Intelligent Business Decisions

Using #AI to extract information from documents to put it into the system is not a new discipline…   …and it has gotten much easier to scale with #generativeAI. With SAP Document AI, we already process billions of documents per year, handling over 50 document types such as invoices or contracts, and being able to understand more than 100 languages. However, a big gap remains: You never get 100% accuracy out of the box, because the remaining 10-20% are a last-mile-problem, slowing down teams and limiting adoption. Sometimes, even a human being has a hard time figuring out in a document where the material number is located.   For example, our customer Tyrolit Group, a leading manufacturer of grinding and drilling tools, had already an excellent out-of-the-box accuracy of Document AI of 91%. But the remaining 9% had still to be corrected and entered manually in the system. A huge gap! So, we were wondering, what if your document processing could learn from every correction - instantly? With instant learning within SAP Document AI, we’re closing exactly that gap - for good. Now, when a user corrects something, the system learns instantly. No retraining. No finetuning. No waiting. Fix it once — and it’s fixed for everyone. This isn’t just an upgrade. It’s a breakthrough.   The benefits: ✅ Automate document handling within SAP apps ✅ Enhance accuracy with AI that adapts in real-time ✅ Simplify operations with seamless integration and built-in compliance   Check out the system in action and watch this real-world demo video from our customer Tyrolit Group! 📹

What if you never had to search for a digital file again? What if your documents organized themselves intelligently, understanding their content and context without manual tagging? In our increasingly digital world, where the average professional manages 1,300+ documents annually across multiple platforms, AI document management isn't just convenient—it's becoming essential for maintaining our sanity and productivity. I've just published an in-depth exploration of "From Chaos to Clarity: How AI Organizes Your Digital Life," examining how artificial intelligence is revolutionizing document management through natural language processing, computer vision, and autonomous knowledge graphs. The transformation is already happening: Stanford studies show users of AI document tools experience 59% less anxiety about information management while saving 7.2 hours monthly on administrative tasks. From Notion AI's intelligent workspaces to Amazon Alexa Document Manager's voice-controlled filing, we're witnessing an explosion of tools designed to tame our digital chaos. But which solutions actually work? My article cuts through the hype to explain the core technologies, showcase real-world implementations, and provide practical guidance for individuals and organizations drowning in digital disorganization. With insights from leading experts like Dr. Micheline Casey, Kate Crawford, and Lee Bogner, this comprehensive guide will help you understand not just what's possible today, but where document management is heading tomorrow. Whether you're a solopreneur managing client files or an enterprise leader overseeing millions of documents, this article offers a roadmap to clarity in your digital life. Join me in exploring how AI is silently transforming information from a burden into an asset. #aitransformation #aiassistent #idp

A major UK retailer recently said no to migrating to SAP S/4HANA. Kingfisher plc kept ECC running, moved it to Google Cloud, brought in third-party support, and built AI personalization on top. The story spread quickly. The internet treated it as proof that the SAP roadmap is optional. That is not the lesson worth carrying. The line that should travel further came from John Burns at Summit BHC, quoted in CIO Online. He stressed the importance of Phase Zero. Before any architecture decision, the organization has to standardize its data, align its processes, and govern its front end. Without that work, splitting the layers solves nothing. Kingfisher's choice worked because they had already done the work. Their architecture was modular and API-first by design. The path decision came after the readiness decision, not instead of it. Most organizations get this backwards. They debate the platform options as if the choice itself creates value. It doesn't. The path only creates value if the organization can absorb it. Last week I wrote about scope getting decided by default. Readiness is the deeper version of the same problem. Skip the work and you don't have a real choice between paths. You only have the illusion of one. Across enterprise programs I've delivered on four continents, the predictable failure has not been the platform decision. It has been the assumption that picking the right path substitutes for the work that should have happened two years earlier. No technology decision compensates for an unready organization. #SAP #DigitalTransformation #ProgramGovernance #ERPModernization

