Demand forecasting, inventory replenishment, production and shipment plans… Because of its data intensity and its repetitive decisions, supply chain planning is one of the areas where automation is discussed most. The question at the management table is also becoming sharper: "What should we prepare before automating demand and supply planning?"
Gartner's forecast, published on September 24, 2026, shows why this question matters now. According to Gartner, by 2030 only 5% of organizations that apply some form of automation in supply chain planning will make at least 10% of their planning decisions autonomously. What is more, this picture is not the result of a lack of investment.
In this article, we look at the gap between investment and autonomous decisions, Gartner's decision-centered approach, and the foundations that need to be prepared before starting automation.
Money is being spent, but handing over decisions is slow
According to a survey Gartner conducted in November–December 2025 with 243 senior executives from organizations with annual revenue of at least $500 million, 83% of participating organizations had spent at least $3 million on supply chain planning automation, including AI; for 51%, spending was between $3 million and $10 million.
Even so, the shift to autonomous planning is expected to remain gradual. Gartner highlights two reasons: the continuing need for human oversight, and readiness problems on the data, technology and organizational side. Gartner's main message is that investment can raise technical capability but, on its own, does not amount to readiness for AI.
In practice it looks like this: a new planning system is installed and the forecasting engine runs, but because planners do not trust the recommendations, they still correct the results by hand in spreadsheets. The investment has been made; the way decisions are made has not changed.
Not every decision automates at the same speed
The most useful distinction in Gartner's framework is the one drawn by type of decision. According to Gartner, the pace of autonomy will depend on the type, complexity and risk of each planning decision. Routine operational decisions such as inventory replenishment and order prioritization carry higher automation potential, while strategic decisions such as network design and inventory policy will continue to require human judgment.
Gartner also stresses that, before handing more decisions over to AI, organizations need to address data quality, decision ownership, employee skills and technology architecture. In other words, the automation question does not begin with "Which software should we buy?" but with "Which decision will we automate, under whose responsibility, and with what data?"
The data foundation: where are the transaction systems?
Planning automation is only as reliable as the transaction data that feeds it. According to Eurostat data compiled in May 2026, 46.45% of enterprises in the EU used enterprise resource planning (ERP) software in 2025. The rate is 41.08% among small enterprises and 88.71% among large ones. Across sectors, it stands at 57.62% in manufacturing and 53.77% in wholesale and retail trade.
These figures cover the EU and do not offer a direct comparison for Türkiye. Even so, the lesson is a general one: in a structure where orders, inventory and deliveries are not tracked consistently in a single transaction system, automation added to the planning layer will also produce decisions from faulty data. That is why it also makes sense that Gartner recommends building new AI and automation capabilities with flexible architectures that complement existing transaction and planning systems.
The planner's role is changing
The human side of readiness is at least as decisive as the data. Gartner notes that, as organizations move from AI-assisted planning to planning automated by AI, planners' roles and skills must change; it suggests that planners stop being "data managers" and become "plan orchestrators" who collaborate across the value chain.
The skills gap shows up in the numbers as well. According to Gartner's June 2026 analysis, demand for supply chain roles requiring AI skills rose 387% between the first quarter of 2023 and the first quarter of 2026. Of the roles requiring these skills, 58% are concentrated at the mid-senior level. Gartner says the gap cannot be closed through hiring alone and recommends giving priority to in-house skills development and to making more effective use of entry-level employees.
We looked at how role design can be approached on the procurement side in our article on procurement automation; in planning, too, the gain comes when the work itself is reorganized.
Before automating: a practical readiness checklist
The steps below are a general framework derived from Gartner's three recommendations (start with the decision, build the foundations for each use case, and measure decision improvement rather than technology rollout); each organization needs to adapt it to its own sector and system structure.
- Build a decision inventory. Classify the priority decisions in the planning process as strategic, tactical or operational. Gartner recommends defining the desired business outcome for each decision and deciding whether AI should support, augment or automate that decision according to its value, complexity, risk and need for human judgment.
- Start with routine operational decisions. Rule-based, repetitive and relatively low-risk decisions such as inventory replenishment and order prioritization are better candidates for a first pilot. Decisions such as network design and inventory policy should stay under human responsibility.
- Test data quality decision by decision. For the selected decision, check in which system, and with what consistency, the required data (demand history, inventory, lead time, open orders) is held. If there are manually maintained spreadsheets, connect them to the transaction system first.
- Define decision ownership in writing. Below which threshold will the plan proposed by the automation be applied on its own, and in which cases will it go to the planner for approval? Clarify the exception rules and the person responsible from the outset.
- Redefine the planner's role. For planners whose data-gathering and correction workload shrinks, add tasks such as scenario evaluation and reaching consensus with supplier and sales teams to the job description; start the in-house training plan together with the automation project.
- Measure decision quality, not technology. In line with Gartner's recommendation, monitor whether planners use the new tools as intended, whether manual corrections are decreasing, and whether people are going back to the old tools.
Conclusion
Gartner's data shows that spending on supply chain planning is rising quickly, but that handing decisions over to AI will progress slowly. The difference lies mostly not in technology but in foundations such as decision classification, data quality, decision ownership and planner skills. Starting automation not with a software choice but with a definition of the decision to be automated raises the likelihood that the investment will turn into a real improvement in decisions.
Why logistics experience matters in supply chain planning. A plan creates value only once warehouses and transport networks can carry it out, so the readiness steps above are best tested against the operation behind the plan: where stock actually sits, how long replenishment actually takes, and which exceptions end up being fixed by hand. That is familiar ground for one of our consultants, who brings more than 30 years in operations, logistics and supply chain. At a technology distribution company of roughly one billion dollars in scale, he ran logistics, warehousing, technical service and administrative operations end to end, building and leading teams of 100+ people and delivering lasting gains in operational performance through KPI-driven programs. He has also designed and directed large-scale logistics and distribution networks across leading retail, logistics and distribution organizations, managing nationwide warehouse and transport operations and network and capacity planning: first-hand experience of network design, one of the strategic decisions Gartner expects to keep requiring human judgment. An industrial engineer, he now brings that supply-chain discipline to SMEs through AI and automation. Meet our consultants.
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