engines exactly what your business is, without making them guess. Here's what you need to know and how to implement it without a developer.
You don't need a data warehouse or a six-figure analytics budget. Here's the exact stack I recommend for small businesses that want real measurement without enterprise overhead.
The AI industry is buzzing about 'agents' but most of the coverage doesn't explain what that means practically. Here's a clear breakdown of the difference, and which one you should actually be building for your business right now.
Keywords are strings of text. Entities are things in the world with attributes and relationships. AI systems think in entities, and that distinction completely changes how you should approach visibility strategy.
After fifty of these, the patterns are hard to miss. Here's what most businesses are getting wrong, and one opportunity almost everyone is sitting on.
Most businesses are either locked down (nobody uses AI tools) or wide open (everyone does whatever they want). Neither works. Here's the practical middle ground: guardrails that enable use without creating risk.
Big companies move slow, decide by committee, and can't trust their employees to run experiments quickly. That's your advantage. Here's how to use it.
llms.txt is a plain-text file at your site root that tells AI models who you are, what you do, and how to represent you. It's one of the highest effort-to-signal AEO moves available right now, and almost no local businesses have one.
Adding AI to your CRM sounds simple until you try it. Here's a practical guide to AI-enhanced CRM workflows: what's worth building, what breaks in practice, and how to phase the rollout.
JSON-LD is the structured data format AI engines actually read, and most local business websites don't have it at all. Here's what it is, which schema types matter, and how to verify it's working.
Before you spend money on AI training, run the math. Here's the framework I use, and the traps that make the numbers look better on paper than they turn out in practice.
Follow-up is where deals are won and lost, and it's the thing most businesses do inconsistently. Here's how to build AI-powered follow-up sequences that feel personal, run automatically, and don't require a full CRM setup.
You can't prove ROI on something you didn't measure before you started. Here's the measurement methodology I use with clients before touching any implementation, and why skipping it is the most expensive mistake in AI adoption.
Not a generic roundup. These are the tools I've actually seen move the needle for businesses under 50 people, with honest notes on where each one falls short.
Not all AEO factors are equal. Here's a scored breakdown of the 6 technical signals that actually drive AI citation probability, ranked by impact and implementation difficulty.
The business owners getting the most out of AI aren't the ones with technical backgrounds. They're the ones who've learned to write clear, specific instructions. Here's the framework I use in every training.
There are hundreds of AI tools competing for your budget. Here's how I actually think about building a practical AI stack: what layers matter, what to buy vs. build, and which tools are earning their cost.
Job security fears get all the press, but they're rarely why AI adoption stalls. The actual culprits are quieter and more fixable.
Having a website isn't enough for AI visibility. Here's the entity recognition problem most local businesses are facing, and the specific signals that determine whether an AI engine knows you exist.
Most business owners think AI automation requires a developer. It doesn't. Here's how to build your first real AI-powered workflow in an afternoon using tools you can set up yourself.
Vendors will tell you every business needs AI right now. Here's how to figure out whether that's actually true for yours, and what 'ready' really looks like for a company under 50 people.
Google rankings are a lagging indicator of discoverability. With AI engines now answering queries directly, the visibility model has split in two, and most businesses are only tracking half of it.
Most AI failures aren't technical problems. They're organizational ones. Here's what I keep seeing in the field, and how to spot them before you waste the budget.