I BUILT A GROK-4 AI AGENT THAT PULLED $23,500 LAST WEEK I set it up in minutes, and it’s now running 24/7, handling restaurant operations and customer engagement automatically. Here’s exactly what I did: - Built an AI agent that manages key tasks and drives sales - Used tools

Never plug an AI agent directly into live systems Instead, build a sandbox environment that mirrors your production setup Example: - Duplicate your CRM or Notion workspace (dummy data) - Connect the agent there first - Feed it real workflows (send emails, update records,

Your team makes 1,000 micro-decisions in meetings: Who to follow up with, which leads to prioritize, what bugs to fix Then everyone forgets half of it Here’s a system I use: - Record every meeting → Auto-transcribe with Whisper/OpenAI - Send transcript to GPT with a system

Everyone builds automations as if things always go right They design happy-path flows: Input → Process → Output But in the real world, 20% of cases break because of bad data, missing fields, API lag, odd phrasing That’s where your Exception Layer comes in: - Every node in

THIS REAL ESTATE AUTOMATION MAKES ME $5000/MO In this video, I break down exactly how the system works, start to finish. Just automation setups that run 24/7 and bring consistent monthly retainers. Here’s what you’ll learn: - How to identify high-value automation use cases in

Everyone talks about prompt engineering like it’s about syntax But what kills systems is misaligned context You can have a flawless prompt, perfect API chain, even structured data But if your inputs don’t reflect how your business actually operates, your outputs will always be

The best AI workflows are completely invisible No one talks about them or praises them They just handle core business functions in the background smoothly The true indicator of a successful system is the absence of questions When the team no longer questions, "Did it work?"

Most AI projects get judged on accuracy “How close was it to the right answer?” But I’ve learned accuracy is only half the story The bigger cost in a business is handoffs Every time a task moves from AI to human or across systems That’s where delays and errors multiply I now

When I was building my first AI assistant, I treated it like a product But I realised AI is more like plumbing It doesn’t sit on top, it runs underneath The email that lands, the Slack ping that fires, the CRM field that updates, that’s where the leverage compounds The people

The first few AI pilots I ran all failed Because I picked the wrong use cases I tried to automate what looked important, complex, high-stakes tasks But those needed human judgment The real wins came when I automated the boring, repetitive bottlenecks for them The lesson:

I realized this after deploying AI across multiple teams: Even when the AI works, if people have to leave their main tools to use it, adoption drops to near zero Nobody wants a separate dashboard or “log in and generate” The hidden tax is context switching We had to rebuild

One of the most underrated things I’ve learned: Never treat AI outputs as disposable Every draft, every summary, every classification is training data At first, we just shipped results and moved on But when we started storing them and fine-tuning on “accepted vs edited”

When you launch an AI system, it works great in month one By month six, accuracy starts to drop Your business changes, customer language shifts, new products roll out Meanwhile, the AI is still trained on old patterns Most teams don’t plan for that decay I treat AI systems

I've seen so many teams build these incredible AI pilots, and it’s awesome Then it just sits there While everyone's busy clapping for the shiny new demo, the real problem is that no one asked the right questions: - You built it, but who owns it? Who is responsible for its

Nano Banana + Linah AI + n8n is next-level This AI workflow creates TikTok, IG & FB video ads at scale powered by the newest generative video models. No filming. No editors. No expensive UGC creators. Just plug in your products and get viral-ready creatives in minutes.

Most AI tools live in isolation: a bot writes emails, another summarizes docs, a third updates spreadsheets No one notices that each of these is a fragment The better approach is to think in roles, not tasks Pick a function: customer onboarding, content creation, reporting

I've noticed teams get stuck thinking, “If AI can do X, we don’t need a person” But the smarter move is to figure out where your team consistently performs well, then put that into your AI system A support rep knows which tickets need quick replies and which need escalation

A lot of companies use different AI tools for every little job one to write emails, another to tag tickets, and so on This looks good in a demo, but it creates a mess You end up with a lot of back-and-forth and no real progress The real money is in using AI to handle entire

Adoption fails inside teams because people don’t trust it yet Even with AI hitting 90% accuracy, reps often revert to manual tasks "just in case" That caution drains ROI Promises won't build trust; audit trails will Every AI output needs to reveal: - Data sources - Decision

Bad automation looks like this: - AI generates initial drafts - Human editors refine the content - A dedicated formatting expert ensures visual consistency - The final version receives approval from a senior human reviewer Each step adds a handoff and that's exactly what slows