Most supply chains will still not use artificial intelligence to automate planning functions by 2030, according to a prediction from research firm Gartner reported by WWD.

The forecast sets a hard boundary on expectations for technology adoption across the tiers that apparel and soft goods buyers depend on — from yarn and fabric mills to cut-and-sew contractors and finished-goods distribution. If Gartner is right, the majority of the industry's planning workflow in 2030 — order sizing, capacity allocation, replenishment calculations — will remain a human-run process, or at best a human-led one with AI in an assistive rather than decision-making role.

That timeline matters for sourcing executives now structuring vendor relationships and technology budgets. A 2030 horizon spans the typical length of two to three sourcing strategy cycles. Buyers who assumed AI-driven planning would arrive in time to absorb the next wave of disruption — tariff shifts, freight volatility, regional capacity swings — now have an analyst house telling them not to count on it at scale.

The prediction also frames a likely split in the supplier base. Large, well-capitalised vendors may automate planning incrementally and win placement on that basis. The majority of suppliers, particularly mid-sized contractors in the main apparel-producing regions, will continue to operate on conventional planning methods. Sourcing teams that build their order books assuming universal digital sophistication across their vendor matrix risk mispricing lead times and capacity commitments.

For planning specifically, the distinction between using AI and automating with AI is the operative one in Gartner's framing. Many supply chains already run forecasting tools, demand-sensing software and analytics dashboards. The forecast addresses the narrower question of whether AI will make autonomous planning decisions — and Gartner's answer, as reported, is that most operations will not reach that point within the next six years.

That gap between deployed analytics and automated decision-making leaves buyers carrying the interpretive burden. Planners, not systems, will remain accountable for judgement calls on how much inventory to commit, which factories to load and when to re-route production. Headcount plans, planner training budgets and escalation protocols all follow from that reality.

The compliance dimension is worth flagging as well. As AI regulation tightens in major consumer markets, companies that do keep humans in planning loops will find it easier to demonstrate accountability for decisions that affect supplier payment terms, order cancellations and delivery commitments. Full automation would have introduced new audit obligations; slower adoption postpones that burden.

The practical takeaway for sourcing and procurement leaders is sequencing. Gartner's forecast suggests treating AI-driven planning automation as a targeted capability to build with select strategic vendors, not a baseline assumption across the supply base. Vendors should be segmented now on planning maturity — which suppliers can exchange machine-readable capacity data, and which still operate on spreadsheets and email.

For most of the industry, the decision the news forces is budgetary: whether to keep investing in full planning automation on an aggressive timeline, or redirect spend toward hybrid capabilities — better data pipelines, demand visibility and planner decision support — that pay off even if autonomous planning arrives late or unevenly.