We’ve written recently about the 20 core requirements for effective multi-agent AI implementation for S&OP and IBP. Even in cases where AI is not currently being used, many of these requirements have broader application for anyone wishing to develop and sustain an effective, mature S&OP process, which is far more difficult than it sounds. Here, we’re going to examine the first core requirement in more detail, and explain why any effective S&OP strategy, particularly one deploying multiple AI agents, must first prioritize a high level of enterprise data access and connectivity.

Why enterprise data access and connectivity is essential before implementing multi-agentic AI for S&OP

Data connectivity is the foundation of effective S&OP, especially when multiple AI agents are involved. No matter how sophisticated they are claimed to be, AI agents are unable to reliably produce relevant, rational, data-based S&OP recommendations and analysis if they have access to only a fraction of an organization’s data. So, any effective S&OP strategy must begin with full (or sufficiently full) enterprise data access and connectivity. (Caveat: certain enterprise information can’t be made available to AI agents for legal and/or strategic reasons, as we’ll discuss in the “4 primary blockers” section below.)

Any S&OP platform being considered must connect to all of the planning data sources required for S&OP decisions, including ERP, planning systems, data warehouses, BI tools, promotion management systems, customer forecasts, supplier inputs, finance files, spreadsheets, and external signals where relevant.

When performed effectively, S&OP/IBP inherently crosses functional boundaries.

Before an organization evaluates how intelligent its AI agents are, it must ensure those agents can access the sufficient information required to understand the entire business within their relevant context window. Multi-agent AI cannot make good decisions from inadequate information.

S&OP must be owned by the E-team, and is an enterprise-wide decision-making process

One of the most common S&OP failings in today’s businesses (as evidenced by our recent survey) is that the sales and operation planning process is most commonly not owned and led by company leadership. Contrary to what many businesses believe (as confirmed by their workflows and processes), S&OP is not simply a demand forecasting or supply planning exercise. It must be fundamental to the entire organization.

Additionally, when implementing AI agents into S&OP processes, any agent-permitted decisions such as increasing production, changing inventory targets, adjusting a forecast, or prioritizing certain customers can affect multiple functions simultaneously. When S&OP data and systems are not sufficiently integrated and permissions granted, an AI agent might identify a demand increase and recommend additional inventory, but that recommendation could be inappropriate if it cannot see production constraints, working-capital targets, supplier limitations, financial priorities, or other mitigating factors.

Multiple AI agents increase the need for connected data

Depending on an organization’s deployment and prioritization of its AI tools, there may be multiple specialized agents within the S&OP process. These may include specialized demand, supply, inventory, finance, or exception-management agents. Individual agents may have different responsibilities, but their recommendations still depend on shared enterprise context.

An agent working from incomplete information may optimize its assigned objective while inadvertently creating problems elsewhere in the business. To be useful, S&OP AI agents need access to foundational operational information, including orders, inventory, purchasing, production, shipments, master data, and other transactional information, in order to understand what is actually happening in the business, rather than relying solely on plans or forecasts.

Access to existing planning systems (or updating them) is essential

Obviously, to be effective in S&OP, sufficient AI agent access to all relevant planning systems must be optimized. These systems may contain demand plans, supply plans, inventory strategies, production plans, capacity plans, and other forward-looking assumptions used by agents in analysis and decision making.

Agents need to view both ERP data (or its equivalent), which largely reflects operational reality, and planning data, which reflects intended future actions. They need to understand the gap between what the organization planned and what is actually occurring. Without forecast-system connectivity, an AI agent may recommend actions that conflict with existing assumptions, targets, or approved plans. Any existing planning systems or tools must either be updated with sufficient connectivity to allow AI agent access and use, or be replaced with more modern options.

Additional resources and tools that must be allowed AI agent access

• Data warehouses add analytical and historical context: Data warehouses often consolidate information from multiple operational systems and provide historical context. Historical demand, inventory performance, service levels, supply performance, and other trends can help AI agents distinguish isolated previous events or true anomalies from meaningful patterns.
• BI tools expose existing business intelligence and resources: Where possible, it’s important to facilitate AI agent connectivity to existing BI environments and dashboards. Most organizations have already invested significant effort in defining KPIs, reports, metrics, and analytical views. An S&OP platform should not force AI agents to ignore or recreate this institutional knowledge. Instead, agents should be able to incorporate established business metrics into their analysis.
• Promotion management system data can help explain demand volatility: Baseline forecasts alone may not capture the impact of promotions. Promotional calendars, planned campaigns, discounts, and other commercial activities can materially change demand. For example, an AI agent that cannot see and account for these inputs may interpret promotional demand as a sustainable trend and recommend inappropriate supply or inventory actions.
• Client forecasts provide external demand visibility: Client-provided forecasts (where applicable and available) can provide information that does not exist in internal demand data. These forecasts can reveal upcoming requirements, changes in customer purchasing patterns, or potential demand shifts. AI agents should be able to compare these forecasts with internal planning data and identify meaningful discrepancies for S&OP review.
• Supplier data adds visibility beyond internal plans: Similarly, any available supplier inputs should be incorporated into the S&OP data environment for use by AI agents (within established governance restraints, which we’ll cover below). Supplier information can include lead times, capacity, availability, constraints, commitments, and potential disruptions. Internal supply plans may assume materials will be available, while suppliers may have information indicating otherwise. Supplier connectivity helps AI agents assess whether proposed demand or inventory responses are operationally feasible.
• Finance tools and data connect operational decisions to business goals: S&OP decisions have financial consequences. Financial information cannot be treated as an afterthought and must be incorporated into AI agents’ permission framework. Decisions about inventory, production, service levels, sourcing, and capacity directly affect revenue, margins, working capital, and costs. An operationally optimal recommendation may not be financially optimal. Connecting financial data enables AI agents to better evaluate potential tradeoffs rather than working toward a single operational metric.
• Looking beyond the enterprise: Relevant external signals can provide early indications of changes affecting demand or supply. Depending on the industry, these might include market conditions, economic indicators, severe weather events, commodity trends or shortages, logistics conditions, socio-political upheaval or uncertainty, or other factors. Relevant external signals can help AI agents identify changes or trends before they become visible via internal transactional data.

