AI System Procurement Best Practices

#AIProcurement Is Redefining How Organizations Buy, Build, and Manage Technology Here are the insights from my latest guest spot with Vishal Patel on the Ivalua #LoveProcurement Podcast: ----------------- KEY TRENDS & INSIGHTS 1️⃣ You’re Not Buying a Product — You’re Buying a Behavior - Acceptance criteria must include edge cases, hallucination risk, bias risk, and failure modes. - You must evaluate model behavior, not just technical specs. - Vendor evaluations must incorporate “black-box testing” and scenario trials, not just security questionnaires. 2️⃣ The Most Important Contract Clause Isn’t Price — It’s #ModelChanges Smart procurement teams negotiate: - Model change notifications - Update sandboxes - Version pinning - Right to revalidate - SLAs for output quality, not just uptime 3️⃣ Procuring AI Requires Cross-Functional Governance — Not Just a Contract AI touches everything, and all parties must participate in governance upkeep: - Customer data - Internal knowledge systems - Decision-making - Intellectual property - Regulatory exposure 4️⃣ Your Existing Evaluation Framework Is Probably Outdated AI evaluations require new categories of due diligence: - Training data provenance and licensing - Model lineage - Fine-tuning risks - Bias, safety, and security testing 5️⃣ AI Procurement Is Moving from a Cost Focus to a Capability Focus The real differentiators are: - Data inputs and outputs - Integration depth - Customization options - IP rights - Safety and security guarantees - Performance -------------------- THE BOTTOM LINE Leaders who update their procurement frameworks now will innovate faster, govern more effectively, reduce long-term risk, and curb losses. Those who don’t? They’ll end up with shadow AI, compliance problems, and expensive rework down the road. ------------ Watch the full episode here:  https://lnkd.in/emsvDjZK AI Procurement Lab

Want AI in Procurement? Don’t Skip the Basics! (A lesson from CPO’s "aha" moment) CPO: How fast can we deploy AI to cut costs & predict risks? Me: Let’s talk about your foundations first. Think of "deploying AI" like building a house: 🚫No foundation (data)-Your house collapses. 🚫No walls (processes)-Rain floods in. 🚫No electricity (skills)-You’re stuck in the dark. 🚫No plumbing (analytics)- Things get messy. His reality: 📉 Data was scattered across 7 systems 🧑💻 Team had zero analytics training. 🔄 Processes were manual, inconsistent & slow. Sound familiar? You’re not alone. Procurement Excellence | MAR 2026 - Deploying AI in procurement without clean data or a trained team is like building a skyscraper on a quicksand. The 5-Step Pyramid for AI-Ready Procurement (Start at the bottom!) #1. Clean Data ↳Get your basic facts straight. ↳No typos in supplier names, no duplicate orders, all spend tracked in one place. #2. Smooth Processes ↳Make your workflows simple and consistent. ↳Everyone follows same steps to approve purchases or sign contracts. #3. Trained The Team ↳Training on data analysis via use of new tools ↳AI helps humans it doesn’t replace them. Scared or confused teams won’t use it. #4. Basic Analytics ↳Use data to spot trends and measure success. ↳You need to walk before you run. Master simple insights before predicting the future. #5. AI ↳Let tech do complex tasks automatically. ↳Predicting shortages, negotiating prices, or finding risks before they happen. AI is the peak of procurement evolution—but you can’t jump straight to the summit. The Hard Truth AI isn’t a quick fix. It’s the climax of a journey Your Action Plan: ✅️Audit your data. ✅️Map processes & fix bottlenecks. ✅️Train your team on data literacy. ✅️Start small using basic analytics. ✅️Then and only then pilot AI. Skip steps & AI becomes expensive hype. Build step-by-step & it changes everything. What are other considerations for deploying AI in procurement? ♻️ Repost to help someone in your network 🔔 Follow Frederick for more hard truths about AI in business. #Procurement #AI #DigitalTransformation #DataDriven #Leadership #Maslow #Innovation

