Automation In The Workplace

Last week, I talked about the possibilities of AI to make work easier. This week, I want to share a clear example of how we are doing that at HubSpot. We’re focused on helping our customers grow. So naturally, we take customer support seriously. Whether it’s a product question or a business challenge, we want inquiries to be answered efficiently and thoughtfully. We knew AI could help, but we didn’t know quite what it would look like! We first deployed AI in website and support chat. To mitigate any growing pains, we had a customer rep standing by for questions that came through who could quickly take the baton if things went sideways. And, sometimes they did. But we didn’t panic. We listened, we improved, and we kept testing. The more data AI collects, the better it gets. Today, 83% of the chat on HubSpot’s website is AI-managed and our Chatbot is digitally resolving about 30% of incoming tickets. That’s an enormous gain in productivity! Our customer reps have more time to focus on complex, high touch questions. AI also helps us quickly identify trends—questions or issues that are being raised more frequently—so we can intervene early. In other words, AI has not just transformed our customer support. It has elevated it. So, here is what we learned: Don’t panic if customer experience gets worse initially! It will improve as your data evolves. Evolve your KPIs and how you measure success- if AI resolves typical questions and your team resolves tricky ones, they will need more time. Use AI to elevate your team's efforts How are you using AI in support? What are you learning? 

Building useful Knowledge Graphs will long be a Humans + AI endeavor. A recent paper lays out how best to implement automation, the specific human roles, and how these are combined. The paper, "From human experts to machines: An LLM supported approach to ontology and knowledge graph construction", provides clear lessons. These include: 🔍 Automate KG construction with targeted human oversight: Use LLMs to automate repetitive tasks like entity extraction and relationship mapping. Human experts should step in at two key points: early, to define scope and competency questions (CQs), and later, to review and fine-tune LLM outputs, focusing on complex areas where LLMs may misinterpret data. Combining automation with human-in-the-loop ensures accuracy while saving time. ❓ Guide ontology development with well-crafted Competency Questions (CQs): CQs define what the Knowledge Graph (KG) must answer, like "What preprocessing techniques were used?" Experts should create CQs to ensure domain relevance, and review LLM-generated CQs for completeness. Once validated, these CQs guide the ontology’s structure, reducing errors in later stages. 🧑⚖️ Use LLMs to evaluate outputs, with humans as quality gatekeepers: LLMs can assess KG accuracy by comparing answers to ground truth data, with humans reviewing outputs that score below a set threshold (e.g., 6/10). This setup allows LLMs to handle initial quality control while humans focus only on edge cases, improving efficiency and ensuring quality. 🌱 Leverage reusable ontologies and refine with human expertise: Start by using pre-built ontologies like PROV-O to structure the KG, then refine it with domain-specific details. Humans should guide this refinement process, ensuring that the KG remains accurate and relevant to the domain’s nuances, particularly in specialized terms and relationships. ⚙️ Optimize prompt engineering with iterative feedback: Prompts for LLMs should be carefully structured, starting simple and iterating based on feedback. Use in-context examples to reduce variability and improve consistency. Human experts should refine these prompts to ensure they lead to accurate entity and relationship extraction, combining automation with expert oversight for best results. These provide solid foundations to optimally applying human and machine capabilities to the very-important task of building robust and useful ontologies.

Are you confused about how to improve your productivity as a PM with AI? Here are the 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗜 𝘂𝘀𝗲, and the 3 that I('d) use daily: AI is everywhere now. Your feed, your news, your software, probably your fridge. But not all of it is built the same, and a lot of AI for PMs is smoke and mirrors, put together for marketing more than for solving a real product problem. So I went on a mission. I identified tools that actually improved my own work output over the last few weeks. Before anything gets adopted, it has to clear all five criteria: • Does it solve a real PM problem, • Does it produce trustworthy output every time, • Can a PM learn it in a day, • Does it fit the workflow you already have, • Was it built for PMs, or is it a generic tool The full stack can be found here: https://lnkd.in/d43xsP5N But here are the three I'd consider best. 1) Wispr Flow (dictation) This is the rarest kind of tool because it makes every other tool on the list faster. System-wide voice-to-text that works in any app your cursor lands in. It strips filler words, fixes punctuation, and learns your vocabulary so it stops mangling your product names. It made my text input twice as fast. Try it for a week, and your keyboard will start to catch dust. Link: https://lnkd.in/dZ4VeSvc? 2) Thumba.ai (tickets and user stories) Thumba generates complete user stories, acceptance criteria, and test scenarios from plain English or even a wireframe image, then pushes straight into Jira and Azure DevOps. Best for teams shipping a high volume of tickets who want consistent, well-structured stories without the manual overhead. Did I mention it builds reliable testing plans as well? Link: www.thumba.ai 3) Granola (meeting notes and action items) How did I ever sit in a meeting without an AI notepad? Granola uses your actual PC audio, transcribes in real time, and hands you a structured summary with action items, decisions, and key moments, searchable across every meeting you've ever had. If I had to keep only one AI tool, it would be a genuinely hard call between this and Wispr Flow. That's the highest compliment I can give a tool. One honest note, same as always: some of these have sponsored my posts in the past, but none of them asked to be on this list, and every spot is earned. And what's gold for me might be noise for you, depending on your company and how your team works. Try one on real work and keep what earns its place. What's already in your daily PM stack? And which tool should I look at next? Let me know in the comments. #ai #productmanagement #productmanager

