Automation of Routine Decisions

Improving organizational decision-making does not begin with a better algorithm. It begins with understanding the structure of the decisions themselves. In some domains, a missed decision is catastrophic. In others, it is routine. In aviation maintenance, a delay could ground an entire fleet. In online media, a wrong recommendation is just one of millions. The context matters. The cost of being wrong matters. And the path to better performance always starts by asking the right questions. I am often asked if machines will soon make all the decisions for us. It is a fair question. After all, artificial intelligence can now beat grandmasters at chess and Go. But here is what those games have that business rarely does: fixed rules, a known objective, and a perfect simulation. Business decisions do not come with those luxuries. They unfold over time. They involve uncertainty, competing objectives, shifting constraints, and limited information. No machine today can look at the next twelve months of your operations and tell you what trade-offs you will be forced to make, or what data you will wish you had. That is where Sequential Decision Analytics comes in. SDA is not about removing people from the loop. It is about putting them in the right part of the loop. People are brilliant at strategy, judgment, and asking the right questions. Machines are good at consistency, speed, and operating at scale. When we frame decisions properly, we can let people design the structure, and let machines run the mechanics. This is not about replacing managers with models. It is about elevating managers out of the noise. Too many decision-makers spend their time firefighting the same problems every day. SDA helps automate those repeatable decisions so that human energy is reserved for where it matters most: setting direction, adjusting policy, and learning from feedback. The real power of SDA is not in the algorithm. It is in the framework. We begin by identifying the decisions that get made, the uncertainties that affect those decisions, and the information we can act on. From there, we design a system that learns and adapts over time. This is not science fiction. It is how the best organizations already operate; only with more structure, more clarity, and more speed. Sequential Decision Analytics is not about handing over control. It is about regaining it. Not by guessing less, but by learning faster. Not by eliminating uncertainty, but by managing it with intent. And in the end, it is not the smartest system that wins. It is the one that learns the right lessons from the decisions it makes. #SDA #SupplyChain #OperationsResearch

Forget RPA. This Is What Intelligent Automation Looks Like Most automation projects fail at the boundary: when the task gets messy, the data gets unstructured, or human judgment is needed. This is why the Automation case from Samsung SDS is worth paying attention to. They didn’t stop at robotic task execution. They built a self-improving system that learns from judgment, not just repetition. Expense processing is a perfect example. Complex receipts. Inconsistent formats. Frequent exceptions. Traditional RPA can’t handle it. The process either breaks or reroutes to a human, creating a costly loop. Here’s what they did differently: • IDP extracts structured data from messy receipts with contextual accuracy • Gen AI (LLMs) steps in where rules fail, offering policy-based reasoning on ambiguous items • Automation Agent presents the results to a human, captures their final decision, and feeds it back into the system • Over time, the system stops escalating the same exceptions. It learns, adapts, and closes the loop. The results? • 80%+ reduction in processing time • F1 score of 0.90 across 1,400+ test cases • Higher compliance, lower errors, and happier finance teams But the real insight is this: automation isn’t about replacing judgment. It’s about codifying it - once, correctly, and at scale. If you’re advising clients or leading transformation programs, stop thinking of automation as “hands-off” work. Think of it as decision capture at the edge. That’s where the next real gains are hiding.