Every loan application, insurance claim, or trade document still burns 30–45 minutes of human effort Reading, re‑keying, validating, escalating. Most enterprises don’t have a document problem - They have a decision latency problem. Not because AI can’t help, but because automation has been brittle, opaque, and untrustworthy. This is where AI‑orchestrated intelligence changes the game. The latest Azure AI Foundry blog shows how intelligent document processing is moving beyond OCR and single‑model extraction to multi‑model, quality‑gated pipelines-where: AI does the heavy lifting Humans stay in the loop where judgment matters Every step is observable, auditable, and compliant The result? 📄 Documents processed in minutes, not hours 🧠 Built‑in reasoning and validation—not blind extraction 🔁 New document types added with configuration, not rewrites This isn’t “AI replacing people.” It’s AI absorbing complexity so humans can focus on decisions. If you’re building document workflows that need to scale and stand up to regulatory scrutiny, this blog from Sunil S. is worth a read 👇 ***************************************** Ranjani Mani #reviewswithranjani #Technology | #Books | #BeingBetter

Have your own free, supercharged version of Joule for Consultants that can connect your own SAP System and SharePoint Instance, is open source, and runs locally. Enterprise SAP teams are stuck between two bad options: you can’t connect public AI to your SAP system for security and governance reasons, and the tools that are enterprise-ready are often either too limited for real project work or priced out of reach for broad rollout. I put together a “middle ground” setup using a LibreChat fork plus additional components, based on open source and MCP. It runs on your own infrastructure (Docker), authenticates via Entra ID, connects to SharePoint for internal specs, and connects to your SAP system in a deliberately safe way (read-only via a technical user with display authorizations). The goal is simple: an assistant that can use your documentation and your ABAP objects as context—without breaking security boundaries. Again thank you to Alice Vinogradova for creating the ADT MCP Server! Full write-up + setup guide: https://lnkd.in/d_jrspCT

3 hours. Every single day. That’s how much time 76% of office workers waste on manual document tasks. This isn’t just a productivity issue. It’s a retention crisis hiding in plain sight. When we speak with C-level leaders in financial services, logistics, and manufacturing, the focus is often on cost savings from automating invoices, KYC, or compliance workflows. But here’s the bigger story: Your best people are quietly becoming your biggest flight risks because of outdated document processes. What’s really happening behind the numbers: ✅ Knowledge workers recreate missing documents 83% of the time instead of doing strategic work ✅ Finance teams waste the equivalent of 12 full-time employees correcting document errors ✅ Employees cite “repetitive tasks” as a top reason for job dissatisfaction We’ve seen banks reduce turnover in document-heavy KYC teams by using intelligent document processing. Platforms like DocHorizon free staff to focus on relationship-building instead of data entry. In 2025, document automation isn’t just digital transformation. It’s an investment in your people. 👉 If your best employees left tomorrow, would outdated document processes be part of the reason?

In our obsession with automating every task, we often miss the root cause of inefficiency: decisions aren’t delayed due to slow processing, but because critical information is fragmented, hidden, or buried deep. AI’s true value isn’t in replacing human judgment - it’s in amplifying and reinforcing it. This is exactly the thinking behind our Clause Mapping Engine. For tendering teams, it means no longer wasting countless hours manually combing through lengthy tender documents. Instead, critical clauses and hidden risks are identified, contextualized, and structured clearly-empowering teams to quickly grasp implications, confidently navigate risks, and make swift, strategic bid decisions. AI isn’t your substitute. It’s your strategic ally, helping your tendering team cut through the noise, see the full picture, and secure better outcomes faster.

Before a USCIS agent ever opens your filing, AI agents have already categorized your documents, flagged anomalies, and decided what the officer sees first. Here's what's running behind the scenes right now: 🔴 An Evidence Classifier that uses machine learning to automatically categorize and tag every document uploaded with a petition. 🔴A Document Translation Service powered by Azure AI that generates image-to-image translations displayed side by side with originals in the ELIS Digital Evidence Viewer. Officers no longer manually compare your certified translation. The AI does it in minutes. 🔴A Verification Match Model that pulls data from multiple systems and compares names, dates, and documents against known records using confidence scores. It powers both E-Verify (250,000 requests/day) and SAVE (70,000 requests/day). 🔴Facial recognition through IDENT for photo validation on I-765 applications, checking uploaded photos against biometric records. (Note: listed as "Retired" in the Jan. 2026 update to DHS AI Use Case Inventory, USCIS.) 🔴A centralized vetting hub using AI to scan written narratives across filings and detect when "the same language appears across many unrelated filings by different people." Repetition signals scripted or mass-produced claims. How do you file evidence in 2026 knowing AI touches it first? 🟢 Name your files like metadata. Use cover sheets as schema declarations. A structured cover sheet with key-value pairs (Document Type, Date, Source, Receipt Number) gives the classifier high-confidence tagging on page one. The machine reads it, categorizes correctly, and surfaces it to the officer in the right context. 🟢 Submit tagged PDFs, not scanned images. Azure Document Intelligence parses selectable text cleanly. Scanned image PDFs require OCR, which introduces error. If your client sends you a phone photo of a bank statement, convert it to a proper PDF with embedded text. Five extra minutes could prevent a misclassification. 🟢 Format every exhibit identically. The classifier learns patterns. If 27 of your 28 exhibits have identical formatting and one doesn't, that outlier gets flagged differently. Same fonts. Same layout. Same metadata structure. I started building exhibit cover sheets specifically optimized for machine readability. Helvetica font. 14pt minimum. Black on white. Structured metadata fields matching USCIS evidence categories. No logos, no shading, no decorative formatting. Evidence now passes through two reviewers. One of them processes documents in milliseconds and never gets tired. 🗽