📜 Every time a company acquires an AI system, it must ensure legal due diligence and a well-structured contract, especially for high-risk use cases. To support this complex process, the European Commission has recently updated the EU Model Contractual Clauses (MCCs) for the Procurement of AI Systems. Although originally drafted for public entities, private organizations can also adopt or adapt the clauses when acquiring or developing AI systems. They serve as a valuable benchmark for any company, especially as the EU AI Act, despite its detailed scope, still leaves room for interpretation regarding specific contractual requirements. The revised MCC-AI are designed to align with the new AI Act and are available in two formats: 1. Full Version (High-Risk): Tailored for AI systems classified as high-risk under the AI Act, such as those used in recruitment, credit scoring, education, or healthcare. 2. Light Version (Low/Moderate Risk): A simplified alternative for AI systems that do not meet the high-risk threshold but may still affect fundamental rights or safety. ⚖️ Key Legal Provisions – Full Version (High-Risk AI Systems): 1. Technical Requirements: Obligations related to the system’s accuracy, robustness, and cybersecurity. 2. Supplier Responsibilities: Requires implementation of quality management systems and conformity assessments. 3. Data Governance: Clearly defines rights and obligations over the datasets used to train and operate the AI system. 4. Audit & Accountability: Grants public buyers the right to audit the supplier to verify compliance. 5. Indemnity Clauses: Suppliers must indemnify the buyer for any violations of intellectual property or data protection rights. ⚖️ Key Legal Provisions – Light Version (''Low/Moderate'' Risk AI Systems): 1. Transparency & Documentation: Suppliers must provide clear documentation about the system’s design, functionality, and purpose. 2. Data Governance: Sets out standards for data use and protection within the context of the AI system. 3. Exemptions: Unlike the high-risk version, it does not require formal conformity assessments or a full quality management system—reflecting a lighter regulatory burden. 🚨 Non-Binding Nature: The MCC-AI are non-binding templates designed to be tailored, adapted and annexed to broader procurement contracts. 🚨 Scope: These clauses focus specifically on AI compliance and the AI Act, without addressing unrelated contractual areas such as Data Protection, IP ownership, SLAs, or payment terms. Link for the updated Model Clauses: https://lnkd.in/eHzJtis7

𝗔𝗜 𝗪𝗮𝘀𝗵𝗶𝗻𝗴 𝗶𝗻 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 - 𝗶𝘀 𝘁𝗵𝗮𝘁 𝗮 𝘁𝗵𝗶𝗻𝗴? 98.5% accuracy in spend classification through machine learning - this is what the vendor promised. 📍But what happens when numbers don't reflect reality? You may have fallen into the trap of AI-washing, when vendors hop on the bandwagon of Artificial Intelligence and relabel their technology as AI-driven or -powered. Often this involves more marketing hype than substance, leading to overpromises and under delivery of true machine learning and supposed intelligence. It happened to us in a Procurement Transformation program with the implementation of a spend management solution. The proposition included a sophisticated machine learning model elevating spend classification close to perfection. In fact, the vendor prided themselves on their solution including: ▪️an advanced form of sophisticated intelligence ▪️capable of translating text on the fly into 10 languages ▪️cleaning & transforming entity and spend data in one go ▪️categorising spend based on learned patterns ▪️only requiring a few cycles of category inputs & reinforcement learning But it became a fiasco, almost a prank i would say: ▪️data feedbacks didn't reflect in fresh loads ▪️it was hardcoded rules, requiring manual updates ▪️with offshore teams translating text via Google ▪️the support teams couldn't explain how things worked ▪️the accuracy was below 85% 📍It broke the trust of key stakeholders, resorting back to Excel Sheets to keep visibility and control over their spend data and it cost the vendor a possible contract extension. A decade ago, AI was an emerging term, often loosely used and touted as the magic of machine learning. 🚩A magic which could have raised red flags, but in hindsight, produced some bitter learnings i have summarised here for you: ▪️𝗕𝗲 𝘀𝗸𝗲𝗽𝘁𝗶𝗰𝗮𝗹 𝗮𝗯𝗼𝘂𝘁 𝗰𝗹𝗮𝗶𝗺𝘀 on what AI can magically do. It has a great potential but won't solve more than the data & algorithm powering it. ▪️𝗗𝗲𝗺𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 & 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 of results and an open dialogue on possible limitations. This helps to seize up realistic expectations. ▪️𝗨𝘀𝗲 𝗣𝗶𝗹𝗼𝘁𝘀 𝘁𝗼 𝘃𝗲𝗿𝗶𝗳𝘆 𝗰𝗹𝗮𝗶𝗺𝘀 - ensure data sample used is diverse, large enough and reflective of your life environment ▪️𝗧𝗮𝗿𝗴𝗲𝘁𝘀 & 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - get guarantees for accuracy, define clear mitigation actions required if targets don't get met. ▪️𝗖𝗵𝗲𝗰𝗸 𝗼𝗻 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 of the partner, build up your own team to validate outputs of AI and for finetuning The early days of machine learning, or AI, in procurement were full of challenges and learnings. With the current AI boom, these pitfalls and vendors overpromising, AI-washing their solutions, is still quite relevant. ❓Have you experienced AI-Washing Share your experience and how to detect it in the comments. #artificialintelligence #procurement #ai #spendmanagement

