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This article was originally written for and published by Supply & Demand Chain Executive.
After decades of best-of-breed systems operating in isolation, agentic AI is making standalone software obsolete. Its requirements are forcing a reckoning with enterprise architecture that supply chain leaders can no longer ignore.
Moving past the hype cycle, agentic AI is entering widespread adoption. According to the 2025 Gartner “Market Guide for PLM Software in Discrete Manufacturing Industries,” this year, more than 80% of PLM vendors will incorporate AI capabilities into their platforms, compared with less than 40% in 2025. Here’s what that stat doesn’t capture: agentic AI doesn’t just add features to existing systems. It fundamentally changes what software delivers.
Why standalone systems can't compete
Consider a typical scenario facing supply chain executives today. A critical component supplier announces a 30% tariff-driven price increase. Someone manually pulls procurement data, cross-references engineering specs in PLM, checks ERP inventory, reviews CRM customer commitments, and attempts to reconcile everything in spreadsheets. Days turn to weeks. Margins erode while leadership has to wait to see the complete picture.
No amount of better workflows or skilled analysts solves this. It's an architecture problem. Standalone systems rarely communicate in real time. They create data islands requiring human intermediaries to bridge gaps. Implementing Agentic AI exposes this limitation with brutal clarity.
Unlike generative AI that responds to prompts, agentic AI can take autonomous action within defined parameters. An agent optimizing supply chain decisions needs simultaneous access to engineering data, quality records, supplier performance, customer commitments, and financial constraints. If that data lives in five disconnected systems, the agent’s ability to function is compromised. It's like hiring an expert operations manager but only allowing them to look at one system per day.
The connected intelligence requirement
This coming generation of AI agents won't live inside individual applications. They'll coordinate across entire ecosystems, turning fragmented processes into intelligent networks. Software platforms extending data across enterprises will dominate while isolated tools fade.
Consider predictive inventory management. An effective agent, or group of agents across apps analyze demand patterns, component availability, supplier lead times, quality results, and customer forecasts. When shortages appear, it triggers procurement workflows, alerts production planning, notifies affected customers, and recommends alternative sourcing.
This orchestration requires connected data and integrated workflows across the supply chain and tech stack which standalone software can’t deliver. Manufacturers with integrated data significantly reduce development cycles because agents access comprehensive, connected data rather than siloed information. When systems no longer operate in isolation, time-to-market improvements scale with agents automatically cross-referencing engineering changes with supplier capacity, regulatory requirements, production schedules, and customer delivery commitments.
The infrastructure mistake executives make
However, this connected intelligence creates a dangerous misconception. Some executives see AI agents performing sophisticated analysis and assume the agents themselves are the solution, that powerful AI can simply work around fragmented systems. If the agent is smart enough, why invest in expensive platform integration?
Here's why that thinking fails.
Having implemented legacy systems for manufacturers during my Accenture years, I've seen what happens when companies chase technology without infrastructure foundations. Agents without structured systems are like a Formula 1 car with no track to race on. The car is impressive, but without guardrails, you're headed for disaster.
The platform provides those guardrails, enforcing the business rules, compliance requirements, and process controls that agents need to operate safely. This is especially critical in supply chain operations where regulatory compliance and audit trails aren't optional. Consider a manufacturer with a decade of supplier quality inspection records. An agent analyzing historical data might notice a compliance field completed less than 50% of the time without business impact. Based on this pattern, the agent determines the field is unimportant and stops prompting for completion.
But what if that field is an FDA reporting requirement that hasn't been audited yet? The moment inspection occurs, missing data becomes a compliance violation shutting down your entire supply chain. A proper platform enforces which fields are mandatory for agents, regardless of historical patterns. The agent becomes incredibly effective once it understands which processes are non-negotiable.
The governance reality
Can your current systems automatically control which AI agents access proprietary supplier agreements or customer pricing? If not, you're not ready for agentic AI at scale. Just as companies have focused on user access, least privilege and protecting confidential and PII data for their employees, the same regimen needs to be applied for agents.
Enterprise platforms provide the governance structure agents desperately need. Without it, you face two bad options: you can't enforce permissions at all, or you spend enormous effort manually recreating permissions in AI models. When permissions inevitably change, agents access data they shouldn't, or make decisions based on outdated rules.
When an auditor asks for the decision trail on a supplier change that impacted customer deliveries, you need more than "the AI agent handled it." You need documented processes, approval workflows, and clear accountability. This is what robust platforms deliver, and it's non-negotiable in supply chain operations.
Real business value emerges when supply chain data flows seamlessly across PLM, ERP, procurement, and logistics through a unified backbone. Agents can then conduct the predictive analysis and autonomous coordination that supply chain executives expect, identifying which products tariffs affect before margins erode, determining which parts need alternate sourcing before shortages hit, and adjusting production schedules when supplier capacity shifts.
The partnership imperative
Successful agentic AI demands a partnership between intelligence and infrastructure. Agents, guided by humans, provide the decision-making layer; enterprise platforms provide the operational foundation. Neither works effectively without the other in supply chain environments where speed, accuracy, and compliance determine success.
The manufacturers and supply chain organizations that recognize this interdependence now will build sustainable competitive advantages. Those that chase AI without addressing their fragmented system architecture will watch competitors pull ahead while they manage unreliable automation built on unstable foundations.
The era of standalone software is ending. The supply chain leaders who understand that connected intelligence requires connected infrastructure will define the next decade of competitive advantage.
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