Stop Writing Documents. Start Creating "Decision Tools". You and your company don't need another PowerPoint. You need decision architectures that scale your expertise to thousands. The stakes are massive: Fortune 500 companies waste approximately 530,000 days of managers' time annually on ineffective decision-making processes—roughly $250 million in wasted wages per enterprise. The traditional knowledge worker creates documents that get read once then forgotten. Their insights—buried in pages of text—never influence decisions after the initial presentation. Meanwhile, forward-thinking professionals are building something radically different: AI-powered decision tools that work even when they don't. Old vs New World: • Old: Onboarding manual → New: Personalized AI guide that adapts to each new hire's questions and learning pace • Old: Project status report → New: Predictive dashboard that flags risks before they become problems • Old: Market research deck → New: Interactive simulator that tests messaging against different audience segments • Old: Employee handbook → New: Decision assistant that provides contextual policy guidance when needed Three AI-powered decision tools you can actually build today: 1. 🧠 Smart Decision Copilot: A custom GPT trained on your team's historical decisions that provides reasoning, precedents, and recommendations for new situations. Particularly powerful for pricing, resource allocation, or prioritization decisions. 2. 🤖 Adaptive Workflow Navigator: Using tools like Make or Zapier with AI integrations to create workflows that adjust based on inputs and outcomes, automating routine decisions while escalating edge cases with recommendations. 3. 📝 Evidence Collector: An AI assistant that continuously gathers relevant information from internal documents, Slack, email, and external sources to support decisions with real-time evidence rather than outdated assumptions. The career impact is profound: • You become the architect of systems, not just the producer of documents • Your expertise scales asymmetrically to your time investment • You build career capital that transcends any single project or role In a world where everyone has access to AI, the differentiator isn't who can write better prompts—it's who can design decision architectures that embed intelligence into everyday workflows. The opportunity is real: Only 7% of businesses currently report using data as a competitive tool within their organization. The other 93% are still drowning in PowerPoints. What recurring decision in your organization deserves to be transformed from a static document into a dynamic tool?

Why and How CFOs Should Embrace Automation Today We know that finance is complex, time-consuming, and under-resourced. I’ve been there with #nonprofits - balancing restricted funds, presenting to the Board, and trying to ensure every dollar aligns with the mission. That’s why I want to talk about something that’s been a game-changer. Automation. If your team is still spending hours on manual processes, it’s time to make the move toward automation. Here are four areas where automation can transform your work as a non-profit #CFO and free up your team to focus on higher-impact tasks: Accounts Payable (AP) & Accounts Receivable (AR): Automating AP/AR can drastically reduce processing time. Tools like Plooto allow you to manage invoices, approve payments, and track receivables - all in one place. Plus, they integrate seamlessly with accounting software, reducing errors and improving efficiency. Bank Reconciliations: Reconciliations don’t have to be a monthly headache. Tools like Floqast or Xero can match transactions automatically, flag discrepancies, and give you real-time updates. What used to take hours can now be done in minutes. Financial Planning & Analysis (FP&A): AI-powered FP&A tools like Fathom can streamline budgeting, forecasting, and scenario planning. They let you analyze “what if” scenarios, identify risks, and make data-driven decisions in real time. Board Presentation Decks: Stop using that same old template deck. Software like Gamma AI can generate financial dashboards and visual reports that are easy to customize and perfect for Board presentations. They’re clear, professional, and aligned with your organization’s mission story. How to Get Started: Identify one process that’s causing you sleepless nights, and research automation tools. Involve your team in the process. Their input will help you choose tools that meet your organization’s needs. Start small. Pilot one tool and measure the time saved before expanding automation efforts. #Automation isn’t just about saving time - it’s about improving accuracy, reducing stress, and enabling you to focus on what matters most: driving impact. Have you embraced automation in your finance processes?

