How to Turn Planning into a Winning Strategic Capability in Operations

Kelly Coutinho

Karishma Jobanputra

August 28, 2026 • 5 min read

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In many recent conversations with supply chain and operations leaders, we’ve seen two themes dominate: planning and AI-ready data. In fact, over the last three months, 45% of our discussions with the community have been about the same baseline ambition related to both of these: Leaders want to move faster, plan continuously, use AI more intelligently, and stop allowing the monthly planning cycle to dictate what the business can see or do.  

This convergence of planning and data is a topic we’ve been specifically focused on for a while. Planning sits at the center of The Loop, and it touches every Zero100 PowerThread (end-to-end agentic workflows) because it connects demand, supply, inventory, finance, and execution into one usable view of the business.  

For instance, in Signal-to-Plan, planning depends on historical demand, orders, promotions, channel activity, supplier constraints, and external market signals arriving at the right grain and cadence. In Inventory-to-Service, that same demand signal must connect to inventory positions, allocation rules, and network decisions before a stockout appears in service metrics. In Source-to-Supply, the forecast becomes the input that shapes capacity, materials, purchasing, and supplier commitments. 

Considering that planning touches everything, the data inputs here are critical in turning it into a “broader-than-single-function” strategic capability for operations. 

Forecasting Is the Foundation  

Every planning process requires a forecast. Decisions about inventory, production, labor, transportation, procurement, or service require a judgment based on historical patterns, current signals, and known events.  And while the volatility of global trade and geopolitics remains, resilience on offense does not render historical data meaningless.  

Ultimately, a trusted demand-history data product is valuable as a shared infrastructure for forecasting, inventory, allocation, replenishment, sourcing, and potentially commercial decisions. The unit of investment should increasingly be the reusable capability and the data products beneath it, not the individual planning process. This makes it an intelligence input shaping decisions across the operating system. If it’s wrong, delayed, incomplete, or disconnected from execution, the quality of downstream decisions is exposed.  

Looking into how leaders can avoid that, our data shows that 75% of companies have at least partly digitized data for demand planning. But this is not the same as data readiness. In member conversations, the same issue keeps surfacing: AI ambition is running ahead of the quality, structure, and trustworthiness of the data feeding decisions.  

So, breaking down the particular data inputs to forecasting, one of the most common is performance and historical data. Strengthening data foundations and specificity here, then, is vital. 

Not All Data Is Created Equal 

AI-enabled operations require, as we’ve written about, AI-ready data: data that is accessible, governed, contextualized, and safe for machines and people to use in decision-making. But forecasting requires a specific subset of AI-ready data. It needs dense, historical, decision-grade data that can support prediction at the level where operational choices are actually made. Enter high-fidelity data, which can be defined as being:  

1. Highly granular, captured at the right level of detail and cadence, with a true representation of the original business activity, without distortion from poor mapping or manual workarounds. 
2. Contextually complete, retaining metadata, timestamps, relationships, and business events.  
3. Low noise, meaning it is free from major errors, gaps, duplication, or corruption. 

The important concept is fidelity relative to the decision. For example, SKU-location-day might be essential for one decision and unnecessarily granular for another. A highly aggregated dataset could still be extremely high fidelity for a strategic capacity decision.  

Without this fidelity, AI may still produce an output, but planners will not trust it – Zero100 survey data shows that 44% of leaders resolve conflicting data through leadership intervention or manual reconciliation. This is how modern planning platforms still end up operating on spreadsheets, fragmented master data, and slow planning cycles. 

One retail company wanted to improve machine-learning-based forecasting but first had to create enough historical “gravity” for the algorithm to predict the future. It started with sales data, structured by time, channel, and product, then brought data together from different parts of the organization into one planning view. 

The challenge was fidelity, not just volume. At the portfolio level, one product had more than 70 SKUs. If those SKUs were not matched correctly, the algorithm saw fragmented demand instead of one coherent product or coherent product signal. The company linked those SKUs, enriching missing attributes using agents to help identify gaps, monitor data quality, and support reconciliation. The result was not perfect data, but usable, dense, historical data that could improve the quality of the forecast. 

No-Regrets Moves  

What does this mean in practice? Some no-regrets moves for leaders to strengthen intelligence inputs include: 

1. Identifying the planning data products that shape the most downstream decisions; consider historical demand, inventory positions, lead times, order history, promotions, constraints, service levels, and planning parameters.

2. Identifying tasks where data will feed automation and determining if existing data-quality conditions match your intended level of task autonomy (the Zero100 Autonomy Ladder can help here).

3. Hardening the identified data products. Define ownership, refresh logic, quality thresholds, exception handling, and governance. Focus first on the data that feeds forecasting and flows across multiple PowerThreads.

4. Matching fidelity to the decision. Define the grain, history, cadence, context, and quality required by the decision — and increase those requirements as autonomy increases.

5. Using agents carefully. They can enrich data, identify gaps, monitor anomalies, and accelerate reconciliation – our data shows a positive AI/data flywheel, where 83% of high AI-maturity companies report improving data quality (vs 58% of low-maturity companies) – but they do not replace the need for a strong foundation.