AI Expectations Are Rising Fast, But Results Aren’t There Yet

Why It Matters

The widening expectation‑performance gap threatens CPG manufacturers’ investment efficiency and could delay industry‑wide digital transformation.

Key Takeaways

• 70% see AI ROI below 20% today.
• Over 40% cite skills gaps as major barrier.
• Legacy systems limit AI value in brownfield sites.
• By 2030, >33% expect AI to drive 50% returns.
• Inefficiencies cost >20% of product expenses now.

Pulse Analysis

Manufacturers are caught in a paradox: AI hype is at an all‑time high, yet measurable gains remain modest. The Schneider Electric survey underscores that while CPG firms are budgeting for AI‑driven productivity, 70% see returns under 20%, and a sizable share struggle to break even. This disconnect reflects a broader market trend where lofty forecasts outpace the operational realities of complex, asset‑heavy environments. Understanding the gap is essential for investors and executives evaluating the true pace of digital transformation.

The root causes are less about algorithms and more about foundations. Fragmented data, outdated automation platforms, and a shortage of skilled data scientists create a brittle substrate for AI models. When operational data is noisy or siloed, predictive insights lose accuracy, leading to under‑performance and wasted spend. Moreover, legacy brownfield sites often lack the connectivity required for real‑time analytics, forcing companies to retrofit rather than redesign. These structural challenges translate into tangible cost leaks—downtime, rework, and delays that now account for more than one‑fifth of product expenses.

Addressing the shortfall demands a coordinated industry response. Standardized data architectures, open‑source interoperability frameworks, and upskilling programs can accelerate AI maturity. Schneider Electric’s call for shared standards and transparent collaboration signals a shift toward ecosystem‑level solutions rather than isolated pilots. Companies that invest early in modernizing their data backbone and cultivating talent are poised to capture the projected 50% ROI by 2030, while laggards risk falling further behind as competitors reap efficiency gains.