AI Is Making Decisions in Your Supply Chain Right Now — Do You Know Which Ones? Most executives in logistics, procurement, and operations aren't asking "should we use AI?" anymore. That ship sailed. The real question — the one keeping supply chain leaders up at night — is: where do you trust it, and where do you don't? Because getting that wrong isn't a tech problem. It's a margin problem. A resilience problem. A career problem. "AI doesn't fail loudly....it fails quietly" Autonomous decisions in planning, inventory, and procurement can compound errors before anyone notices. The executives who are ahead aren't using less AI — they're using it with governance frameworks that give them visibility and control at the right moments. "Resilience isn't expensive.....unplanned disruption is" Anti-fragile supply chains don't just survive volatility. They learn from it. Diversification, scenario modeling, and modular network design are the tools leaders at $500M+ companies are using to turn disruption into structural advantage — without killing margins. "ESG reporting is costing you money. ESG strategy is making it." There's a gap between companies drowning in compliance and those using traceability and sustainable procurement as a competitive lever. End-to-end visibility isn't just a reporting tool — it's a brand advantage and a cost reduction engine. "Your digital supply chain is only as strong as the team running it" AI adoption stalls when the talent model doesn't evolve with it. Reskilling, cross-functional collaboration, and digital-first recruitment aren't HR talking points — they're operational necessities for leaders building supply chains that actually work in 2026. "The best insights don't come from consultants — they come from peers" 90+ senior executives. All targeting the same problems. Companies ranging from $500M to $300B+ in annual revenue. When you put those people in a room — from logistics, operations, transportation, procurement — the conversations don't stay theoretical for long. The supply chain leaders who will define the next five years aren't waiting for perfect conditions. They're building the governance, the networks, and the talent models right now — while others are still debating whether AI is ready. The question isn't whether your supply chain needs to evolve. It's whether you'll be in the room where that conversation happens first.
Supply Chain Leaders: Leveraging AI for Resilience and Growth
This title was summarized by AI from the post below.
More Relevant Posts
-
77% of supply chain organisations have no formal AI strategy. Not a draft. Not a roadmap. Not even a one-pager. Just a growing list of projects that don't connect to each other or to the rest of the business Gartner surveyed 120 supply chain leaders who had already deployed AI. The majority were operating project-by-project, chasing short-term wins while the technology they were buying accelerated around them. The result? Fragmented systems that can't talk to each other. Spend that converts to cost. And competitors executing against roadmaps, pulling further ahead every single month. Here's what makes that worse: By 2030, half of all supply chain management solutions will deploy autonomous AI agents. The organisations without a strategy today aren't just behind, they're building debt that compounds. A minimum viable AI strategy is six elements. One document. Completable in under two weeks. Most teams don't have that. But here's the truth nobody puts in the infographic: Writing the strategy is the easiest part. The hard part is what comes after the document: Getting leadership aligned on three use cases when everyone has a favourite. Confronting the data quality problems the strategy will surface. Holding the 90-day pilot to its scope when pressure builds to expand it. Naming the owner, and giving them real authority to say no. Organisations pulling ahead with AI didn't just write a better document. They built the discipline, the accountability, and the structure to execute against it and kept executing when it got uncomfortable. That's the gap. Not the page. What comes after it. Does your supply chain have a strategy, or just a list of projects? #SupplyChain #AI #SupplyChainAI #DigitalTransformation
-
AI implementation is one of the biggest opportunities in supply chain right now. But it comes with real risk if organizations aren't approaching it with discipline. Our EVP of Alliances, Thomas Deakins, MBA, draws a striking parallel to the dot-com era: the rush to adopt was real, the money flowed fast, and then came the buyer's remorse. The reason? Misalignment between buyers and sellers on inputs, outcomes, and ROI. Sound familiar? Before scaling AI, organizations need five things in place: 1. A strategy tied to clear business priorities (AI enables strategy, it isn't the strategy) 2. Defined outcomes with measurable baselines and expected lift 3. Clean data and a Master Data Management plan (AI amplifies bad data, it doesn't fix it) 4. A signed-off change management plan with executive sponsorship 5. A workforce strategy alongside the technology strategy The companies that win won't be the ones who move first. They'll be the ones who move strategically. Read Thomas's full perspective on what structured AI adoption actually looks like. https://hubs.li/Q045L2820 #AI #AgenticAI #SupplyChain #DigitalTransformation #DataStrategy #ChangeManagement #Leadership
-
Some excellent insights here from my University of Technology Sydney colleague Llewellyn Spink: When it comes to how you'll use AI, "...sometimes slowing down now is how you move faster later. Investing early in governance, safety and operating models may slow initial deployment, but it pay dividends later and prevents failure at scale."