Will Accounting Be Replaced? 🤖 💼 Everyone's asking if AI will replace accountants... Let me settle this once and for all. ➡️ WHAT WILL TRANSFORM ADVISORY SERVICES are becoming the heart of what we do. Gone are the days when accountants just crunch numbers. Now we guide strategic decisions using real data insights. Companies need advisors who understand both numbers AND business strategy. FORENSIC ACCOUNTING gets supercharged with advanced analytics. Finding fraud used to be like searching for a needle in a haystack... With AI-powered anomaly detection, we spot patterns humans would miss. The fraudsters are getting smarter, but so are our tools. AUDIT & RISK ASSESSMENT will never go away, but everything about it is changing. Instead of sampling transactions once a year, we're moving to continuous auditing with real-time data. AI review systems flag issues as they happen, not months later when it's too late. FINANCIAL ANALYSIS & FORECASTING is where accountants shine brightest. Sure, AI can run calculations, but humans bring context to numbers. Our forecasting is getting enhanced by predictive analytics and scenario modeling that processes variables faster than ever before. CLIENT COMMUNICATION is shifting completely. We're moving from transaction processors to trusted advisors. ➡️ WHAT WILL BE REPLACED Let's be honest... some parts of accounting are tedious and perfect for automation. MANUAL DATA ENTRY is already on its way out. AI-driven data capture and OCR tools process invoices and receipts in seconds, without the errors humans make after hours of monotonous work. ROUTINE BOOKKEEPING tasks are getting automated through cloud accounting software. Bank feeds, automatic categorization, and machine learning mean the days of manually reconciling every transaction are numbered. BASIC TAX PREPARATION for standard situations will be handled by smart platforms. E-filing tools get smarter every tax season. The complex tax strategy work? That's still all us. INVOICE MATCHING & RECONCILIATION is perfect for automation. AI bots can match thousands of invoices to purchase orders in minutes, with real-time reconciliation systems keeping everything in sync. COMPLIANCE MONITORING no longer needs accountants to manually check every rule. Automated alerts and built-in compliance checks flag issues instantly, letting us focus on solving problems rather than finding them. ➡️ THE FUTURE ACCOUNTANT The accountants who will thrive aren't fighting against technology... They're embracing it. The future belongs to those who combine technical accounting knowledge with: - Strategic thinking - Business acumen - Technology fluency - Communication skills === What parts of your accounting job do you think will change the most with AI? Which skills are you developing to stay ahead? Join the discussion in the comments below 👇

If your finance team is still doing these tasks manually in 2026… You’re paying twice. Once in salary. Once in lost capacity. Here are 10 accounting tasks CFOs should seriously consider automating: • Tax filing • Expense tracking • Vendor payments • Payroll processing • Invoice generation • Financial reporting • Bank reconciliation • Expense approvals • Receipt management • Cash flow forecasting None of these create competitive advantage. They create stability. And stability should be automated. The real question isn’t: “𝘊𝘢𝘯 𝘸𝘦 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘵𝘩𝘪𝘴?” It’s: “𝘞𝘩𝘢𝘵 𝘸𝘰𝘶𝘭𝘥 𝘰𝘶𝘳 𝘧𝘪𝘯𝘢𝘯𝘤𝘦 𝘵𝘦𝘢𝘮 𝘥𝘰 𝘪𝘧 30–50% 𝘰𝘧 𝘦𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯 𝘸𝘰𝘳𝘬 𝘥𝘪𝘴𝘢𝘱𝘱𝘦𝘢𝘳𝘦𝘥?” More scenario analysis. More performance dialogue. More capital allocation focus. More forward-looking insight. Automation doesn’t reduce the importance of finance. It reallocates it. The CFOs who win won’t be the ones with the most dashboards. They’ll be the ones who turned operational efficiency into strategic capacity. Which of these 10 areas still consumes too much manual time in your organization? P.S. Automation is not about cutting heads. It’s about upgrading impact.

