Overwhelmed by demand data? How retailers can harness AI + execution systems

Summary: The future of retail resilience lies not in collecting more data, but in connecting insight to execution. Retailers are drowning in demand data. Every click and cart adds signals faster than legacy systems can act on them. AI and intelligent execution technology are emerging as the fix to turning data overload into real-time visibility and faster, smarter decisions.

The new reality: retail is drowning in demand data

Retailers are no longer struggling with a lack of data but an excess of it.

Zero100 reports that demand-chain data is now six times larger than supply-chain data and growing faster every year.

Every consumer click, cart and channel adds new signals, but most legacy systems weren’t designed to handle this velocity. Instead of enabling smarter decisions, the data flood often slows execution, leaving teams reacting instead of anticipating.

Retail leaders are beginning to realize that visibility without action is no longer enough.

Why data volume doesn’t equal visibility

Having more data doesn’t automatically create better decisions. In fact, for many retailers, it has the opposite effect.

While leadership teams invest heavily in analytics platforms, insights still take days or weeks to reach the warehouse, the store floor or the last-mile delivery partner.

95% of technology leaders said trustworthy data is “needed now more than ever.” Yet, few have built the operational bridges to act on it when it matters most. - Zero100’s 2024 CEO study

This gap between knowing and doing is where AI and intelligent execution systems step in.

From overwhelm to orchestration: the role of AI

AI can transform how retailers process, prioritize and act on demand signals. It learns which inputs matter most, automates repetitive decisions and allows teams to respond to real-world conditions in near real time.

For many, the first proof point lies in AI demand forecasting in retail – using machine learning to sense shifts in buying behavior earlier and translate them into execution decisions faster than traditional planning systems can react.

The AI-in-Retail market, valued at $11.6 billion in 2024, is projected to reach $40.7 billion by 2030 (Grand View Research, 2024), underscoring the growing recognition that prediction alone isn’t enough. Retailers now need systems that can orchestrate action.

The future has moved beyond dashboards; it’s now about AI-powered loops that connect insight to immediate execution.

How leading retailers are using AI to stay ahead

Across the industry, leading retailers are pairing AI with adaptive execution platforms to close the “decision gap.”

They’re using intelligent order management to re-route stock before a shortage hits, AI-driven warehouse systems to re-balance capacity in real time and predictive analytics to pre-position inventory ahead of weather or event-driven surges.

Still, progress isn’t universal. 74% of companies struggle to scale AI value across their operations (BCG, 2024).

The key difference lies in execution maturity; how well AI is integrated into operational workflows, not just analytics teams.

The companies that get ahead will be those who treat execution as a strategic capability, not a downstream process.

Execution readiness checklist for retailers

1. Establish your critical demand signals: decide which data points really matter (e.g., online search trends, store foot traffic, SKU-level movement) and ensure they are flowing in real time.
2. Ensure data trust and speed: clean, integrated data is essential. Even the best AI will suffer if data is fragmented or badly timed. (See how only some companies have scaled AI in operations).
3. Select the right execution systems: choose platforms or modules that connect prediction → decision → action (order management, replenishment, dynamic allocation).
4. Embed decision-loops, not just dashboards: make sure insights trigger workflows/alerts, not just reports.
5. Define metrics for action speed & outcome: track not just forecast accuracy, but time from signal to action and impact (e.g., fewer stock-outs, faster restocks, improved margin).
6. Pilot and scale: start with one high-impact use-case (e.g., out-of-stock reduction) before scaling across categories/regions.
7. Organize for change and governance: assign ownership, build cross-functional teams (analytics + operations + IT) and address skills/data culture early.
8. Iterate and refine: once the loop is running, refine which signals matter, which actions are effective and how to continuously optimize.

The Infios perspective: turning data into decisions

At Infios, we see the future of retail execution as intelligence in motion. Our Intelligent Supply Chain Execution (ISCE) framework connects every operational layer—from forecasting to fulfillment—into one adaptive, AI-driven ecosystem.

When execution becomes intelligent, data stops being a burden and starts becoming a competitive advantage. Retailers can move faster, make better decisions and meet customers where they are instantly.

It’s the foundation for truly real-time retail operations, where data, AI and execution flow together to create resilience at scale.