Is it time to rewrite the AI strategy playbook? That was the topic Jon Shen and Lenka Bednarikova explored with Su Jella MAICD, MBA at Corinium Global Intelligence's Enterprise AI Sydney 2026. Here are my top takeaways: 1. "Use AI" isn't a strategy. Leadership are telling staff to "use AI" without defining what that means, what risks they will accept, or how people should actually adopt it. Lack of clarity and focus are why AI is not working. 2. Doing nothing is a risk too. Boards that are not discussing AI are not playing it safe. Competitors are moving and employees are experimenting regardless. Directors do not need to be AI experts, but they do need to engage, educate themselves, and set a clear risk appetite. 3. SMEs can start small. Not every organisation needs a $50k strategy. First, identify the real problem. Then ask, is AI actually the right solution? If interest, trust, capability are low, focus on AI literacy and mindset first. Cheap experimentation is now possible. The cycle from idea to prototype can take hours rather than months. But prototypes need serious testing before production! 4. Sometimes slowing down now is how you move faster later. Investing early in governance, safety and operating models may slow initial deployment, but it pay dividends later and prevents failure at scale. 5. Are we measuring value or just making pretty PowerPoints? Many organisations claim to track AI ROI, but can they show it clearly? Not every use case has a direct dollar return, but leaders should still be able to articulate the expected value e.g. Copilot: individual productivity benefits, reduced shadow AI risk, and improved employee satisfaction. A few other insights from the day that stuck with me: - Simon Hilton set out MYOB's three core questions for AI risks: Does it work? Is it safe? Is it on brand? - Cindi Howson highlighted that 80% of CIOs think AI is outpacing governance and most organisations are not investing in effective change management for AI. - Michael Hou asked: If 88% of organisations are using AI, why have so few scaled it enterprise-wide? Fragmented data, bespoke integration, unfmiliar governance and low maturity.
-
-
AI in Supply Chain: From Operational Tool to Strategic Advantage. AI is rapidly reshaping supply chains across Africa and globally. As global supply chains grow more complex and interconnected, the role of leadership is evolving. Today, Artificial Intelligence (AI) is no longer just an efficiency tool, it is a strategic driver of competitive advantage. From my perspective, organizations that are winning are those leveraging AI not just to automate, but to anticipate, adapt, and lead. Driving Predictive Decision-Making- AI is fundamentally shifting supply chains from reactive to predictive. Leaders can now anticipate demand fluctuations, align supply strategies proactively, and significantly reduce volatility across operations. Reinventing Cost and Service Balance- Traditionally, supply chain leaders have had to trade off between cost efficiency and service levels. AI is changing that equation—enabling both through smarter inventory optimization, dynamic pricing insights, and intelligent logistics planning. Building Resilient Supply Networks- In an era of disruption, resilience is non-negotiable. AI enhances visibility across the value chain, identifies risks early, and enables faster scenario planning—ensuring continuity even in uncertain environments. Unlocking End-to-End Visibility- True transformation happens when data is connected. AI integrates fragmented systems into a unified view, empowering leadership teams with real-time insights for faster, more confident decision-making. Elevating the Role of Supply Chain Leadership- Perhaps most importantly, AI is redefining the role of supply chain leaders—from operational managers to strategic business partners. The focus is shifting toward value creation, innovation, and long-term growth. Final Thought- AI adoption is no longer a question of capability, but of urgency. The competitive gap between AI-enabled supply chains and traditional models is widening rapidly. For leaders, the mandate is clear: embrace AI, invest in capabilities, and lead the transformation.
-
If AI collapses the gap between decision and execution, the risk is no longer speed. It is direction. In my last post, I talked about how we can now be efficiently wrong at the speed of light. What does that mean operationally? Most organizations were built for a world where execution was expensive. Friction acted as a filter. Ideas took time. Mistakes surfaced before they could scale. AI removes that filter. Now decisions can be executed instantly and at scale. If the intent is off, the system will execute it perfectly. That is the new failure mode. Not slow failure. Fast, precise, large-scale misalignment. Left unchecked, this creates a different kind of risk. Not visible failure, but drift. Systems continue to operate, dashboards stay green, but the organization quietly moves away from its intent. So the problem is no longer just capability. It is control over what gets executed. The operating model must evolve across four dimensions. - Decision rights must be explicit. Who decides what, and under what conditions, can no longer be implicit. - Policy must be machine interpretable. Not documents, but encoded and enforceable constraints. - Execution must operate within guardrails. Not approvals, but clearly defined boundaries. - Feedback must be continuous. Real-time visibility with rapid correction. Together, these create controlled autonomy at scale. As execution becomes faster and cheaper, the only thing that does not scale automatically is intent. That makes human judgment the most critical infrastructure in the system. This is the shift. From managing systems to governing decisions. From coordinating people to directing outcomes. Speed without direction is not an advantage. It is risk. The companies that get this right will not just move faster. They will stay aligned as they scale. That is where value will be created. Are you building a faster engine, or a better steering system? #AI #Leadership #TechnologyStrategy #OperatingModel #Governance #DigitalTransformation
-
While the ROI of a unified AI system can be realized rapidly (weeks or months), the road is often blocked by legacy silos and dirty data. We finally have the tools to unlock this value, but the tools are only as effective as the data we feed them... and for most, that data is commonly locked in silos. What holds back teams and organizations from making this shift? Cultural change? Cost/investment? Leadership mandate? Opportunity awareness? #Procurement #SupplyChain #AI #DigitalTransformation Inverto | A BCG Company https://lnkd.in/dtxHw9Vr
-
AI optimism is everywhere. Better decisions are not. Most manufacturing companies already have: • Supplier scorecards • Yield data • Cost and financial reporting But it lives in different systems. So here’s the problem: Leaders think they’re making data-driven decisions… when they’re actually making fragmented decisions with data attached. Now layer AI on top of that. AI doesn’t fix disconnected data. It scales it. → Faster analysis → Same blind spots → Bigger consequences The real opportunity isn’t “more AI.�� It’s connecting: • Supplier risk • Operational performance • Financial impact So, decisions are based on what actually matters. That’s where I’m seeing the biggest gains: Not better tools. Better alignment of data and priorities. Curious—where do you see the biggest disconnect in your organization today?
