AI in the Chain

Introduction

Artificial intelligence (AI) is no longer a futuristic dream in supply chain management. Companies have adopted forecasting engines, autonomous scheduling systems and real‑time monitoring platforms. Yet the promised productivity gains and resilience remain elusive. Hidden behind the hype lies a new bottleneck: the speed at which organisations act on the insights generated by algorithms. While machines process data in milliseconds, human approvals and legacy structures often take days or weeks. This tension between algorithmic speed and organisational speed is emerging as one of the most critical challenges of 2026.

The AI–Organisation Speed Gap

AI systems generate recommendations faster than ever. Forecasting models update demand projections in real time, transportation algorithms reroute shipments within seconds and predictive maintenance tools can predict equipment failures before they happen. However, these systems only deliver value when their recommendations are translated into action. Many companies still rely on traditional approval chains, manual overrides and weekly planning cycles. The result is a growing gap between how quickly machines can sense and decide, and how slowly people execute. This gap erodes the benefits of AI and leads to frustration among leaders who expected transformational results.

The New Bottleneck

The bottleneck has shifted. For decades, technological limitations dictated decision speed: planners waited for overnight batch runs, data integrations took weeks and reports required manual compilation. Today, the technology is ready—but organisations are not. Slow decision processes delay responses to disruptions and opportunities. Suppliers cannot pivot when purchase orders remain stuck in approval queues. Production adjustments stall as teams seek cross‑functional consensus. Inventory policies are updated too late to prevent stockouts or overstocks. AI exposes these delays because it makes the difference between sensing and acting more visible.

Why It’s Happening

Several factors contribute to organisational inertia:

• Legacy governance models. Many companies maintain hierarchical approval structures designed for stability and risk avoidance. These frameworks slow down decisions even when the underlying data is robust.
• Cultural resistance. Employees accustomed to manual processes may distrust machine recommendations and prefer human judgment. Without buy‑in, they delay action while they double‑check outputs.
• Unclear decision rights. AI may recommend a change, but teams are unsure who is empowered to implement it. The lack of clarity leads to duplicated efforts and escalations.
• Mismatched KPIs. Departments measure success on productivity, cost or forecast accuracy, rather than responsiveness. When incentives favour stability, employees hesitate to adopt AI‑driven actions that could disrupt short‑term metrics.
• Insufficient integration. Algorithms work at lightning speed only if data flows freely across systems. Fragmented IT landscapes and siloed data slow down decision execution.

Redesigning for Speed

Closing the gap requires rethinking organisational design to match algorithmic speed. Studies show that even when companies deploy generative AI widely, more than 80 % see little or no impact on revenue or profits because the human systems around the technology remain unchanged. Creating prototypes is relatively simple; generating measurable business value requires deliberate organisational redesign. The following principles can help leaders reshape their companies for velocity:

• Reengineer approval workflows. Delegate authority to front‑line managers where appropriate and establish clear thresholds for automatic execution. Define conditions under which AI recommendations can be applied without additional approvals. By flattening hierarchies and moving decisions closer to the information source, organisations reduce waiting time and increase responsiveness.
• Align incentives with velocity. Traditional KPIs such as forecast accuracy or cost per unit ignore decision speed. Create metrics for decision latency and decision velocity, and reward teams for acting on insights quickly, not just for reducing costs or meeting service levels. Making latency visible changes behaviour: when teams know their performance is measured in minutes or hours instead of weeks, they prioritise speed over perfection.
• Embed decision rights in processes. AI systems often produce recommendations without clarity on who should implement them. Clearly map who owns each type of decision and empower those stakeholders to act. Provide transparent guardrails so teams know when to involve higher levels. Without this clarity, recommendations bounce between functions and erode their value.
• Invest in change management. Cultural readiness is a critical barrier. Employees must trust the system and understand how to collaborate with AI. Educate teams on how algorithms work, what assumptions they make and how to interpret outputs. Encourage experimentation and build confidence by showcasing early wins. Many organisations lack clear execution mechanisms and talent capable of bridging business and technology; targeted capability building and communication are essential to overcome this gap.
• Improve data integration. Algorithms work at lightning speed only if data flows freely across systems. Build a unified data backbone that allows AI to draw from and feed into all relevant systems. Ensure that data is updated in near real time so that recommendations are based on current conditions. Fragmented technology and data foundations are a common barrier to scaling AI. Integrating disparate systems reduces manual reconciliation and speeds up execution.

These principles aim to synchronize organisational tempo with algorithmic tempo. However, redesign alone does not ensure adoption. Organisations must also build governance structures and cultivate new roles that oversee AI operations, as discussed next.

