Automation In Project Management

Learning AI agents might be the best career move for procurement managers right now. But many still confuse them with chatbots. So, what is an AI agent? I loved Bill Gate's definition: “Agents are smarter. They’re proactive – capable of making suggestions before you ask for them. They improve over time because they remember your activities and recognise intent and patterns in your behavior. Based on this information, they offer to provide what they think you need, although you will always make the final decisions.” An AI agent can: • Read information and provide you answers fast • look at different options and choose the best ones based on what it's been taught • do boring or repetitive jobs for you, like sorting e-mails, booking meetings or filling out forms • over time get better at its job by learning from mistakes and adjusting how it works Recently, procure tech companies started using a new term: Agentic AI. Supposedly, even more autonomous than AI agents. My take? Don’t waste time on academic debates. AI agents are becoming increasingly autonomous. What we should care about is their actual use cases to improve our output in procurement. For technical discussions, AI agents working together can be called "multi-agent systems" --- Here are the key AI Agent Use Cases in Procurement in 2025, in case you’re feeling left behind. 1️⃣ Automating Routine Procurement Tasks → Purchase order creation → Invoice processing and matching → Vendor onboarding documentation → Contract renewals reminders 2️⃣ Spend Analytics & Cost Optimization → Detect maverick spending → Recommend better pricing contracts based on historical trends → Benchmark supplier pricing against market rates 3️⃣ Supplier Risk Monitoring & Due Diligence → Flagging vendors linked to financial instability or ESG controversies → Proactively notifying teams about changes in risk profiles 4️⃣ Automating RFP, RFQ, and RFI Processes → Faster supplier selection cycles → Consistency in evaluation criteria → Automated bid comparison and recommendations 5️⃣ Intelligent Contract Management → Flagging high-risk clauses → Notifying procurement about upcoming contract renewals → Recommending standard clause templates Emerging Use Cases: → Generative AI for Procurement Document Drafting: AI writing scopes of work, RFP templates, and vendor emails. → AI Agents for ESG Compliance: Real-time monitoring of supplier ESG claims against external data sources. →AI Copilots for Category Managers: Assisting in supplier negotiations with data-backed arguments and scenario analysis. ---- What do you think is the most valuable use of an AI agent right now?

Transformation thrives when people are empowered to make the most of technology. 🚀 My recent visit to the Bosch production facility for automotive and eBike drives in Miskolc, Hungary, showcased this perfectly. I was deeply impressed to see firsthand how their progress in digitalization and the implementation of the Bosch Manufacturing and Logistics Platform (BMLP) is reshaping their manufacturing operations. BMLP is a globally standardized, open IT platform that connects all stages of production and logistics. During an insightful plant tour, I observed a successful example of how the platform leads to significant improvements in efficiency, quality, and data transparency across the plant. What stood out most was seeing the passionate and enthusiastic team at Miskolc leverage this technology in action and achieving great results towards operational excellence. Here are three key areas where BMLP is contributing to the plant’s digital transformation success, powered by our NEXEED IAS: 1️⃣ Enhanced Efficiency & Reduced Downtime: The module Shopfloor Management enables a closed PDCA cycle in production by consequent integration of all relevant information in one system. This leads to quick reaction in case of deviations to minimize downtimes and safeguard the daily performance targets.   2️⃣ Improved Product Quality: Continuous monitoring throughout production stages helps the team identify issues early, ensuring top-tier quality while driving process improvements.   3️⃣ Change Management: Change management plays a crucial role in digital transformation within a plant. As seen in Miskolc, effectively managing change ensures that the workforce is engaged, and equipped to embrace new technologies, driving sustainable success. In Miskolc we have seen solutions using gamification that help to involve all associates, making the transition both engaging and effective.   I was also excited to see AI in action with a live demo of 8D Analysis using GenAI, cutting failure analysis time by half. By automating the root cause analysis process, engineers are now spending less time on administrative tasks and more on proactive problem-solving – a great example of how technology empowers people. Beyond the production lines, the most rewarding part of the visit was engaging with the team. Their passion for digitalization, commitment to upskilling, and their drive for innovation truly brought home the message: technology is only as strong as the people behind it. A special thank you to the entire Miskolc team for the inspiring discussions and warm welcome – along with Volker Schilling, Klaus Maeder, Joerg Klingler, Volker Schiek, Norbert Jung, Stephan Brand, Aemen Bouafif, and everyone who joined us on this great trip. I’m excited to see what’s next on this incredible digitalization journey!

