All Dataleo news, jobs, analyses and tutorials around BI in Supply Chain and Operations.
The decisive architectural question is not whether a platform uses AI, but whether the customer can inspect, configure, version and govern the planning logic.
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A more accurate forecast creates value only when it changes inventory, capacity, service or financial decisions in a measurable way.
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Beauty, home-goods and pet brands expose the same planning problem: inventory policies must reflect shelf life, lead time, velocity and cash—not one portfolio-wide target.
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Decision capacity, data reliability and explainability as practical filters for AI investment
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Capability maps do not reveal how many agents exist, which decisions they influence or who owns their actions.
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Supply Chain AI fails when companies automate a weak process, fragmented data and unclear decision rights.
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Simulation adds value when approved assumptions become part of the same capacity model used for operational planning.
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As AI prioritizes exceptions and diagnoses root causes, planner work shifts from recalculating every plan to governing which decisions deserve action.
Read more →Counting agents, use cases or automated workflows does not prove that planning decisions improved.
Read more →The next planning failure will not come from a bad forecast. It will come from an AI agent making a plausible decision that nobody clearly owns.
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AI can accelerate decisions, but it cannot repair fragmented data, unclear ownership or dysfunctional processes
Read more →Why one forecast KPI cannot represent every product, audience and planning decision
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Why AI should expand professional capacity without transferring decision accountability
Read more →Moving planners from manual reporting toward interpretation and strategic influence
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A CMO appointment to monitor for shifts in how o9 frames planning decision value
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Model literacy helps only when outputs enter governed planning decisions
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Combining inventory and demand context matters more than dashboard count
Read more →Learning AI methods is easier than defining decision ownership
Read more →Practitioner conversations often reveal what scales and what does not
Read more →The value may shift toward the layers that enable AI adoption
Read more →The challenge is turning disruption signals into governed decisions
Read more →Emerging logistics technologies require governance before scale
Read more →Separating AI marketing from decision capability
Read more →Spending expectations are accelerating, but many organizations are still trying to automate planning without redesigning decision rights, data ownership or workflows.
Read more →From forecasting and procurement to governed operational decision support
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From decision-ready insights to governed planning actions
Read more →Evaluating planning outcomes instead of marketing claims
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Automation may reduce manual planning work while increasing the need for people who design decision rules, govern agents and connect business priorities to planning systems.
Read more →Planning expertise increasingly sits alongside SQL, modelling and data-structure skills
Read more →Fast application creation increases the need for a governed trust layer
Read more →AI, dashboards, process mining and automation often claim benefits against the same underlying work, inflating the business case.
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A practical framework for decision purpose, ownership, versioning and security
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When every operational choice requires senior validation, leadership becomes the constraint the planning process cannot optimize around.
Read more →Why workforce redesign, training and governance matter as much as generative AI capability
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