George Ninikas,\u00a0SVP\u00a0of Sales and Accounts\u00a0and\u00a0Supply Chain Planning \u2013 Americas for ORTEC,\u00a0examines why the future of manufacturing artificial intelligence lies in supporting experienced planners with faster\u00a0recommendations, continuous monitoring, and intelligent scenario analysis\u00a0\u2013\u00a0not replacing human\u00a0expertise.<\/p>\n
Manufacturing executives are not short on enthusiasm for artificial intelligence\u00a0(AI), but they are short on certainty about how to deploy it where it counts.\u00a0\u00a0<\/p>\n
A recent poll of supply chain and\u00a0logistics\u00a0professionals found that only 29 percent are currently deploying AI to support real, operational decisions. The largest group, 37 percent, describe themselves as actively exploring how AI might help, testing pilots\u00a0and trying to separate genuine capability from marketing language.\u00a0<\/p>\n
That gap matters for plant and operations leaders balancing tight\u00a0labour\u00a0markets, capacity constraints, and customer commitments all at once. It suggests the industry has moved well past asking whether AI belongs in manufacturing operations and is now wrestling with a harder question: what role\u00a0should it actually\u00a0play once it arrives?\u00a0\u00a0<\/p>\n
The poll also pointed to where practitioners expect the most value to materialise. 34 percent identified dynamic routing and real-time replanning as the application most likely to deliver meaningful impact in the near term, ahead of other commonly discussed use cases. Operations leaders do not want AI to simply generate a static production plan and walk away;\u00a0they want a system that\u00a0can\u00a0respond as conditions change throughout the shift \u2013\u00a0the way the plant floor\u00a0actually operates.\u00a0<\/p>\n
Much of the public conversation about AI in manufacturing still\u00a0centres\u00a0on automation replacing human judgment and the algorithm quietly taking over the scheduler\u2019s desk. That framing, whilst\u00a0attention-grabbing, does not match what is happening inside most manufacturing planning organisations.\u00a0\u00a0<\/p>\n
A more\u00a0accurate\u00a0view is also more practical. Agentic AI, meaning AI systems capable of taking multi-step actions on a planner\u2019s behalf rather than simply producing a single output, is increasingly being designed as a support system. It sits alongside the human decision maker, not in place of them.\u00a0<\/p>\n
This distinction is not trivial. A planner managing production scheduling and outbound\u00a0logistics\u00a0is not solving one isolated problem. They are juggling machine capacity, workforce availability, customer delivery windows, fluctuating demand, supplier disruptions, and last-minute changes, often simultaneously.\u00a0\u00a0<\/p>\n
In one recent example, a team was using 14 planners for six hours a day to engineer delivery territories, assigning dedicated zones of delivery by driver and day of the week. Decades of work have gone into giving planners powerful optimisation engines to handle that complexity mathematically. What has been missing is an easier way to interact with those engines without needing a data science background to\u00a0operate\u00a0them.\u00a0<\/p>\n
Anyone who has worked inside an advanced planning system knows the feeling of staring at a dense configuration screen, full of buttons and fields that control\u00a0behaviour\u00a0in ways that are not always obvious. This condition might be called the parameter plague, and it is one of the quieter barriers to AI adoption in manufacturing operations. Powerful optimisation logic is only useful if the people who need it\u00a0can\u00a0actually access\u00a0it.\u00a0<\/p>\n
Agentic AI offers a practical answer to this problem. Rather than requiring a planner to manually adjust dozens of settings to test a scenario, a natural language interface lets that same planner simply describe what they want to explore. Asking a system to show what happens if a machine goes down, or to compare two production schedules, or to flag which orders are most exposed to a supplier delay, becomes a conversation rather than a technical exercise. The underlying optimisation math has not changed;\u00a0what has changed is who\u00a0can\u00a0reach it and how quickly.\u00a0<\/p>\n
This is also where the idea of AI as a digital co-worker becomes useful. A good colleague does not take over someone\u2019s job, but\u00a0handles the repetitive, time-consuming groundwork, surfaces relevant information at the right moment, and offers a recommendation whilst\u00a0still leaving the final call to the person with context and accountability.\u00a0\u00a0<\/p>\n
Applied to manufacturing planning, that means an agentic system\u00a0can\u00a0monitor plant and logistics operations continuously, flag a disruption the moment it happens, propose a reassignment, and explain its reasoning, whilst\u00a0the planner retains the authority to accept, adjust, or override the suggestion.\u00a0<\/p>\n
This shift is changing what the planning role\u00a0actually looks\u00a0like day to day. Historically, planners spent much of their time building production schedules from scratch, often under\u00a0significant\u00a0time\u00a0pressure. As agentic systems take on more of that initial construction work, the planner\u2019s role is shifting toward analysis and judgment: reviewing automated proposals, adjusting for context\u00a0what\u00a0the system may not fully capture, and deciding how much autonomy to grant for routine, low-risk decisions versus how much to reserve for direct human approval.\u00a0<\/p>\n
The technology\u2019s value lies less in removing people from the loop and more in giving them faster access to better information, fewer manual steps, and more time for the strategic decisions that genuinely require human judgment.\u00a0<\/p>\n
George Ninikas,\u00a0SVP\u00a0of Sales and Accounts\u00a0and\u00a0Supply Chain Planning \u2013 Americas, ORTEC<\/p>\n
That balance of autonomy is not fixed. Manufacturers are increasingly able to dial it up or down based on comfort level and\u00a0track record. A single recurring task, such as reassigning a production run when a machine or team member is unavailable, might begin as a fully human-reviewed decision and gradually become automated once the system has\u00a0demonstrated\u00a0consistent, reliable judgment in that specific scenario.\u00a0<\/p>\n
The poll data points to an industry in transition rather than\u00a0one\u00a0that has arrived. With the largest segment of practitioners still in an exploratory phase, and the clearest near-term demand\u00a0centred\u00a0on dynamic, real-time decision support rather than full automation, the opportunity for manufacturing executives is to treat agentic AI as an extension of their planning teams, and not as a replacement for them.\u00a0\u00a0<\/p>\n
The technology\u2019s value lies less in removing people from the loop and more in giving them faster access to better information, fewer manual steps, and more time for the strategic decisions that genuinely require human judgment. That is a more grounded story than utopian automation or job displacement, and it is increasingly the one playing out on plant floors today.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"George Ninikas,\u00a0SVP\u00a0of Sales and Accounts\u00a0and\u00a0Supply Chain Planning \u2013 Americas for ORTEC,\u00a0examines why the future of manufacturing artificial intelligence…\n","protected":false},"author":2,"featured_media":137035,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,7493,4928],"class_list":["post-138216","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-agentic-artificial-intelligence","tag-insights"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/138216","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=138216"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/138216\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/137035"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=138216"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=138216"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=138216"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}