It’s one of the most radical changes AI agents bring to financial services - and yet it’s barely discussed. Financial services have historically been built around systems, processes, and workflows: • Products, pricing, and journeys were designed upfront with little room for variation. • Decisioning relied on fixed rules and batch processing. • Customer understanding was fragmented across systems with no coherent view. As a result, the 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 has always reflected system capabilities vs. customer needs: • Broadcast-first: one message to many customers • Limited channels used for different things: transaction alerts, compliance notifications, marketing campaigns, etc. • Personalization limited to basic variables (name, balance threshold, segment). 𝗡𝗼𝘄 𝗔𝗜 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗲𝘁-𝘂𝗽: • Real-time, contextual, continuously updated decisions • Intelligence embedded directly into customer flows • Systems that trigger and execute actions automatically And that changes communication too, as it now needs to be part of the decision loop: • Context-aware (driven by what happened) • Intent-aware (based on the customer’s purpose) • Adaptive (messages change based on response) • Channel-native (tone, format, and interaction adapted per channel) • Two-way by default 𝗧𝗵𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆: Move from broadcast messaging to agentic, intelligence-driven communication. • Millions of customers • Each receiving: 1) The right message 2) At the right time 3) On the right channel 4) With the right next action Examples: • Response-driven fraud warnings • Payment reminders adjusted by timing and tone • Adaptive onboarding flows Players today need communication platforms that can execute across channels, at scale and in real time. Sinch is one such example acting as the execution layer between internal systems and customer-facing interactions: • Multi-channel message delivery based on system and agent instructions. • High message volumes • Two-way interactions, feeding customer responses back into automated flows 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁: • One-way interactions are a thing of the past. Sinch data shows that nearly one in three consumers find them annoying. • AI is already creating new types of communication • Agent-to-agent communication is likely to be the next major shift 𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: What are the biggest showstoppers preventing mass communication from scaling? Opinions: my own, Graphic sources: BCG, Panagiotis Kriaris #SinchPartner https://lnkd.in/d5wT5tNQ

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

How we saved 10+ hours weekly by giving finance a simple interface. Our finance team was processing invoices the same way for years: 1. Email attachments → 2. Manual download → 3. Print → 4. Physical signature → 5. Scan → 6. Manual data entry The entire cycle took 3-5 days. The request to "build a proper approval system" kept getting deprioritized—it felt like a multi-month project. We reframed the problem: We didn't need a complex system. We just needed to connect two things: the data from our accounting software's API and a simple list where the right people could click "Approve" or "Reject." What actually got built: • A single-page app that pulls unpaid invoices automatically • Logic that routes invoices over $5k to directors, others to managers • A comment field for rejections • A basic audit log showing who approved what and when What changed: ✅ Approvals now happen in under 24 hours ✅ The finance team stopped chasing paper trails ✅ Vendors get paid faster ✅ Every decision is logged automatically The takeaway: Sometimes "digital transformation" isn't about big platforms. It's about giving a team one less PDF to manage by building a simple, focused tool that sits on top of the data they already use. What's the most stubborn, repetitive task in your team's workflow? Often the highest-impact tools are the smallest ones that remove a single point of friction. https://uibakery.io/ #ProcessAutomation #FinanceTech #OperationalEfficiency #DigitalTransformation

