It’s no secret that trust is the foundation for successful AI adoption. By addressing skepticism, prioritising data quality and ensuring algorithms are explainable and auditable, AI can become a powerful force-multiplier in manufacturing operations.
Manufacturers are increasingly looking to AI to boost efficiency, streamline operations and automate routine tasks, with 75% planning to step up their AI spending in 2025. However, much of this attention is focused on Generative AI – something that we believe is poorly suited to factory settings.
Part of this misalignment stems from a lack of understanding of AI’s practical applications in industry. With only 7% of manufacturing leaders feeling “very knowledgeable” about AI applications, skepticism and trust issues loom large.
Feedback from vendors and end-users consistently points to trust as a leading barrier to adoption. Without trust, AI cannot deliver on its full potential, leaving many manufacturers hesitant to go beyond pilot projects, XpertRule’s Technical Director, Darren Falconer explores this further…
Overcoming the AI ‘Fear Factor’
The portrayal of AI in the media has long been dominated by dystopian headlines and Hollywood blockbusters, with fears of mass unemployment and doomsday narratives. For manufacturers, this continuous, subliminal bombardment creates a trust deficit before any AI project even begins.
Business leaders are having to overcome not only technical hurdles but also the deep-seated skepticism that AI solutions are uncontrollable or inherently risky. To counter this, companies must approach AI with transparency and explainability at every stage, showing that AI is a tool to amplify human capability not replace it.
For a simple comparison, think about cruise control in a car. [within cars today,]Traditional cruise control maintains a set speed but that’s all. Compare that to adaptive cruise control, which considers real-time conditions, adapts to your driving preferences and responds intelligently. Similarly, AI in manufacturing must adapt to the unique needs and complexities of each operation.
For those implementing these systems, understanding the ‘mechanics’ – how algorithms interact with data inputs and external influences – is a vital part of building trust. Explainable AI bridges the gap between automation and operator oversight, providing a clear view of how the system reacts and adapts. This clarity increases confidence among users, fostering trust in AI’s outputs.
But of course, building trust also requires a mindset shift – from a data-centric focus to a decision-centric approach.
Trust Starts with Decisions, Not Data
A common misstep in AI adoption is starting with the data instead of focusing on the desired outcomes. Many manufacturers think, We have all this data – what can we do with it? However, this approach often leads to complex systems that lack focus, transparency, fail to deliver meaningful outcomes and reinforce doubt over AI’s value.
A decision-centric approach begins by asking, What do we want to achieve, and what decisions need to be made to deliver those outcomes? Only then should businesses ask, What data supports those decisions and what are the models linking these decisions to this data?
From there, manufacturers must focus on ensuring data quality – calibrating sensors, cleaning data streams, validating inputs and standardising formats. Remember, the vast majority of AI success lies in data preparation and only a small percentage in the modeling itself.
Imagine a manufacturer aiming to improve quality control. They might gather extensive data from every step of the production process to find possible defects, leading to an overwhelming volume of disjointed data with no clear path to action.
Using a decision-centric approach, they would:
- Define the Goal: Improve product quality and aim to reduce defects by 10% over the next quarter.
- Identify Key Decisions: What factors directly impact product quality? What parameters should trigger quality checks? How can inspection processes be optimised to catch defects earlier? What actions should be taken when deviations are detected?
- Use AI to model the Outcomes: Build AI models that analyse historical production data , to discover explainable patterns relating outcomes to metrics like machine settings, material consistency or environmental conditions. The system can then use these models in real time to flag anomalies that indicate potential defects and recommend adjustments to maintain product quality.
This clarity in purpose makes AI implementations transparent, explainable and, ultimately, more trustworthy. It also provides a clear framework for measuring success, helping to build greater confidence from engineers, users and management alike.
Decision Intelligence – The Missing Link
A key factor in building trust is recognising that AI doesn’t replace human insights and experience – quite the opposite. Human operators and engineers bring a level of expertise, contextual knowledge and intuition that machines cannot replicate. Having a ‘human in the loop’ is therefore critical to an AI system’s effectiveness.
Decision Intelligence connects Explainable AI principles with operational trustworthiness by embedding human oversight at its core. For example, experienced technicians possess knowledge built up over years of practice. While they can’t be everywhere at once, their expertise can be integrated into AI systems to automate routine decisions while reserving complex or ambiguous scenarios for human intervention.
This balance between human and machine intelligence ensures AI systems remain transparent, reliable and dynamic. It also enables manufacturers to scale the knowledge of their experts, reducing variability across shifts and locations while maintaining trust and accountability.
From Pilots to Trusted Partner
For AI adoption to move from pilot projects to the heart of manufacturing operations, trust must come first. A decision-centric approach offers a practical pathway to achieve this, ensuring AI systems are transparent, aligned with business goals and designed to augment human expertise.
When manufacturers trust their AI systems, they can harness the technology’s full potential, creating new opportunities for efficiency, resilience and competitive advantage. Decision Intelligence becomes the connector between Explainable AI and operational trust, moving AI from being perceived as a risk to becoming a trusted partner.