Hamza Baig @hamza_automates

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…

The real opportunity in using AI is capturing judgment, the way your best people make decisions Take finance: Two analysts look at the same P&L. One spots a hidden trend, the other misses it. It’s not the numbers that matter, it’s the judgment applied to them. When you build AI…

Everyone talks about AI writing emails, making reports, or creating proposals But the biggest time sink is the setup beofre the final draft - A marketer spends 20 minutes pulling links, screenshots, and data before even writing a report - A recruiter manually copies resumes…

AI works best in bounded, structured workflows It fails when you drop it into unstructured chaos Good candidates for automation: - Categorizing 500+ daily tickets into 6 buckets - Generating weekly sales reports from CRM and email data - Drafting proposals from pre-approved…

AI systems don’t stay perfect forever APIs change, data formats evolve, customer language shifts, that’s called model drift If you design assuming things will break, you’re ahead: - Add monitoring to flag weird outputs (like empty fields or nonsense text) - Build fallbacks…

When you build with AI, you have to choose what you care about more: Accuracy: how often the output is correct Coverage: how many cases it can handle without fallback Most people try to max both and fail A smarter approach is to define trade-offs: For invoices, you want 99%…

This is the AI workflow for invoices that actually works A lot of time gets wasted like this: Every incoming invoice gets opened manually → someone checks vendor name → copies line items → validates totals → routes it to the right approver → uploads to the accounting system…

I BUILT AN AI VOICE AGENT FOR A PIZZA FRANCHISE THAT GENERATED $73000 It took me 15 minutes to set it up Want the workflow + setup doc? Like + RT & reply “375” and I’ll DM it to you (Must follow for DM)

Everyone thinks the hardest part of AI is picking the right tool But the real challenge is mapping where AI creates net value in your workflow Here’s the framework I use when building AI systems inside $10M–$50M companies: - The first mistake is starting with tasks “Can AI…

This is how you spot a good automation opportunity Start with: - High-volume task - Repetitive structure - Clear definition of success For example: Categorize 500 incoming support tickets per day into 6 buckets Don't go with something generic like: “Handle any customer…

Most workflows break when AI is added because of the handoff What I mean is: - Email parser drops raw text into a sheet - AI summarizes the text - Sheet sends the output downstream If the AI fails even once, the whole chain is broken The fix is to design for failure - Add…

I BUILT A GROK 4 AI AGENT FOR A RESTAURANT THAT MADE THEM $21,900 It literally took me 10 minutes to create it Want a walkthrough video + Doc on how to do it? Like+ RT & reply "219” and I’ll DM it to you (Must follow for DM)