The best Program Managers don’t work harder. They leverage AI to work smarter. Here are 7 ChatGPT prompts to 10x your productivity with AI 👇🏼 __ 1/ Project Planning & Timeline Creation Prompt: "Act as a project management assistant. Create a detailed project plan for [Project Name], including key phases, timelines, milestones, and responsible team members. Align it with strategic business goals." ✅ Use it when: you’re kicking off a new project or updating leadership. __ 2/ Risk Assessment & Mitigation Prompt: "Analyze potential risks for [Project Name] across scope, resources, and timeline. Recommend mitigation strategies for each identified risk. Present it in a table format." ✅ Use it when: you need to anticipate blockers before they show up. __ 3/ Stakeholder Update Email Prompt: "Write a stakeholder update email for [Project Name] with a clear summary of progress, blockers, decisions made, and upcoming actions. Keep the tone professional, transparent, and concise." ✅ Use it when: you’re preparing your weekly or monthly status updates. __ 4/ Daily Standup Summary Prompt: "Summarize the key updates from today’s standup meeting for [Project Name], highlighting who did what, blockers discussed, and next steps. Write it in Slack message format." ✅ Use it when: async communication is the norm and clarity is key. __ 5/ Sprint Planning Prompt: "Act as an Agile project coach. Help plan a 2-week sprint for [Team/Project Name], breaking down user stories, assigning effort estimates, and suggesting sprint goals." ✅ Use it when: you’re prepping for sprint kickoff and want structure. __ 6/ Change Management Strategy Prompt: "Outline a change management plan for introducing [New Process/Tool] in [Project Name]. Include communication strategy, training needs, and tactics for stakeholder buy-in." ✅ Use it when: your team is shifting how they work and needs a roadmap. __ 7/ Program Retro Prompt: "Facilitate a project retrospective for [Project Name]. Identify what went well, what could improve, and key takeaways. Organize it into a simple report format." ✅ Use it when: you’re closing out a project and want to capture insights. __ ⚡Want to check out my LinkedIn Learning course where we dive even deeper: “The AI-Driven Project Manager: 10x Your Productivity with Generative AI.” ▶️ Watch it for free (for a limited time): https://lnkd.in/epd2bYdS

Are you still wasting time collecting site data from 5 different portals? Building footprints from one source. Wind patterns from another. Topography? Probably buried in a PDF somewhere. It’s 2025, and with AI tools around, we shouldn’t be spending hours stitching datasets together just to start a design. I use Aino to cut through the noise and get clean, reliable data fast. Here’s what makes it work so well for site studies: 👉 Building footprints and building use mapped in seconds 👉 Adjustable building heights visualised in a gradient 👉 Real-time wind movement overlays 👉 Street network identified and simplified 👉 Topography with contour clarity 👉 Open spaces sorted into categories My favourite features: ✅ Traffic Heatmaps ↳ See where bottlenecks occur and plan circulation with confidence. ✅ Clip and Export ↳ Crop any area and export in PNG, SVG, PDF, or DXF for design workflows. With Aino, you spend less time on data chaos and more time designing with clarity. Want to see how it works in real projects? I’ve added a short tutorial video below.