Build the data foundation before scaling AI Agents

It can be tempting to “bolt on” 3rd party AI agents to existing S&OP systems and workflows, particularly when the “do you have AI?” question is seemingly all any potential clients ask. However, it’s important to build a solid foundation of appropriate data access and connectivity before widespread deployment of AI agents.

Remember, using AI agents does not compensate for poor data design or inadequate access. Multiple agents can actually worsen the consequences of fragmented or inconsistent data because each agent may make decisions based on a different view of the business.

Organizations should prioritize integration, data accessibility, governance, and consistent definitions before expanding their agent architecture.

4 primary blockers to enterprise-wide data access for multi-agentic S&OP

Let’s go over 4 of the primary reasons why enterprise-wide connectivity and data access may be inadequate or difficult when implementing agentic AI into an organization’s S&OP strategy.

1. Fragmented systems and disconnected data sources

Today’s S&OP data is typically distributed across legacy ERP, planning systems, data warehouses, BI tools, spreadsheets, promotion systems, supplier portals, finance files, and even external sources. These systems often use different architectures, data models, identifiers, permissions, and update schedules, to say nothing of how well they “play together” within an S&OP or IBP platform.

Furthermore, some of the most important S&OP inputs may never reach formal enterprise systems. Planners and functional teams still often maintain spreadsheets containing overrides, assumptions, custom formulas, customer intelligence, supply constraints, scenarios, and management adjustments. These files can change frequently, exist in multiple versions, and depend on human interpretation. If an AI platform ignores them or can’t access them, it misses important business context.

When data sources are not sufficiently connected and systems are fragmented, any team, including those employing AI agents, can only have a partial, fragmented view of the business. This makes it impossible to establish a consistent picture of demand, supply, inventory, capacity, service, and financial performance. In other words, even though the data exists, it is not connected in a way that allows teams, tools, and agents to access it effectively and use it for reasoning, planning, and executing.

2. Inconsistent data definitions and poor data quality

Within an organization’s systems, different functions may define fundamental concepts differently, including product, customer, demand, inventory, revenue, service level, or forecast parameters. Duplicate records, missing fields, outdated information, inconsistent hierarchies, and conflicting values make things even more complicated.

Where connected data is not necessarily trustworthy or consistent as viewed by an AI agent, rational recommendations or decisions are impossible. Multi-agent AI depends on reliable context. If two agents receive different definitions of the same metric, they can produce conflicting actions even when they are technically accessing the same underlying data, and both actions might be equally valid based on the available information.

3. Legacy integration architecture and difficult access

Many enterprises still rely on legacy applications, custom databases, file-based processes, and point-to-point integrations that were not designed or intended for real-time, enterprise-wide AI access. Certain important information may require manual extraction, batch processing, or specialized technical knowledge to retrieve. This creates delays and makes it difficult for AI agents to access current information when S&OP decisions require it.

4. Security, governance, and ownership constraints

Despite the current sweeping trend toward “AI everything,” much of an enterprise’s data simply can’t be exposed to every AI agent. Things like sensitive financial records, certain customer data, supplier details, and commercially or strategically sensitive information require appropriate access controls. Insufficient governance here can create unacceptable security and compliance risks.

However, excessively restricted access can leave agents with an incomplete picture, without the information they need to make recommendations or take actions. Enterprises must understand how to balance sufficiently broad data accessibility with controlled, governed access, ownership, lineage, and permissions to reach their multi-agent S&OP functionality goals.

We can see from these blockers that sufficient, well-governed enterprise data access is not simply an integration problem. For multi-agentic AI to meaningfully assist with S&OP, the platform needs to make the correct data accessible, current, consistent, appropriately governed, and understandable in context. Otherwise, agents may be highly capable at reasoning but still make poor recommendations because they are reasoning from an incomplete or contradictory view of the enterprise based on the data available.

Intelligent S&OP starts with seeing the whole business

AI capability is only one component of effective AI-powered S&OP. The quality of agents’ recommendations and decisions depends heavily on the breadth, accuracy, priority, and interconnectivity of the underlying data. Rather than asking “How many AI agents should we deploy for S&OP?” enterprises should first ask whether their AI agents can actually access and appropriately prioritize the information they need to understand the entire business and its goals.