AI in Manufacturing: The Gap Between Strategy and Execution is Wider Than You Think

AI is everywhere in business strategy. Every leadership team is talking about it, every roadmap includes it and every forward-looking organisation claims to use it. Yet, despite all the talk, the gap between AI strategy and real-world execution remains stubbornly wide.

The answer to closing the gap is to focus on people and culture first, then apply the right AI solutions. XpertRule’s Akeel Attar and Darren Falconer explain how manufacturers can turn strategy into action using Decision Intelligence.

Studies suggest that most manufacturers, around four in five, are using or exploring AI in their business operations, with the majority citing AI as a top priority in their strategy. But, while leaders are setting ambitious AI goals, fewer than one in ten describe their implementations as successful.

The first step in closing the gap between strategy and execution is understanding why it exists in the first place.

Why AI Strategies Stall

Several key barriers are slowing down the adoption of AI in manufacturing and preventing companies from gaining the expected benefits. How many of the following describes your company?

• Fragmented Data Across Functions – Manufacturing operations generate vast amounts of data across production, quality control, inventory management and the supply chain. However, much of this data is locked in disparate systems that don’t communicate with each other. AI models need access to integrated data streams to deliver high-value, accurate insights, yet most companies struggle with data access and integration.
• Lack of Critical Data for High-Stakes AI Modeling – Industrial operations often involve processes where the consequences of failure are so costly or dangerous that failure isn’t an option. This results in a lack of real-world data to train AI models to perform risk mitigation or failure analysis. Harnessing AI to capture and digitise the explicit and tacit knowledge of skilled engineers can help fill this need, as we explored in our article on plugging manufacturing’s brain drain.
• Siloed AI Implementations – AI is too often treated as an IT-driven project rather than an enterprise-wide transformation. Confining AI to a single department severely limits its potential impact, with the organisation missing out on broader operational benefits. Successful adoption requires cross-functional collaboration between IT, managers, engineers and frontline operators.
• Lack of Internal Knowledge – Many manufacturers are rushing to adopt AI due to competitive pressure or fear of being left behind but lack the in-house expertise to select the right use case and models, integrate AI with existing systems or interpret AI-generated insights effectively.
• AI That Doesn’t Fit Manufacturing Realities – Many AI solutions are developed as black box models, lacking transparency and without a deep understanding of the complexities of industrial environments. Manufacturers need to start demanding Responsible AI models that are explainable, auditable, accurate, safe, and having the necessary contextual knowledge to generate reliable insights and performance improvement and control actions.
• Lack of Employee Buy-In – Workers are understandably cautious about using AI, given frequent headlines about how it may impact the job market. When any technology is imposed without clear communication or involvement from the workforce, resistance follows. AI is no different. Successful adoption relies on engaging employees early, addressing concerns and showing how AI is a tool that supports employees, not one that replaces them.

The Foundation: People and Culture First

A major barrier to AI adoption isn’t just fragmented, incomplete or inconsistent data, it’s that teams don’t see how their role fits into the bigger picture. Within organisations, especially large ones, information is often treated as transactional. Once entered into a system or passed to the next department, responsibility for it ends. The consequences of this mindset go far beyond just AI.

Workers need to understand how the data they generate or capture directly influences business performance and success elsewhere in the operation. Missing or incomplete data, for example, can create delays on the factory floor, quality issues or costly errors in production.

Those seeing bottom-line impact through AI adoption follow a top-down approach, starting with committed leadership. “The more we see organizations using AI, the more we recognize that it takes a top-down process to really move the needle,” notes Alexander Sukharevsky, Senior partner at McKinsey.

“Effective AI implementation starts with a fully committed C-suite and, ideally, an engaged board. Many companies’ instinct is to delegate implementation to the IT or digital department, but over and over again, this turns out to be a recipe for failure.”

Decision Intelligence: The Missing Link Between Strategy and Execution

The siloed nature of manufacturing operations is especially evident in the disconnect between operational technologies (OT) on the shopfloor – e.g. data collection, monitoring and control and enterprise IT systems like procurement, sales and supply chain management. This divide is a leading cause of why many AI strategies struggle to deliver impact. More and more manufacturers are now turning to Decision Intelligence (DI) to provide the visibility needed to bridge this gap, as we discussed in this earlier article on building trust in AI through a decision-centric approach.

DI equips teams with sharper insights to improve decision-making by unifying operational data across machines, systems and business functions into a single source of truth, enabling real-time, data-driven decision-making.

One of XpertRule’s customers is using our decision-intelligence powered manufacturing solution XpertFactory to increase agility and efficiencies across their entire operation.

One way is translating complex numerical information into user-friendly actionable insights. Previously, only a handful of specialists could interpret the codes but with XpertFactory, DI translates these numbers into clear, readable text that everyone across the organisation can understand. 

The manufacturer has enhanced its operational efficiency, responsiveness and supply chain visibility, resulting in tangible environmental and financial gains.

This is the power of decision intelligence: raw data becomes insights, insights drive actions, and actions create measure value.

How to Move from AI Strategy to Execution

AI might process information faster than humans, but it can’t execute strategy. The challenge for manufacturers isn’t just adopting AI but embedding it into their operations in a way that drives real benefit.

Based on our experience, companies that succeed in AI adoption focus on these five priorities:

1. Align AI with Business Goals – Don’t chase AI for AI’s sake. Start with a clear problem, such as reducing waste, improving quality or optimising production scheduling. AI adoption should be driven by measurable business goals.
2. Connect the Right Data Sources – Siloed data is the biggest barrier to AI success. Integrate shop-floor and enterprise data into a unified system to ensure AI models have the right inputs to generate useful insights and actions.
3. Build Internal AI Capabilities – Invest in training your workforce to understand, trust and apply AI insights and performance improvement actions. Without internal expertise, AI remains an abstract concept rather than a practical tool for daily decision-making.
4. Involve the Entire Workforce – Engage production teams, engineers and managers early to ensure AI is implemented with real operational challenges in mind.
5. Embed AI into Daily Decision-Making – AI delivers the most value when integrated into day-to-day operations. Decision Intelligence helps convert raw data into actionable insights, enabling employees to make informed decisions with confidence.

By taking this structured approach, manufacturers can move beyond AI pilots and proof of concepts to practical, scalable solutions that improve efficiency, quality and competitiveness.

This article is part of our new series, Reality Check: What AI Really Means for Manufacturing, designed to inform, inspire and help you implement AI in your manufacturing operation.

This article helps you create a robust, results-driven AI strategy. In our next article – You’re Being Killed by Your Product Recalls – we’ll demonstrate how to put that strategy into action, showing how AI can deliver long-term financial and environmental results.