Most AI automation content is theoretical. Mine comes from deploying AI at enterprise scale. As CMO at Papa Johns, I've built two AI agents in production:. 🤖 Erika: Handles marketing finance via Outlook. 8 months running. 🤖 Ava: Personal assistant connected to 15+ tools. When

So true.

the gap between youtube tutorials and production reality. screenshot 1: simple n8n workflow that gets 50k views."build ai automation in 10 minutes!". screenshot 2: actual production system.managing chat histories, agent states, error handling, failovers, data validation, logging,

Stop chasing shiny objects in AI automation. Most "revolutionary" agent swarms and complex templates are digital noise. Real value comes from simplicity. What actually works:. Simple systems (2-6 nodes) > Million-node workflows • They deliver results that matter •.

The n8n physical button is a fascinating example of a growing trend: the convergence of digital automation with the physical world. These simple, tangible interfaces can trigger complex workflows, making automation more accessible and intuitive for everyone. This is a significant

matt wood from aws: 80% of developers use ai in workflows. the future of work is about removing "toil" from jobs.learn how to learn, not just master specific tools. multi-model approach > single model dominance.enterprise ai needs security, scalability, audit trails. stop chasing

definitely a time saver.

openai tried to retire old models AKA "take away people's AI babies". the backlash was so intense they reversed the decision. this isn't about technology anymore. it's about emotional dependency. sam altman acknowledged it: some users can't distinguish reality from AI roleplay. a

This is massive. You wont know how massive until you make use of it. Now it will all be about accuracy during long context sessions.

scaling erika from pilot to org-wide deployment taught us something critical: access control isn't optional anymore. when it was just our finance team using the marketing spend agent, simple was fine. but as adoption spread across departments, we needed real guardrails.

batch tool calling is the most underrated AI optimization trick . instead of 15 separate API calls with system prompts each time, you can batch process them into 3 calls on platforms like @n8n_io . the difference? instead of:.- call tool 1 → wait → process.- call tool 2 → wait.

Monday morning. Flooded inbox. Endless notifications. Sound familiar?. I've been building AI-powered workflows that handle this chaos automatically. Error handling, smart routing, priority filtering - all running in the background while I focus on strategic work. The

when a ton of new models drop and you like to test every single one. - gpt 5 is good but too slow (lots of pressure on API).- claude sonnet 4 still the king at agentic workflows and tool calls but often the price doesnt justify having it everywhere.-glm-4.5 great open source

Everyone's testing GPT-5 with coding challenges and creative writing. I tested it with something harder: our Q4 marketing budget optimization. The task: Allocate budget across 3 markets, 8 channels, and 15 campaign types while maintaining brand consistency and hitting growth

my optimization philosophy: get it working first, then make it cheap. spent the last month testing kimi k2, glm 4.5, and gpt oss 120b for different parts of mario's workflow. initially glm 4.5 via inference providers was crushing kimi k2. but something changed - kimi k2 on groq.

This highlights something critical for enterprise leaders: the AI skills gap isn't just about hiring specialists. It's about upskilling existing teams to work effectively with AI tools. Your marketing team doesn't need to become data scientists, but understanding how to.

Really weird things happening with GPT-5.- When in a project, it will ignore any instructions and just read the project knowledge and ask me what I want to do with it. Over and over and over, completely ignoring instructions. - Advanced voice, super strange, starts playing low.