In the electronics industry, where complexity and speed are essential, artificial intelligence (AI) stands out as a transformative force poised to revolutionize the field. Yet, despite significant investments and high expectations, many organizations find themselves grappling with an uncomfortable reality: the true value of AI for supply chains remains elusive at scale.

Great expectations, harsh realities

Gartner research found that 65% of CEOs in supply chain-intensive sectors believe the next “business era” will be defined by AI. An impressive 73% believe that AI will emerge as the most transformative technology for their businesses in the next three to five years. 

Electronics companies, with their global networks and rapid product cycles, are especially attuned to these possibilities. Leaders expect AI to automate processes, enhance coordination across functions, and boost productivity.

However, reality often falls short. Many organizations report only incremental gains, with AI initiatives remaining siloed in individual functions and rarely achieving cross-functional integration. 

Of concern, 77% of supply chain leaders say that none or only some of their AI investments are integrated cross-functionally across their supply chain organizations, and just 17% have completed many AI deployments at scale in the past year. 

The result is a persistent gap between expectations and outcomes, and growing pressure on supply chain leaders to demonstrate real, scalable value.

The vision beyond cost savings

A key reason for this gap lies in how organizations define their AI vision. Too often, the focus is narrowly on cost savings and efficiency. While these are important, they place an artificial ceiling on AI’s potential. 

Electronics industry leaders who have achieved more transformative results have broadened their AI vision beyond profitability to encompass growth, resilience, and the creation of new business models.

One tangible way to steer the AI vision towards this top-line and bottom-line growth is by incorporating key performance indicators (KPIs) that reflect both operational efficiency and strategic development.

Since these metrics tie back to both cost savings and revenue growth, it makes it easier to communicate how the supply chain organization is realizing value from AI.

By linking every AI initiative to tangible business outcomes, such as expanding into new markets, managing risk across procurement and logistics, and supporting new product launches, companies can move beyond incremental improvements.

Anchored by the goal of achieving excellence in customer order fulfillment, we’ve seen electronic manufacturers drive measurable results, including an increase in on-time, in-full deliveries, and a reduction of up to 60% in decision-making time for frontline workers, as well as a 30% boost in process efficiency.

The real bottleneck of scaling AI

Many supply chain leaders believe that cultural resistance to AI adoption is a key challenge when moving an AI investment from the pilot stage to full-scale implementation. Yet recent Gartner survey data paints a different picture of receptiveness to using AI.

In fact, 94% of supply chain leaders report being entirely receptive to using AI tools, and 86% identify valuable use cases for incorporating AI tools into their existing workflows.

The main challenge is not cultural resistance. Instead, it’s the difficulty of integrating AI and overcoming persistent process bottlenecks. Only about one-third of supply chain leaders report seamless integration of AI tools in their existing workflows.

In electronics supply chains, these bottlenecks can occur at every stage, including demand forecasting errors leading to stockouts, price fluctuations in raw materials, quality control issues in manufacturing, and transportation delays.

To successfully integrate AI solutions into these processes, it is essential to first address the fundamental workflow challenges. Failing to take this critical step may lead to increased friction and significantly hinder the adoption process.

Lowering the barrier to AI adoption

To unlock AI’s full potential, organizations must focus on lowering the barrier to entry for widespread adoption. Three approaches are particularly effective:

1. Standardize complex processes: By streamlining and standardizing processes, such as demand forecasting, organizations make it easier for staff to incorporate AI tools. For instance, standardizing demand planning across regions enables AI to more accurately predict inventory needs, resulting in consistent and optimized stock levels.
2. Automate routine tasks: Freeing up employees from repetitive, low-risk tasks enables them to focus on higher-value activities. In the electronics sector, automating data calculations, order processing, or routine quality checks with AI enables skilled workers to devote more attention to strategic initiatives, such as new product introductions or risk mitigation.
3. Designate AI stewards: Appointing “AI stewards”—individuals who bridge the gap between technology teams and end users—ensures that AI solutions are tailored to real business needs. These stewards help manage change, provide training, and champion adoption, reinforcing a culture of innovation and continuous improvement.

These strategies can help significantly improve efficiency, customer satisfaction, and supply chain transparency.

From hype to scalable impact

Despite the clear promise of AI, electronics supply chains remain at a crossroads. The gap between high expectations and day-to-day reality is widening, as organizations encounter persistent barriers to scaling and integrating AI solutions. 

Without a fundamental shift in approach, one that prioritizes seamless integration, process standardization, and empowered AI champions, organizations risk falling behind in a rapidly evolving landscape.