This rapid expert consultation offers practical insights into how state and local governments can effectively integrate AI technologies into public services and governance processes. 1. Foundations and Governance (Why use AI and how to ensure its responsible use?) - Be Purpose- and People-Oriented - Engage the Public - Build Proportional and Iterative AI Governance - Participate in and Help Shape Emerging Collaborative Frameworks - Develop Tiered AI Procurement Guidance 2. Planning and Scoping (How to get started responsibly?) - Conduct Feasibility Assessments and Workflow Mapping - Scope Internal Capacity 3. Design, Development (if internal), or Selection (if procuring externally) (How to align design and development with purpose and use?) - Align the Problem Definition with Context, Goals, and Technical Design - Define Evaluation Criteria and Assess System-Level Impacts - Establish Feedback Mechanisms 4. Capability and Culture (How to build readiness?) - Build Internal Capacity and Competency - Use Partnerships with Stakeholders 5. Ongoing Accountability and Engagement (How to manage -implementation and maintain trust?) - Establish Tiered Continuous Monitoring and Improvement Mechanisms - Create and Sustain Advisory and Oversight Bodies" National Academy of Sciences

Why are so many companies stuck in what I call "AI Pilot Purgatory"? And how can they break out? After working with several large organizations over the past year, I keep seeing the same pattern: AI enthusiasm is everywhere… but measurable business impact is elusive. It’s not because of the technology. It’s because of the system around it. Here are some hard-won lessons from the field: 1. AI Doesn’t Fail — Organizations Do Most pilots don’t die from bad models or lack of data. They die because no one is empowered to make them real. When every decision requires a VP’s blessing, progress freezes. 👉 Best practice: assign empowered mid-level “AI owners” who can drive adoption, training, and workflow changes day-to-day. 2. You Can’t Automate a Broken Process Many teams try to “add AI” on top of inefficient workflows. Without a process audit first, all you’re doing is automating waste. 👉 Best practice: start with process mapping — identify the biggest time drains, handoffs, and rework loops. Then ask, “What if AI made this entire step disappear?” 3. Quick Wins Are Necessary, But Not Sufficient Every organization starts with small, function-specific tools that save time in HR, Finance, or Marketing. But true transformation happens in connecting silos and redesigning workflows across functions. 👉 Best practice: celebrate quick wins, but tie them to broader process metrics (throughput, yield, margin). 4. Culture Eats Model Performance for Breakfast The biggest blocker isn’t data or algorithms — it’s fear of imperfection. Many teams still think “if it’s not perfect, don’t deploy.” Meanwhile, their competitors are learning 10x faster. 👉 Best practice: create a “safe-to-pilot” culture — launch, measure, iterate. Perfectionism is the enemy of progress. 5. Measure Business Outcomes, Not Model Accuracy Executives don’t care about precision scores; they care about P&L. 👉 Best practice: define ROI metrics that speak their language: Hours saved → OPEX reduction Faster cycle times → working capital gains Higher throughput → revenue lift Without quantified ROI, AI becomes a cost center, not a growth driver. 6. Governance Should Accelerate, Not Paralyze Too many firms treat AI governance like a police force. It should act more like air traffic control — fast, structured, and transparent. 👉 Best practice: a 5-day governance SLA: every new AI initiative gets a quick risk review and greenlight, not a 3-month bottleneck. Scaling AI isn’t about chasing the next model or tool. It’s about aligning people, process, and accountability around business outcomes. Until that happens, even the best technology will stay trapped in pilot mode.

