Planning a real-time network in a spreadsheet.
Supply chains are volatile, visibility is partial, and planning still happens in tools built for a slower world. The gap between what is happening and what the plan assumes is where cost-to-serve leaks — and AI is now moving from planning into execution.
The economics are concrete. The IBM Global AI Adoption Index reports 87% of enterprises use AI for demand forecasting, driving a 35%+ accuracy improvement, with 67% reporting roughly a 28% drop in stockouts[1]. A Deloitte benchmark finds firms with mature AI supply chains achieve 25–30% higher operational efficiency in North America and Europe[2]. The decisive 2026 shift is AI moving from planning into execution — acting within the flow of events as conditions change[3].
What actually constrains logistics technology.
Plans that don’t survive contact
Static planning degrades the moment reality diverges — and as volatility rises, planning assumptions degrade faster[3].
Partial, lagging signal
Decisions made on where things were, not where they are — delayed visibility, not lack of data, is the failure mode.
Exception handling by humans
Every disruption escalates to a person, so the network can’t scale its responsiveness.
The real barrier
Industry leaders consistently name partner/data chaos and data readiness as the limiting factor, not model capability[2].
The opportunity map — grounded in deployed systems.
From the constraint to the capability.
Where the industry data meets our work.
Supply chain engagement
This is where a verified CodesmoTech logistics engagement is presented — the lever (on-time delivery, cost-to-serve or exception rate) modelled in Stage P, the architecture, and the measured outcome. The industry data above is sourced and attributed; the figure below is reported on CodesmoTech’s primary site where applicable.
The capabilities behind this.
Mapped to PRISM — front-loaded into Proof and Roadmap, where the risk to budget is highest.
Every industry figure on this page is attributed.
- IBM Global AI Adoption Index (2025, reported 2026) — 87% of enterprises use AI for demand forecasting; 35%+ accuracy improvement; 67% report ~28% stockout reduction. ibm.com
- Deloitte benchmark study (reported 2025–2026) — firms with mature AI supply-chain systems achieve 25–30% higher operational efficiency in North America and Europe.
- Logistics Viewpoints (2026) — the shift from AI-in-planning to AI-in-execution; planning assumptions degrade faster as volatility rises. logisticsviewpoints.com
- Inbound Logistics / industry expert panel (2026) — demand forecasting and route optimisation as the most reliable AI wins; data readiness as the primary barrier.
Where is cost-to-serve actually leaking?
That is a Stage P conversation. We model the network economics with your operations leads — against your numbers, not industry averages — before proposing a build.
Model my ROI →