It can be very tempting for today’s organizations to assume that implementing 3rd-party AI agents into their current ERP system, or even into a specialized S&OP platform, will open up all sorts of new functions, insights, and possibilities, as if by magic.It’s important for business leaders and tech officers to vet any potential vendors by asking specific, informed questions. Rather than asking, “Do you have AI?” a better place to begin would be to ask, “Can you demonstrate governed, explainable, enterprise-ready multi-agent planning intelligence?” and learn the key components and functions to look for in the vendors’ responses.
As we’ve written recently, agentic AI will transform sales and operations planning as the technology develops, but there are 20 core requirements for effective multi-agent AI implementation for S&OP. Some of these requirements depend on the developers of the software or platform offering multi-agentic AI functionality, and some depend (at least partially) on the business or other organization wishing to implement agents for this purpose.
In this article we’re going to dive deeper into a core prerequisite for successful multi-agentic AI implementation for S&OP, which is data quality, timeliness, and synchronization.
For all its wondrous capabilities, multi-agentic AI doesn’t eliminate the consequences of poor information. In many cases it can amplify them and make things worse than before implementation. The programming axiom “GIGO” (garbage in, garbage out) has never been more appropriate. This truism states that the quality of a system’s output is directly determined by the quality of its input. In other words, no matter how perfect, logical, or advanced a program or system is, if it is fed incorrect, incomplete, poorly formatted, or nonsensical data, it will inevitably produce an incorrect, incomplete, flawed, or nonsensical result.
The fact that AI agents have become integrated as part of the system doesn’t change these underlying requirements. An organization, as well as any S&OP platform connected to it, must verify that its data is accurate, current, complete, and synchronized across all planning functions. Let’s briefly look at these factors individually.
These conditions are all interconnected. As we said above, data can be accurate but also outdated or incomplete. It can be current but inadequately synchronized among all tools, platforms, agents, and functions. Or similarly, data can be complete and accurate within one system but inconsistent with information being used elsewhere.
Any agentic AI S&OP platform must identify stale information for obvious reasons. However, not all stale data is necessarily problematic. There are degrees or relevant windows of “data freshness.” For example, an inventory record that is several hours old might be perfectly acceptable for one planning function but unacceptable for another, especially in industries where inventory changes rapidly. Along the same lines, a monthly forecast may remain valid for use in a strategic planning exercise or AI-assisted scenario even though a large, unexpected customer order has come in since the forecast was created.
So, the platform should have context-aware data freshness rules. For example, it might establish thresholds for different kinds of data:
When data falls outside acceptable windows, the platform should flag it before an AI agent uses it.
Data must be sufficiently “fresh” but it must also be effectively synchronized across functions within the organization (and outside where appropriate). Planning recommendations are time-sensitive. A demand agent using yesterday’s customer update while a supply agent uses today’s production plan can produce recommendations that appear logical but are operationally wrong. Let’s imagine a relatively simple scenario to illustrate this:
An AI demand agent receives an updated customer forecast at the end of Monday. Meanwhile, an AI supply agent updates its production plan on Tuesday morning after discovering that a key production line will be unavailable for several days.
If the demand agent continues working based on Monday’s information and the supply agent uses Tuesday’s production plan, each agent could produce a perfectly reasonable analysis based on the information available, but those analyses and suggested actions could come into conflict. The demand agent may recommend increasing production to satisfy a large customer order, while the supply agent might simultaneously recommend reducing, reallocating, or outsourcing production because in-house capacity has fallen. In this particular example, neither recommendation is necessarily “wrong” or irrational. The agents are merely operating from different points in time.
A multi-agent platform can’t simply ask whether a particular dataset exists… it must also determine when the dataset was generated, when it was updated, what relevant business events have occurred since the last update, and whether other planning functions are depending upon a compatible version of the information.
Effective data synchronization also means resolving conflicts between sources. For example, the demand planning system might indicate one forecast quantity while a key-account team’s latest customer commitment indicates another. Or, an ERP system might report one inventory level while a warehouse management system reflects a different quantity, if those systems are not able to communicate and synchronize data effectively.
An AI agent working from this data should not automatically choose whichever number appears most convenient. Instead, the platform should identify the conflict, determine the relevant source-of-truth rules, and either reconcile the discrepancy or flag and escalate it for human review.
The overall goal of implementing multi-agent AI for S&OP shouldn’t be to automate decision-making at any cost. It should be to enable making overall strategic and regular planning decisions faster, more consistent, and better informed. This requires that organizations build robust information discipline into everything they do. Like any computer system or human worker, AI agents are only as reliable as the data and the business context they are given.
Again, simply bolting on AI agents to enterprise data is insufficient and may actually exacerbate inherent problems in cases where information resources aren’t accurate, current, complete, and/or synchronized before agents use it for analytics and decision-making. This becomes even more important as the number of agents increases. When multiple agents interact, small data discrepancies can propagate through the system. For example, an out-of-date demand signal can influence an inventory recommendation, which in turn may influence supply, production, procurement, logistics, and financial decisions.
An effective multi-agent S&OP platform should include a dedicated data validation and synchronization process before the information is made available to autonomous agents for use. This process may differ from the below depending on an organization’s preferences and systems, but it should ideally:
Only after these checks are performed should information become available for agentic AI planning analysis or recommendations.
In today’s crazy-fast environment where planning decisions are becoming increasingly time-sensitive and automation and/or agentic AI is being employed, the ability for platforms and organizations to know when their data is trustworthy, when it is uncertain or incomplete, and when it may be out of sync is more important than ever before.
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