𝗛𝗼𝘄 𝘄𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝟭𝟬𝟬,𝟬𝟬𝟬+ 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗔𝗜 I've just released a new issue of my newsletter, breaking down how we built Trucking Hub DocuSense™, our AI-powered document processor that transformed a manual bottleneck into an automated pipeline. Trucking companies process thousands of rate confirmations daily. Each arrives in a different format: clean PDFs, scanned faxes, mobile photos. Manual processing breaks at scale. We needed a system that could handle chaos. Here is what you will find inside: 🔹 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. Manual entry costs time and money. A single misread date or rate triggers detention fees. Traditional OCR tools fail when document formats vary across thousands of brokers. 🔹 𝗧𝘄𝗼-𝘀𝘁𝗮𝗴𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲. We separated text extraction from AI understanding. PDFPig + Tesseract for OCR, GPT-4 for field extraction. This architecture enables us to optimize each stage independently and swap providers without affecting business logic. 🔹 𝗣𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴. We hit 96% accuracy without training custom models. Externalized prompts let us adapt to new broker formats in hours, not weeks. Few-shot examples improved accuracy by 15%. 🔹 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿. 90% automation rate. 70% faster processing. 2x reduction in manual work. 25-30% cost savings. The system scaled from zero to 100K+ documents with minimal code changes. 👉 Read the complete breakdown here: https://lnkd.in/d4arNVfB ____ 🎁 This issue is brought to you proudly by Parlant, your new coding agent: https://lnkd.in/dV3p8D8N

The most expensive words in business: "We've always done it this way." Especially when it comes to document processing - where outdated methods silently drain resources while critical information remains trapped. After implementing an intelligent document system for a financial services client, we found one capability that delivered outsized returns: automated data relationship mapping. What it is: Instead of just extracting text from documents, relationship mapping automatically connects information across your content ecosystem - linking related contracts, identifying conflicting terms, and spotting missing documentation. How it works:  Our data relationship agent:  → Identifies important entities in documents (clients, projects, deadlines)  → Maps connections between related documents automatically  → Flags inconsistencies between related materials  → Creates visual relationship maps for complex document sets The technology behind it: We built a specialized agent that uses natural language understanding to identify key entities and their relationships within documents. It then creates a graph database that maintains these connections, updating automatically as new documents enter the system. For our financial client, when a new contract amendment arrived, the system instantly connected it to the original agreement, highlighted changed terms, and flagged affected downstream documents - a process that previously took hours of manual review. Business impact: Our client transformed their document-heavy workflows: → Contract review time dramatically reduced → Missing documentation identified proactively → Risk exposure from inconsistent terms eliminated → Comprehensive audit trails created automatically How this applies to your business:  This capability delivers value wherever document relationships matter: For legal teams: Connect contracts, amendments, and supporting documents into coherent wholes. For compliance: Link policies to related procedures and verification evidence. For project management: Connect specifications, change orders, and delivery documentation. For operations: Link process documentation with training materials and compliance records. Quick path to results:  We can build a targeted proof-of-concept in 6-8 weeks using your actual documents, allowing you to:  → See relationship mapping working with your specific content  → Measure time saved in document processing  → Identify previously hidden document relationships  → Quantify reduced risk from comprehensive document visibility The key insight: Documents don't exist in isolation - their value multiplies when their relationships are understood. Automated relationship mapping brings this hidden value to the surface, transforming static files into a dynamic knowledge network. Is your team drowning in documents while missing critical connections between them? Let's talk about how relationship mapping could streamline your operations, with demonstrable results in weeks.