AI Agents Are Becoming a Commodity - Here's What Actually Matters in Procurement Tech After six years in aerospace procurement and now building AI solutions, I've watched the same movie play out over and over: Company buys AI-powered procurement platform for $2M. Six months later, buyers are back to Excel and email chains. The AI worked perfectly. The adoption didn't. Here's the uncomfortable truth that hit me during a conversation with a manufacturing CFO last week: AI agents aren't the differentiator anymore. Everyone has them. OpenAI, Claude, Gemini, pick your favourite. The real question is: can your team actually use them when it matters? I learned this the hard way at Bombardier. We had cutting-edge systems tracking 300,000 parts from hundreds of suppliers. But when a critical component was delayed? Back to phone calls and spreadsheets. The problem wasn't the technology. It was the last mile. Today, every procurement tech vendor is racing to add "AI agents" to their pitch deck. But here's what actually moves the needle: 🎯 Integration beats intelligence Your AI can be brilliant, but if it doesn't talk to your ERP, read your unstructured supplier emails, and handle your messy PDFs, it's useless. One client discovered this after their AI couldn't process 80% of their supplier communications. 📊 Data structure at the source Remember my "Acme Corp" example? Five entries for the same supplier. AI doesn't fix this, it amplifies it. The winners are building systems that structure data as it enters, not after. ⚡ The Monday Morning Test If your stressed procurement team won't use it on their worst day, you've built a demo, not a solution. Machinage Piché cut order processing from 50 minutes to 2. That's what passes the test. 🔄 Start where the pain lives Forget digital transformation. Pick one process that makes your team want to scream. Fix that. Then build from there. Small wins create believers. Believers drive adoption. The aerospace industry taught me something crucial: In mission-critical environments, the best technology is the one people actually trust and use. Three years from now, AI agents will be like email: everyone will have them. The companies winning will be those who made them invisible, integrated, and indispensable. The gap between procurement leaders and laggards isn't about who has AI anymore. It's about who's made AI work in the real world, with real data, solving real problems. Your move: What's the one manual procurement task your team does every day that shouldn't exist in 2025? #Procurement #AI #SupplyChain #Manufacturing #DigitalTransformation

AI procurement tools don't fail in the demo. They fail when CPOs skip these 5 buying questions. In the first five months of this year, we ran AI in Procurement trainings across several organizations. Different industries. Different budgets. Very different levels of AI maturity. But one thing kept coming up. In almost every session, someone mentioned an AI tool their company had already bought. When we asked how it was going, the room usually got quiet. Not because every tool was bad. Most weren't. The problem was that the buying decision happened before the team had answered some basic questions. These are the 5 that came up most often: 1. What problem are we actually trying to solve? Not "what can the tool do?" What is the specific gap this fixes in your procurement operation? If your team can't agree on that before the demo, you're not ready to buy. 2. What has to change internally for this to work? This was the pattern we heard again and again. The process stayed the same, but the tool changed. That rarely works. If the team's habits, workflows, approval steps, or decision rights don't change, the technology won't do much on its own. 3. What data does this tool need and do we actually have it? AI procurement tools are only as useful as the data they can work with. If your spend data is messy, supplier records are incomplete, or category structures are inconsistent, the tool won't magically fix that. It may just make the same problem move faster. 4. What results are realistic for our categories? Not a vendor case study from another industry. Your categories, your suppliers and your baseline. Ask what measurable improvement is realistic in your environment and get that expectation clear before signing. 5. Who owns this after go-live? Not the vendor. Not "IT." Not "Procurement". A real person on the procurement team who has the time, authority and accountability to drive adoption. If that person doesn't exist before the contract is signed, the tool probably won't survive past month three. We didn't come up with these questions in a classroom. The teams taught them to us. Usually after learning the hard way. If your team is about to buy an AI procurement tool, it may be worth having this conversation before the budget gets approved.