-
-
Why does AI enthusiasm so often outpace AI impact? New industry data points to a familiar pattern. Investment is rising, pilots are everywhere, yet scaled value remains elusive. The constraint is rarely the model. It is operating discipline. Organisations report stalled adoption not because the technology fails, but because governance, incentives and workflow design lag behind ambition. Teams experiment at the edge while core processes remain unchanged. Leaders sponsor initiatives, yet hesitate to rewire decision rights, funding models or risk thresholds. For CFOs and supply chain leaders, this is less a technology story and more a performance management question. If AI insights do not influence pricing, inventory buffers, capital allocation or scenario cadence, they become interesting artefacts rather than drivers of margin. Adoption falters where accountability is unclear and success measures are vague. It accelerates where AI is treated as an enterprise capability, embedded into how the business runs, not bolted on as a side project. The real question is not whether to invest in AI. It is whether the organisation is prepared to change how decisions are made. Read more: https://lnkd.in/g8xMP7GP #AIAdoption #CFO #SupplyChain #PerformanceManagement #EnterpriseTransformation
-
-
Why does AI enthusiasm so often outpace AI impact? New industry data points to a familiar pattern. Investment is rising, pilots are everywhere, yet scaled value remains elusive. The constraint is rarely the model. It is operating discipline. Organisations report stalled adoption not because the technology fails, but because governance, incentives and workflow design lag behind ambition. Teams experiment at the edge while core processes remain unchanged. Leaders sponsor initiatives, yet hesitate to rewire decision rights, funding models or risk thresholds. For CFOs and supply chain leaders, this is less a technology story and more a performance management question. If AI insights do not influence pricing, inventory buffers, capital allocation or scenario cadence, they become interesting artefacts rather than drivers of margin. Adoption falters where accountability is unclear and success measures are vague. It accelerates where AI is treated as an enterprise capability, embedded into how the business runs, not bolted on as a side project. The real question is not whether to invest in AI. It is whether the organisation is prepared to change how decisions are made. Read more: https://lnkd.in/gWfA9jVz #AIAdoption #CFO #SupplyChain #PerformanceManagement #EnterpriseTransformation
-
-
AI in Procurement isn’t about replacing people — it’s about finally unlocking the function’s full potential. For years, procurement has been expected to drive savings, manage risk, and enable the business… often with limited visibility and manual processes. AI is changing that equation. What I’m seeing (and where the real value is emerging): • Spend intelligence at scale – AI can classify and normalize line-item data in ways traditional tools never could, giving leaders true visibility into where money is going • Market intelligence on demand – real-time insights into pricing trends, supplier benchmarks, and category dynamics that were previously fragmented, outdated, or locked behind static reports • Proactive risk management – from contract deviations to supplier signals, AI can surface issues before they become audit findings or business disruptions • Faster, smarter sourcing – drafting RFx events, analyzing bids, and identifying negotiation levers in a fraction of the time • Cycle time compression – intake to contract is no longer a weeks-long process when workflows are augmented with AI-driven insights • Stronger stakeholder alignment – AI helps translate procurement data into business outcomes leaders actually care about But here’s the reality: AI won’t fix a broken procurement model. If your policies are unclear, your data is fragmented, or your governance is inconsistent, AI will simply accelerate the noise. The organizations that win will be the ones that: 1) Have disciplined processes and clean data foundations 2) Align procurement to business outcomes, not just cost savings 3) Use AI as an augmentation layer — not a shortcut Procurement has always had the potential to be a strategic lever. AI just removes the excuses. Curious how others are applying AI in procurement — where are you seeing real impact vs. hype? #Procurement #StrategicSourcing #SupplyChain #ArtificialIntelligence #AI #DigitalTransformation #SpendAnalytics #CategoryManagement #Sourcing #ProcurementLeaders #CPO #ThirdPartyRisk #VendorManagement #FinServ #EnterpriseTransformation