🏦 Are banks wasting millions automating the wrong things? We see it often: 𝐜𝐡𝐚𝐬𝐢𝐧𝐠 𝐬𝐡𝐢𝐧𝐲 “𝐀𝐈” 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐯𝐞 𝟑𝟎 𝐦𝐢𝐧𝐮𝐭𝐞𝐬 𝐚 𝐰𝐞𝐞𝐤 instead of fixing the high-volume, repetitive work that truly drags the business down. The first type might free up 1 FTE. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐭𝐚𝐬𝐤𝐬 𝐜𝐨𝐮𝐥𝐝 𝐦𝐞𝐚𝐧 𝐝𝐨𝐮𝐛𝐥𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐫𝐞𝐯𝐞𝐧𝐮𝐞 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐚𝐝𝐝𝐢𝐧𝐠 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐡𝐞𝐚𝐝𝐜𝐨𝐮𝐧𝐭. 🏮 𝐒𝐨 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞? It’s not necessarily the jobs your team finds frustrating. Start with 𝐚 𝐬𝐢𝐦𝐩𝐥𝐞 𝐏𝐚𝐫𝐞𝐭𝐨 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐭𝐞𝐚𝐦’𝐬 𝐰𝐨𝐫𝐤𝐥𝐨𝐚𝐝 and focus on the top 4–5 tasks that usually account for 70–80% of total effort. 🏮 With today’s browser-use automation, you can reliably automate repetitive workflows across multiple tools if you have the right compliance layer. For example, 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐝𝐚𝐢𝐥𝐲 𝐬𝐚𝐧𝐜𝐭𝐢𝐨𝐧𝐬 𝐬𝐜𝐫𝐞𝐞𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐥𝐞𝐫𝐭 𝐝𝐢𝐬𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠. You can orchestrate Alloy, Unit21, and your core banking platform end-to-end in the browser, with no API integration required. 🏮 And here’s where it gets interesting: Jobs once thought to require “human judgment” are now automatable. Judgment is just pattern recognition, something modern LLMs can learn when trained on your historical decisions. For example, 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐛𝐨𝐫𝐝𝐞𝐫𝐥𝐢𝐧𝐞 𝐭𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐥𝐞𝐫𝐭𝐬. An LLM can be trained on past analyst decisions, apply the same reasoning to new cases, and flag only the truly ambiguous ones for human review. The key is to pair this with rule-based deterministic guardrails: ✅ 𝐄𝐥𝐢𝐠𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐜𝐡𝐞𝐜𝐤𝐬 before a decision ✅ 𝐏𝐨𝐬𝐭-𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐯𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 to catch edge cases This doesn’t mean you automate everything just because you can. The test remains: Will the time saved actually matter? If it’s not significant, it’s just digital busywork 💼 Banks that get this right will see efficiency gains that compound 🚀 Those that don’t will end up automating watering the office plants 🪴

Gartner Insights on How to Get Started With Automated Enterprise Architecture Governance and Assurance Enterprise architects with mature enterprise architecture governance and assurance practices seek to boost efficiency via automation. Get foresight into how Decision Intelligence Platforms with embedded AI can scalably support and automate quality architecture decisions, reducing costly errors and speeding value capture. 1️⃣ Target routine decisions for AI automation. 2️⃣ Utilize #DecisionIntelligence platforms.  3️⃣ Integrate reliable data sources. By Andrei Sachelarescu, David Pidsley and Philip Allega Published 23 May 2025 ( ID G00832766 ) 🔗 Link in comments

DFIR Automation Series: 4 Levels of Automation Some people fear that automation will limit their control of a situation. While that can happen, it doesn't need to. Automations can still let you make the important decisions. I tend to think of four levels of automation: 1: None. You need to do everything. You need to realize a decision needs to be made (!), what the options are, and what to choose. 2: Prompt. The app realizes a decision needs to be made, gives you options, and lets you pick. 3: Recommend. Same as Prompting, but it will suggest an option. You can choose it or overrule it. 4: Full: The app automatically does its recommendation. You don't get involved. Examples of these can be found in lots of tools: - Tools that hide NSRL files from the UI are fully automated. The tool is deciding to hide the files based on their NSRL existence. - Tools that flag pictures based on their hash in a "bad" database are often "Recommending". You get to make the final call. - Many tools automatically parse artifacts (such as Prefetch) if they detect them. This is fully automated. In Cyber Triage, we focus a lot on Recommending. It assigns a Bad or Suspicious score to artifacts as a recommendation, but you can overrule it. Having no automation means investigations are slow and error prone. Investigators are going to not realize they should make a decision about something. I think the ideal scenario is one where its: * Fully automated for low risk decisions * Recommendation for higher risk decisions