#AgenticAI is making waves, but here’s the thing — what matters right now isn’t the hype, it’s the use cases, especially domain-specific ones. In #procurement, we don’t need abstract theory — we need AI agents that can help us negotiate better, spot risks faster, and automate the boring stuff. There are 5 basic types of AI agents, and each plays a different role. Below are simple explanations and how they show up in procurement. 1. Simple Reflex Agent - The Rule Executor - “If X happens, do Y.” Instantly. This is the most basic kind of agent. It just sees a signal and fires off a pre-defined action. - In Procurement: An agent that says, “This request is under $1,000 and from a preferred supplier? Auto-approve it.” Perfect for high-volume, low-risk and immediate decisions 2. Model-Based Reflex Agent - The Context-Aware Assistant - “I’ve seen this before. Let me factor in what’s going on.” This one’s a bit smarter. It considers the current situation. It can detect patterns and respond more thoughtfully than the rule executor. - In Procurement: “This supplier usually delays orders when capacity is tight. There’s a big order coming in — maybe flag this one for review.” Great for catching risks that aren’t obvious at first glance. 3. Goal-Based Agent - The Goal-Driven Planner - “What’s our objective? Let me figure out the steps to get there.” This agent evaluates possible actions based on how well they align with a defined goal, then maps a path toward it. - In Procurement: “We’re aiming to consolidate suppliers by 30% this quarter. Here’s a multi-step plan to rebalance categories, reissue RFQs, and renegotiate contracts.” Ideal for executing sourcing strategies that require foresight and coordination. 4. Utility-Based Agent - The Trade-Off Master - “Let me score the options and choose the best one based on what matters most.” This agent uses a utility function to pick the most beneficial option to achieve the goal. - In Procurement: “Supplier A is cheaper. Supplier B delivers faster. Supplier C has better ESG ratings. Based on your weighted preferences (cost > speed > ESG), B is your best option.” A powerful assistant when you’re juggling KPIs and competing stakeholder interests. 5. Learning Agent - The Continuous Learner - “I’m learning from every decision, and I’ll do better next time.” This is the most advanced type — and the most exciting. A learning agent adapts over time by analyzing the outcomes of past actions. It constantly refines its model and improves performance. - In Procurement: “After reviewing 100 supplier evaluations, I’ve learned that certain vendors consistently fail at on-time delivery — I’ll deprioritize them in future sourcing events.” The closest thing to a self-improving digital team member — and likely the most impactful over time. At this stage, you don’t need every agent type in your stack — but knowing them helps you ask better questions, choose better tools, and build effective roadmaps.

Most Procurement AI Optimizes Speed. Very Little Improves Decisions.   Last week, I was on a call with a procurement leader at a global enterprise.   They weren’t confused about AI.   They were exhausted by it.   Every vendor pitch sounded the same. Copilots. AI-native. LLM-powered. Autonomous workflows.   They asked:   “What actually changes for my team? Faster workflows or better decisions?”   That question splits the market.   Traditional SaaS solved real problems.   Faster invoice routing. Cleaner dashboards. Better approval chains.   But speed without reasoning is a liability.   Take Accounts Payable.   A SaaS platform processes invoices faster.   An agentic system understands the contract terms behind the invoice, checks negotiated pricing, evaluates SLA compliance, reviews dispute history before payment is released.   On a $100,000 invoice, a 3% overcharge equals $3,000 in leakage.   A faster workflow doesn’t catch that.   It rubber-stamps it quicker.   That’s the structural gap between automation and intelligence.   The difference is context.   Procurement decisions do not live in isolated documents.   They span:   What sourcing negotiated. What the contract obligates. How the supplier has performed. What payment history reveals.   When those threads are linked, an agent reasons.   When they are not, you get a chatbot with a procurement interface.   As Jamin Ball has pointed out, the real question is not whether a product has AI.   It is whether intelligence is the operating core or layered onto workflows.   Alan Holland commented on a recent post:   AI-native requires learning, reasoning, structured precision, and autonomous agents working together.   An LLM can summarize a contract.   But can it translate obligations into enforceable logic, compare them to live performance data, and recommend renewal with traceability?   That requires different architecture.   Teams that understand this split will not just buy different software.   They will design different procurement systems.   Next time a vendor pitches AI, ask:   Does it automate workflows or improve decisions?   The answer reveals the architecture underneath.   Over the next few weeks, I will be posting a series breaking this down across sourcing, contracting, AP, and vendor governance using the same SaaS vs Agentic lens.   If you are rethinking how your team evaluates procurement AI, follow along.