Logistics AI Use Cases: Real Value and Smoke and Mirrors

Most AI guides are written for people who already get it. This one is written for financial advisors. What AI actually is. What it cannot do. Why compliance responsibility never transfers to the tool. And the questions financial advisors are actually asking, answered plainly. Swipe through. Share with a financial advisor who needs it. Because everybody needs to be AI Savvy. Number 3 of the AI in Plain English series. One profession at a time.

🏀 Just shipped -an AI document processing pipeline that turns messy PDFs into clean structured data. Zero manual work. *** Here's what it does in 10 seconds: Drop in an invoice, receipt, or contract → it will reads it → structured data appears in Google Sheets automatically. No templates. No rules. No manual copying. The problem it solves is real — businesses waste hours every week manually extracting data from supplier invoices, expense receipts, and contracts. This pipeline handles it end to end, flags anything it's uncertain about for human review, and logs everything to a live spreadsheet. #AI #Automaiton #Document_Processing

𝐓𝐡𝐞 𝐈𝐫𝐫𝐞𝐝𝐮𝐜𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐨𝐟 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐢𝐧 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞. 𝐖𝐡𝐚𝐭 𝐀𝐈 𝐜𝐚𝐧 𝐜𝐨𝐩𝐲, 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐜𝐚𝐧'𝐭. A lot of us are asking the same question right now: if AI can build what took us months, what is actually valuable? What is the moat? I’ve been thinking about this for a while. My answer is in this chart. Code is no longer what’s hard to replicate(obviously!). What your system has learned by running is. That gap only grows. Full write-up here:https://lnkd.in/g_k7-zZU #AgenticSoftwareEngineering #Claude #Softwaremoat

Here is an extremely interesting perspective on why AI is increasing the gap between system and Production knowledge from my cofounder, Aravind Jayendran. These are our first-hand experiences at LatentForce. Have a look here - https://lnkd.in/gC3EVpVB

I’ve never posted on LinkedIn before, but this problem made me start. What if one missed document could delay a life-saving drug? That’s not hypothetical, it Actually happens. Pharmaceutical companies deal with thousands of compliance documents, and sometimes a single outdated file buried inside a 100+ page vendor bundle can trigger FDA warning letters, production delays, or even recalls. I recently started an externship where I’m diving into this exact problem: Understanding how AI can help automate document processing in pharmaceutical supply chains. Right now, I’m not even building models yet. I’m focused on something more important first: understanding the problem deeply. Here’s what I’ve been exploring so far: • Why manual document processing breaks at scale • How AI combines Computer Vision + OCR to “read” documents • How NLP and LLMs extract meaning (not just text) • Why compliance requirements make this problem much harder than typical document processing One thing that stood out to me: Humans can skim a document and “just know” what matters. AI has to be explicitly trained to understand structure, context, and edge cases, and that’s where most challenges come in. This project is helping me connect multiple areas I’ve worked on: • Machine Learning • Data Engineering • Cloud-based data pipelines Over the next few weeks, I’ll be sharing: • Real challenges in document AI • What works vs what doesn’t • How different approaches (rule-based vs AI vs LLMs) compare If you’ve worked on document processing, OCR, or LLM pipelines, I’d love to hear your insights. #AI #MachineLearning #DataEngineering #DocumentAI #SupplyChain

Unpopular opinion: Training a model is the easiest part of AI/ML. The real problems start after deployment. Things you don’t really think about at first: - data drift - weird edge cases - pipelines breaking at the worst time Your model might look great in testing. But production is a different story. That’s where most ML projects struggle. And honestly, that’s where you actually learn what you’re doing.

What Is llms.txt and Does Your Business Need One? You may have seen mentions of a file called llms.txt and wondered whether your business website needs one. The honest answer depends entirely on what kind of website you run. Short answer: llms.txt is a Markdown file you place in your website's root directory to help AI models understand your content. It was proposed in September 2024, and over 844,000 websites have adopted it according to BuiltWith. For most small business websites, it will not change your rankings. For content-heavy or documentation sites, it is worth the 10 minutes it takes to create. What llms.txt Actually Is llms.txt was proposed by Jeremy Howard, co-founder of Answer.AI and creator of the fast.ai deep learning course, in September 2024. The specification lives at llmstxt.org. For a complete editorial article: https://lnkd.in/gqN9TXnm

Here's my go-to AI workflow: - I have a need or question - I ask two separate models to do a lit review of the topics - I read them and then talk what I'm taking away outloud (and record it) - I feed that back into a system to transcribe and debate* - I do it again (and record again) - I turn that recording into talking points (headlines, not full paragraphs) - I generate from there *I'm also learning that I need a break at this point to go walk outside or clean the house so my brain can think about it outside of my attention window (in the backburner).

AI models out of the box STILL can't get questions like these right. Manus AI can, since last year. Manus isn't cheap, but it's quality. 💡 TIP: Get your companies to cover your Manus subscription. Credits can be pooled among members, and we don't train on your data. AI slop is becoming a pandemic. The additional accuracy is worth paying for. -- [Edit: More background because I forget that not everyone is in the AI Bubble] The other models, including the latest, ALL reply that you should walk instead of drive. (And this doesn't make sense because you want your car at the car wash to be washed)