Most AI tool lists miss the point. The advantage doesn’t come from knowing more tools. It comes from knowing where they fit in your workflow. Right now most people use AI like this: → Try a tool → Generate something → Move on No structure. No repeatability. So the productivity gains stay small. The real leverage appears when you treat AI tools like a stack, not a collection of apps. Almost every modern AI workflow fits into four layers. If you understand these layers, you can build systems that run every week without starting from scratch. 1️⃣ Thinking layer Tools that help you clarify problems and structure ideas. → ChatGPT → Claude Use them to: → research unfamiliar topics → break down complex problems → outline strategies and plans → stress-test ideas before execution Most people jump straight to creation. The real value often starts one step earlier: better thinking. 2️⃣ Creation layer Tools that turn ideas into assets. → writing tools (Jasper, Writesonic) → design tools (Canva AI, Flair) → image tools (Midjourney, DALL-E, Stable Diffusion) → video tools (Runway, HeyGen, Synthesia) This layer turns raw ideas into: → presentations → visuals → videos → marketing assets → documentation Think of it as production infrastructure for knowledge work. 3️⃣ Automation layer Tools that connect steps together. → Zapier → Make → Bardeen Instead of repeating tasks manually, these tools: → move information between systems → trigger actions automatically → remove repetitive work Example: Research → draft → create visuals → publish. Automation turns that into a repeatable pipeline. 4️⃣ Deployment layer Tools that deliver work to customers and teams. → websites (Framer, Durable) → chatbots (Chatbase, SiteGPT) → marketing tools (AdCreative, Simplified) This is where work becomes: → websites → marketing campaigns → customer experiences → digital products Without deployment, great AI output never reaches the real world. If you run a business or lead a team, here’s a simple playbook. Step 1 Pick one tool per layer. You don’t need ten tools doing the same job. Step 2 Design one repeatable workflow. Example: → research with ChatGPT → draft content → create visuals in Canva → automate publishing with Zapier Step 3 Automate the steps that repeat every week. Anything you do more than three times should become a system. Step 4 Improve the workflow over time. Small improvements compound faster than constantly switching tools. The people getting the most value from AI right now are not the ones testing every new tool. They are the ones building simple systems that run every day. Tools will change. Workflows compound. 💾 Save this if you’re building your AI stack. ♻️ Repost to help others move from experimenting with AI to actually using it in their work. ➕ Follow Gabriel Millien for practical insights on AI execution and building real leverage with AI. Image credit: Aditya Goenka