650. That’s the staggering number of companies offering conversational AI solutions for sales and service. The flood isn’t slowing: each week brings new entrants or announcements. A year ago, the market was already crowded; today, the latest wave of AI technologies has further lowered barriers to entry, fueling an unsustainable proliferation. Beyond the three hyperscalers, only a handful of providers have surpassed $100M in ARR. I spent the summer making sense of the mayhem. The result: nine categories mapped to the core jobs-to-be-done. Customer service and support solutions fall into four categories: • Virtual Agents. IVAs and their AI evolution operate across digital channels, handling transactional interactions and escalating to humans when necessary. • AI Answer Engines. These retrieve and format answers from knowledge bases. Generative AI has dramatically improved precision for informational inquiries. • Conversational IVR and Voice Agents. Voice remains complex; these agents primarily handle transactional interactions. • Conversational Engagement and Outreach Agents. These manage outbound communications across voice, SMS, and messaging channels, complying with regulations. Historically transactional, they increasingly enable dynamic engagement. Sales solutions are grouped into three categories: • Conversational Commerce & Concierge Agents. Mature agents replacing traditional chat with conversational experiences across pre- and post-sales. "Concierge" reflects their versatility in guiding customers seamlessly. • Autonomous SDRs (Sales Development Reps). Focused on complex B2B scenarios, they enrich and qualify leads, route them to sellers, and schedule appointments. Among the most mature AI applications for B2B sales. • Autonomous BDRs (Business Development Reps). These drive outbound sales motions where relevance is critical. Complex to implement and scale, they work best in highly targeted scenarios where personalization is flawless. Some providers span the full spectrum of service use cases and Conversational Commerce & Concierge Agents. Rather than duplicating them across categories, I group them under Conversational AI Platforms, relying on robust capabilities to design, deploy, and continuously improve applications and agents. Customer Support Automation is an emerging platform category, tailored for handling support requests and a natural fit for GenAI. These platforms deliver full resolutions when possible, automate workflows, and assist agents with context and guidance. It’s a mature use case for Agentic AI, with many providers publicly demonstrating transformative results. The visual landscape below captures this segmentation. A few vendors will emerge as true platforms, while others will focus on niches or become embedded in broader applications. The market remains in motion, and I welcome perspectives on what I may have overlooked. #conversationalai #agenticai #cx #salestech

Are you prepared for the algorithm-led future of #Amazon's Vendor Management? 🤖 Amazon buyers are becoming a rare breed, and brands must adapt to thrive in an increasingly automated environment. To cut costs, Amazon is pushing ahead with its offshoring initiatives and starting to shape its future without account-specific Vendor Managers: - Layoffs have reduced the VM community by -15% - AVS is getting offshored to Eastern Europe and India - Amazon actively pushes account management tasks to brands - Automation is now driving pricing, listing, and CRAP decisions - Pan-EU and North American regionalisation is here to stay Interestingly, most vendors I talk to ignore this trend altogether. They think their brand is too important for Amazon to neglect. Yet Amazon quietly transfers most of the manual account management tasks to suppliers and automates the rest, leading to a future where algorithms hold the reins. How should brands respond? By focusing on 3 key areas: 𝟭- 𝗟𝗲𝗮𝗻 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Amazon has already increased its regional management of European vendor accounts. Instead of a dedicated buyer contact by market, brands mainly navigate their business at pan-European level. Given Amazon's past layoffs, it's unlikely that more VM resources will be deployed on vendor accounts anytime soon. Instead, brands must adapt to this regional focus to ensure they don't duplicate tasks across markets, when only one Vendor Manager sits on the other side. This almost always means that some form of re-organisation has to happen. Whether it's impacting your wider digital commerce unit or not will depend on the existing org structure. 𝟮- 𝗖𝗵𝗮𝗻𝗻𝗲𝗹-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 With automation becoming Amazon's NorthStar to develop its retail business, vendors must review and adapt their portfolio strategies with the online retailer. Ensuring your NPD pipeline aligns with a healthy ASP, and Net PPM ambition from Amazon is already and will become even more critical. It is good practice to follow a selective portfolio approach by focusing on listing those items with a healthy RRP to ASP ratio. 𝟯- 𝗢𝗳𝗳𝘀𝗵𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 As Amazon deploys fewer headcount resources per account and offshores more tasks to brands, vendors are faced with higher headcount requirements to manage these processes from their side. Identifying labour-intense tasks should already be high on the priority list of suppliers. Manually downloading data, disputing chargebacks, or listing items should be outsourced at best and eventually automated. --- Amazon's profitability focus will reduce and eventually remove the Vendor Manager function. The question is: Is your business ready for an algorithm-led future? Let me know in the comments! #amazonvendor #amazonstrategy