-Adam Sharman, CEO LMAC Group APAC

 The manufacturing sector stands at the precipice of an AI revolution that promises to transform everything from production efficiency to supply chain management.

Yet for many business leaders, the question isn’t whether to adopt AI, but how to prepare their organisations to extract maximum value from these powerful technologies. The answer lies not in the sophistication of the AI itself, but in the quality of the foundation upon which it’s built.

The Data Foundation Imperative

Before any AI system can deliver meaningful insights, your manufacturing operation must first establish a robust data infrastructure.

This begins with conducting a comprehensive audit of your existing data landscape. Map out where data is generated across your operation—from sensor readings on the factory floor to inventory management systems, quality control metrics, and customer order patterns.

Many manufacturers discover they’re sitting on vast amounts of valuable data that remains siloed across different departments and systems.

The key is achieving data integration and standardisation. Legacy systems often speak different languages, storing similar information in incompatible formats.

A machine’s operational data might be recorded in one system while maintenance records exist in another, making it impossible for AI to identify patterns between equipment performance and maintenance needs. Investing in data integration platforms that can unify these disparate sources creates the coherent dataset that AI algorithms require to function effectively.

Data quality represents another critical pillar. AI systems amplify the characteristics of the data they’re trained on—if your data contains errors, inconsistencies, or gaps, your AI will perpetuate and magnify these problems. Implement rigorous data validation processes, establish clear data governance protocols, and create systems for continuous data quality monitoring.

This might involve automated checks for outliers, regular audits of data entry processes, and establishing clear accountability for data accuracy across teams.

Strategic AI Implementation Planning

Successful AI adoption requires a strategic approach that aligns technology deployment with business objectives. Rather than pursuing AI for its own sake, identify specific pain points where AI can deliver measurable value.

Common high-impact areas in manufacturing include predictive maintenance, quality control optimisation, demand forecasting, and supply chain efficiency.

Start with pilot projects that offer clear success metrics and manageable scope. A predictive maintenance system for critical equipment, for example, provides tangible ROI through reduced downtime and maintenance costs while serving as a proving ground for broader AI initiatives.

These early wins build organisational confidence and provide valuable lessons for larger deployments.

Consider the interconnected nature of manufacturing processes when planning AI implementations. An AI system optimising production scheduling must integrate with inventory management, quality control, and shipping logistics to deliver comprehensive value.

This systems thinking approach prevents the creation of isolated AI solutions that optimise individual processes at the expense of overall efficiency.

Workforce Transformation and Change Management

The human element remains crucial to AI success in manufacturing. While AI can automate certain tasks and provide powerful insights, it requires skilled professionals to interpret results, make strategic decisions, and maintain the underlying systems.

This necessitates a proactive approach to workforce development that goes beyond traditional training programs.

Identify the roles that will be most impacted by AI implementation and develop comprehensive reskilling programs. Machine operators might need training in interpreting AI-generated insights about equipment performance, while quality control specialists must learn to work alongside AI systems that can detect defects at superhuman speeds.

The goal isn’t to replace human expertise but to augment it with AI capabilities.

Create cross-functional teams that include IT specialists, operations managers, and frontline workers. This collaborative approach ensures that AI implementations address real operational challenges rather than theoretical problems.

It also helps identify potential resistance to change early in the process, allowing for targeted interventions that smooth the transition.

Infrastructure and Technology Readiness

Modern AI applications demand robust technological infrastructure that many traditional manufacturing facilities lack. Evaluate your current IT capabilities against the requirements of AI systems, including computational power, data storage capacity, and network connectivity. Edge computing solutions may be necessary for real-time AI applications on the factory floor, while cloud-based systems can provide the scalability needed for enterprise-wide AI deployment.

Cybersecurity considerations become paramount when implementing AI systems that process sensitive operational data. AI systems create new attack vectors and require specialised security measures.

Develop comprehensive cybersecurity protocols that address both traditional threats and AI-specific vulnerabilities, including data poisoning attacks and adversarial inputs that could compromise AI decision-making.

Consider the integration requirements between AI systems and existing manufacturing execution systems, enterprise resource planning platforms, and other critical business applications.

Seamless integration ensures that AI insights can be acted upon quickly and efficiently, while poor integration can create bottlenecks that negate AI’s benefits.

Measuring Success and Continuous Improvement

Establish clear metrics for AI performance that align with business objectives. These might include reductions in equipment downtime, improvements in product quality, increases in production efficiency, or cost savings from optimised resource allocation.

Avoid the temptation to focus solely on technical metrics like model accuracy—business impact should be the primary measure of success.

Implement continuous monitoring systems that track both AI performance and business outcomes. AI models can degrade over time as conditions change, requiring regular retraining and adjustment.

Create processes for ongoing model maintenance, performance monitoring, and improvement that ensure your AI systems continue to deliver value as your business evolves.

Foster a culture of experimentation and learning that encourages teams to identify new applications for AI technology.

The most successful manufacturers treat AI implementation as an ongoing journey rather than a one-time project, continuously exploring new ways to apply these technologies to improve operations.

The New Zealand Manufacturing Landscape

New Zealand’s manufacturing sector stands at a unique crossroads in its AI journey. The recent Callaghan Innovation survey showed that AI is the second-most desired technology, overtaking data and analytics among those already implementing technology.

The country already possesses some advantages, including one of the world’s highest rates of AI adoption among workers and a reputation as a global test-bed for innovation, with generative AI expected to add $76 billion to New Zealand’s economy by 2038 (Generative AI expected to more than double New Zealand’s productivity: report – New Zealand News Centre).

However, comparatively low digital maturity is eroding potential returns and putting New Zealand’s competitiveness at risk.

The infrastructure foundation is strengthening rapidly. Digital infrastructure is about to receive a huge boost with the opening of Microsoft’s hyperscale cloud region in 2024, and other datacentres promised over the next few years.

This development addresses one of the key barriers to AI adoption that many New Zealand manufacturers have faced—access to the computational power and data storage capabilities required for sophisticated AI applications.

For New Zealand manufacturers, the message is clear: the opportunity is significant, but the window for competitive advantage is narrowing.

Local manufacturers who act decisively to build their AI readiness now will be best positioned to capture their share of this transformative economic opportunity.

The Path Forward

Preparing your manufacturing business for AI success requires a holistic approach that addresses data infrastructure, strategic planning, workforce development, and technological readiness.

The companies that invest in building these foundations today will be best positioned to capitalise on AI’s transformative potential tomorrow.

The key is to start with a clear vision of how AI will support your business objectives, then systematically build the capabilities needed to achieve that vision.

This isn’t about implementing the most advanced AI technology available—it’s about creating an organisation that can effectively leverage AI to drive measurable business results.

The manufacturing landscape is evolving rapidly, and AI adoption is no longer optional for companies seeking to remain competitive.

By taking a strategic, comprehensive approach to AI readiness, manufacturing leaders can ensure their organisations are prepared to thrive in an AI-driven future.