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

𝗔𝗜 𝗖𝗼𝗮𝗰𝗵𝗶𝗻𝗴: 𝘠𝘰𝘶𝘳 𝘗𝘔 𝘗𝘰𝘸𝘦𝘳 𝘛𝘙𝘐𝘖 Three tools can transform how you plan, lead, and grow. Lets look at how you can put ChatGPT, LinkedIn Learning's Role Play Coach, and PMI Infinity to work for you...TODAY. 1️⃣ 𝗖𝗵𝗮𝘁𝗚𝗣𝗧: Your On-demand Strategist 𝘜𝘴𝘦 𝘪𝘵 𝘢𝘴 𝘢 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘤𝘰𝘢𝘤𝘩 𝘧𝘰𝘳 𝘳𝘢𝘱𝘪𝘥 𝘱𝘳𝘰𝘣𝘭𝘦𝘮-𝘴𝘰𝘭𝘷𝘪𝘯𝘨 & 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘵𝘳𝘢𝘯𝘴𝘭𝘢𝘵𝘪𝘰𝘯. ⮕ Draft project plans instantly, "𝘾𝙧𝙚𝙖𝙩𝙚 𝙖 3-𝙢𝙤𝙣𝙩𝙝 𝙧𝙤𝙡𝙡𝙤𝙪𝙩 𝙥𝙡𝙖𝙣 𝙛𝙤𝙧 𝙣𝙚𝙬 𝘾𝙍𝙈 𝙨𝙤𝙛𝙩𝙬𝙖𝙧𝙚. 𝙄𝙣𝙘𝙡𝙪𝙙𝙚 𝙥𝙝𝙖𝙨𝙚𝙨, 𝙩𝙖𝙨𝙠𝙨, 𝙢𝙞𝙡𝙚𝙨𝙩𝙤𝙣𝙚𝙨 𝙛𝙤𝙧 𝙖 30-𝙥𝙚𝙧𝙨𝙤𝙣 𝙩𝙚𝙖𝙢." [Refine the output in your PM tool.] ⮕ Translate technical jargon. When devs say "API authentication error," ask ChatGPT to explain it for executive stakeholders in plain language. ⮕ Prep for tough convos, "𝙍𝙤𝙡𝙚-𝙥𝙡𝙖𝙮 𝙖 𝙨𝙘𝙤𝙥𝙚 𝙘𝙧𝙚𝙚𝙥 𝙙𝙞𝙨𝙘𝙪𝙨𝙨𝙞𝙤𝙣 𝙬𝙞𝙩𝙝 𝙖 𝙙𝙚𝙢𝙖𝙣𝙙𝙞𝙣𝙜 𝙨𝙥𝙤𝙣𝙨𝙤𝙧. 𝙋𝙪𝙨𝙝 𝙗𝙖𝙘𝙠 𝙥𝙧𝙤𝙛𝙚𝙨𝙨𝙞𝙤𝙣𝙖𝙡𝙡𝙮." ⮕ Brainstorm risk responses, "𝙇𝙞𝙨𝙩 5 𝙢𝙞𝙩𝙞𝙜𝙖𝙩𝙞𝙤𝙣 𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙚𝙨 𝙛𝙤𝙧 𝙫𝙚𝙣𝙙𝙤𝙧 𝙙𝙚𝙡𝙖𝙮𝙨 𝙤𝙣 𝙖 $500𝙆 𝙘𝙤𝙣𝙨𝙩𝙧𝙪𝙘𝙩𝙞𝙤𝙣 𝙥𝙧𝙤𝙟𝙚𝙘𝙩." 2️⃣ 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗼𝗹𝗲 𝗣𝗹𝗮𝘆: Practice Under Pressure 𝘛𝘩𝘪𝘴 𝘈𝘐-𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘧𝘦𝘢𝘵𝘶𝘳𝘦 𝘭𝘦𝘵𝘴 𝘺𝘰𝘶 𝘳𝘦𝘩𝘦𝘢𝘳𝘴𝘦 𝘥𝘪𝘧𝘧𝘪𝘤𝘶𝘭𝘵 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯𝘴 𝘷𝘪𝘢 𝘵𝘦𝘹𝘵 𝘰𝘳 𝘷𝘰𝘪𝘤𝘦 𝘸𝘪𝘵𝘩 𝘢 𝘤𝘶𝘴𝘵𝘰𝘮𝘪𝘻𝘢𝘣𝘭𝘦 𝘈𝘐 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭𝘪𝘵𝘺. ⮕ Select scenarios like giving feedback on prioritization or navigating conflict. ⮕ Adjust the AI's tone (ex. more defensive, agreeable, or professional). ⮕ After each session, review feedback for clarity, empathy, and goal achievement, then follow course recommendations to close skill gaps. 3️⃣ 𝗣𝗠𝗜 𝗜𝗻𝗳𝗶𝗻𝗶𝘁𝘆: Your PM-Specific AI Coach 𝘉𝘶𝘪𝘭𝘵 𝘰𝘯 𝘎𝘗𝘛-4𝘰 𝘸𝘪𝘵𝘩 15,000+ 𝘱𝘪𝘦𝘤𝘦𝘴 𝘰𝘧 𝘷𝘦𝘵𝘵𝘦𝘥 𝘗𝘔 𝘤𝘰𝘯𝘵𝘦𝘯𝘵, 𝘐𝘯𝘧𝘪𝘯𝘪𝘵𝘺 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘺𝘰𝘶𝘳 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘥𝘦𝘦𝘱𝘭𝘺. ⮕ Upload project docs for tailored analysis. Get summaries, risks flagged, and actionable insights extracted. ⮕ Use "Task Mode" to generate smart templates, checklists, and status reports aligned with PMI standards. ⮕ Leverage real-time integration with Jira, Trello, or MS Project for centralized monitoring. ⮕ Access personalized recommendations and sustainable PM best practices based on your project specifics. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: ChatGPT handles speed. LinkedIn sharpens soft skills. PMI Infinity delivers PM-grade precision. Stack all three. 📌 Which of these AI-powered tool(s) gets your vote? [𝘋𝘳𝘰𝘱 𝘺𝘰𝘶𝘳 𝘧𝘢𝘷𝘰𝘳𝘪𝘵𝘦 𝘱𝘳𝘰𝘮𝘱𝘵 𝘣𝘦𝘭𝘰𝘸.] #ArtificialIntelligence #ProjectManagement #AICoaching

𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗘𝗿𝗼𝘀𝗶𝗼𝗻 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗽𝗹𝗮𝗰𝗲𝗻𝗰𝘆 𝗥𝗶𝘀𝗸 When AI handles duplicate detection, impact analysis, and traceability automatically, junior configuration managers never develop pattern-recognition skills by performing these tasks manually. The efficiency gains are real, but the cost manifests years later when organizations discover their CM professionals can't perform critical analysis without algorithmic assistance. Aviation already confronted this. Research on automation-induced skill fade shows that pilots who rely heavily on autopilot exhibit degraded manual flying skills. Recent studies show that pilots can lose manual-flying skills in as little as two months without practice. A 2025 survey revealed even experienced flight instructors struggle with basic manual flying when automation is disabled. One senior instructor, an examiner, who was asked to fly manually, "had a tough time doing it." The same dynamic threatens configuration management. When AI consistently provides correct answers, humans stop questioning those answers, stop developing judgment to recognize when AI recommendations are wrong, and gradually lose expertise that makes human oversight valuable. Consider requirements traceability. With AI generating trace links at 94% accuracy, reviews should be simple. However, a reviewer who manually creates links must evaluate semantic relationships and architecture, and recognize missed dependencies, whereas someone who only reviews AI suggestions relies on pattern matching: Is this reasonable? Experienced configuration managers develop intuition about which changes need scrutiny, which stakeholders to engage early, and where documentation gaps reveal issues. This intuition comes from mistakes and experience, whereas AI that prevents errors can hinder learning from them. Several approaches have emerged: 𝗚𝗿𝗮𝗱𝘂𝗮𝘁𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Junior managers perform tasks manually before gaining AI access, developing pattern recognition before algorithmic support. 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗺𝗮𝗻𝘂𝗮𝗹 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Require manual requirements tracing for at least one component per quarter, ensuring the ability to perform core functions when AI isn't available. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀: When an AI system flags a potential impact between an ECU firmware change and thermal management, the explanation teaches a pattern they can apply independently. 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝘆 𝗴𝗮𝘁𝗲𝘀: Require demonstrating manual competency before using AI for critical functions. The goal isn't to prevent AI adoption, it's to ensure AI enhances, rather than replaces, human expertise. If your AI system went offline tomorrow, how many configuration managers could perform critical analysis manually, and how would you know before it's too late? What's your approach to preserving expertise while adopting automation? #ConfigurationManagement  #CM2 #ArtificialIntelligence #AI #PLM

A company you may never have heard of called Stacks just raised a $23 million Series A round to bring Agentic AI into the the CFO's office. They're building a platform for enterprise finance teams, aimed at taking the grind out of reconciliations, journal entries, and the month-end close. They say they are already saving finance teams over 100,000 hours per year, with more than 30 enterprise customers on board. Having spent most of my career in financial services, the real problem Stacks is solving is that finance data is scattered across ERPs, spreadsheets, data lakes, and legacy systems. As a result, every “why did this number change?” at month end turns into a manual hunt. I've been there with my face in a green screen, and it's painful. But Stacks is tackling that by building a finance data layer first, then letting agents run repeatable work on top of it. The sequencing here matters, because automation fails very quickly when the data foundation isn't in place; hence why so many AI initiatives aren't baring fruit. So why is agentic AI the right fit here? The monthly, quarterly, or year-end close is not one task. It's a chain of tasks with handoffs, approvals, exceptions, and constant context switching. Agents do well when the tasks are in a sequence, not a single prompt. And we're talking tasks here, not entire roles. I think Agentic AI in the contact center as the enterprise warm-up act puts an interesting perspective on the Finance play. Finance is definitely a higher-stakes arena because every single output touches controls, audit trails, and accountability. So if you lead a function that lives in spreadsheets and swivel-chair workflows, your world is about to get rocked. So everyone from Procurement, Revenue Ops, Payroll, Risk & Compliance, and Internal Audit should be taking their company's contact center leader out for lunch soon to pick their brain on the impact they're about to experience. So, who do you think is next in your company, once Finance stops being spreadsheet-first and becomes system-first? #finance #ai #agenticai #futureofwork