How to Get Started

Transforming organisational speed can feel daunting, but leaders can begin with targeted actions that build momentum and demonstrate value. A structured approach helps teams prioritise efforts and align around measurable goals:

1. Diagnose the current state. Begin by mapping your end‑to‑end decision journeys. Measure how long it currently takes to move from signal to action for critical processes such as demand planning, purchase orders, production schedule changes or logistics rerouting. This baseline reveals where approvals accumulate, data becomes stale or ownership is unclear. AI analytics can help by automatically logging timestamps across systems and visualising bottlenecks, showing exactly where decisions stall. Many companies are surprised to find that decisions routinely sit idle for days or weeks—even when AI models detect issues within minutes.
2. Clarify the value focus. Many companies adopt AI without a clear value focus and end up with fragmented pilots. Identify high‑impact business domains (such as service levels, working capital or new product introductions) and prioritise decisions in those areas. Advanced analytics can highlight where slow decisions erode value by quantifying cost‑to‑serve trade‑offs and lost revenue. Align your AI initiatives with business strategy so that teams understand why speed matters and how it drives tangible outcomes.
3. Define decision rights and governance. Create a decision rights matrix that specifies who makes which decisions, under what conditions, and with what authority. Include new roles such as AI risk auditors, ethics and governance directors, compliance officers and accountability officers to oversee algorithmic recommendations. These specialists interrogate automated forecasts and sourcing choices, establish usage policies, maintain audit trails and define escalation protocols. Their presence parallels financial controllers; they ensure that AI‑driven operations operate within appropriate risk boundaries. Modern AI platforms often include governance modules to log decisions, track model performance and surface compliance issues, making it easier for these roles to operate.
4. Launch a cross‑functional pilot. Select one high‑value decision—such as supplier allocation, inventory rebalancing or order promising—and redesign its workflow end to end. Assemble a cross‑functional team (planning, procurement, logistics, finance and IT) to implement AI recommendations rapidly. Allow the team to execute decisions within predefined guardrails without seeking higher approval. Use the pilot to identify obstacles, refine guardrails, and demonstrate the benefits of reduced latency. Successful pilots build confidence and create champions for broader change.
5. Align metrics and incentives. Introduce decision velocity as a KPI alongside cost and service metrics. Measure the time from signal to action for each pilot and compare it with baseline latency. Recognise teams that shorten the loop and share their practices. Over time, embed decision speed into performance reviews, quarterly business reviews and compensation structures. When leaders reward responsiveness, behaviours shift accordingly.
6. Build talent and culture. Invest in upskilling existing employees so they can work effectively with AI. Provide training on analytics interpretation, scenario planning and ethical considerations. Encourage a culture of rapid experimentation where teams test AI recommendations in low‑risk contexts and learn quickly from outcomes. Address the talent shortage by developing internal capability rather than relying solely on external hires. Pair domain experts with data scientists to create hybrid roles capable of bridging business and technology, and leverage AI‑based learning platforms to deliver personalised training modules.
7. Upgrade data and technology foundations. Consolidate data sources and remove silos that slow decision execution. Prioritise building a scalable architecture that supports near real‑time data sharing and model deployment. Avoid over‑engineering; focus on integrating systems and standardising data so that AI recommendations can be trusted and acted upon without extensive manual validation. Emerging AI‑powered data fabric technologies can help by automatically mapping data across systems and harmonising formats, reducing manual effort and accelerating integration.
8. Establish continuous improvement cycles. After initial pilots, implement routines to review performance, identify new bottlenecks and refine decision flows. Use feedback loops to adjust guardrails, modify metrics and update training. As culture evolves and teams gain confidence, expand the approach to more complex and cross‑functional decisions. Recognise that transformation is iterative; sustain momentum by celebrating successes and transparently discussing failures.

These steps provide a practical roadmap for organisations that want to match human speed with machine speed. Rather than attempting to overhaul everything at once, the emphasis is on measurable actions, clear roles and iterative learning. Over time, this discipline transforms supply chains from reactive networks into agile systems capable of exploiting AI’s full potential.

Final Thought

AI is not a panacea—it is a tool that amplifies organisational behaviours. If a company’s decision processes are slow and siloed, AI will reveal those weaknesses rather than eliminate them. In 2026, the competitive advantage does not come from deploying more sophisticated algorithms; it comes from aligning organisational speed with algorithmic speed. Leaders must shift the focus from technological adoption to governance redesign, cultural change and process simplification. Only then will the promises of AI translate into faster responses, greater resilience and tangible performance gains.

References

• McKinsey & Company – “Beyond the Hype: Unlocking Value from the AI Revolution” (Sep 8 2025). https://www.mckinsey.com/cn/our-insights/our-insights/beyond-the-hype-unlocking-value-from-the-ai-revolution
• Trax Technologies – “AI Governance and Compliance Roles Emerge in Supply Chain Operations” (Nov 20 2025). https://www.traxtech.com/ai-in-supply-chain/ai-governance-and-compliance-roles-emerge-in-supply-chain-operations
• GAINS Systems – “How to Speed Up Your Supply Chain Decision Making and Cut Latency” (Dec 4 2025). https://gainsystems.com/blog/how-to-speed-up-your-supply-chain-decision-making-and-cut-latency/