AI isn't magic. It's a tool with real strengths and real limitations. Some people try it once, get frustrated, and give up. Others integrate it into their workflow and can't imagine going back. The difference isn't luck. It's understanding what AI is good at, what it struggles with, and how to work with it effectively.
AI used to be a research project. Now it's a practical tool that small businesses can actually use. But there's a gap between the hype and the reality, and that gap trips people up.
Some people try AI once, get frustrated, and give up. Others integrate it into their workflow and can't imagine going back. The difference isn't luck. It's understanding what works and what doesn't.
Early AI was like a very smart intern. Great at answering questions, but you had to tell it every single step. Modern AI is agentic, meaning it can break down tasks, make decisions, and take action with less hand-holding.
This shift is why AI suddenly feels more useful. You can ask it to "analyze our sales data and draft an email to the team with insights" and it can actually do that, connecting tools and reasoning through the steps itself.
AI can be powerful, but it can also be dangerous or frustrating if you don't understand the risks.
Malicious actors can hide instructions in websites, documents, or emails that trick AI into doing things it shouldn't.
Giving AI access to sensitive data creates risk of accidental exposure through conversations, logs, or compromised accounts.
AI can misunderstand instructions and take actions you didn't intend. Always set strict limits on what it can access.
"We are still unable to secure LLMs from malicious inputs. We simply don't know how to defend against these attacks."
— Bruce Schneier, Security Expert
Makes up facts confidently
Same prompt, different results
Forgets earlier details
Hard to predict expenses
"We believe the impact of AI might be comparable to that of the industrial and scientific revolutions, but we aren't confident it will go well."
— Anthropic, creators of Claude
It's not about technical skill. It's about understanding what AI needs from you to work well.
"Make my website better" doesn't give AI enough context. It needs specifics.
AI gets things wrong. People who succeed expect to review and refine, not accept blindly.
Trying to do complex math in ChatGPT or creative writing in a code model—mismatched expectations.
AI doesn't know your business, your workflow, or your constraints unless you tell it.
First attempt fails, they quit. Success comes from iteration—refining prompts and approach.
"Analyze Q3 sales data and identify our top 3 customer segments by revenue" gets results.
AI drafts, you refine. It's a collaboration, not automation. That's where the value is.
Use Claude for coding, GPT-4 for writing, Gemini for multimodal tasks. Right tool, right job.
Share your workflow, constraints, and goals. The more AI understands, the better it performs.
Each attempt teaches you what works. Successful users refine their approach over time.
Understanding AI models is one thing. Getting them to work with your actual business processes is another. That's where most people get stuck.
The gap isn't technical knowledge. It's knowing how to connect AI to your workflow in a way that actually saves time instead of creating more work. That's what I help with.
I don't just explain how AI works. I help you integrate it into your actual workflow. I'll figure out which models fit your needs, how to structure your processes to work with AI effectively, and how to build tools you can own and maintain.
You don't need to become an AI expert. You just need someone who understands both the technology and your business reality to guide you through it.
A typical engagement is a few focused weeks. Exact timing depends on access, scope, and how quickly you can test with real work.
Pick one workflow to improve, define success, and set up access (docs, tools, test data).
I map what happens today, identify the bottleneck, and turn it into a concrete plan with tradeoffs.
You get a working version quickly, then I iterate with you using real work (not demos).
Add the boring-but-important parts so it keeps working, then I hand it off cleanly.
Note: I'm not trying to stretch this into a long retainer. The goal is a working tool plus a clean handoff so you can run it without me.
Test AI on tasks where mistakes won't cause major problems. Drafting emails, research summaries, brainstorming. Build trust before expanding to critical workflows.
Treat AI outputs as first drafts, not final products. Check facts, review decisions, test code. AI is a tool to accelerate your work, not replace your judgment.
Don't give AI access to production databases, financial accounts, or customer data without safeguards. Use read-only access where possible. Set spending limits.
Critical decisions should require human approval. Email campaigns, financial transactions, data deletions. AI proposes, humans approve.
AI capabilities and limitations change rapidly. What's true today might not be true in six months. Keep learning, stay skeptical, adapt your approach.
The key insight: AI is powerful when used thoughtfully, dangerous when deployed carelessly. The businesses that succeed with AI aren't the ones who use it the most. They're the ones who use it strategically, safely, and with realistic expectations.