During 2025, a wave of innovative technologies is expected to disrupt and change the way IT is applied to automation systems. The challenge lies in balancing the reliability of proven techniques with the potential of new technologies, all while minimising and controlling risk.
Yet, for many manufacturers − most of whom are risk-averse and focused on other business imperatives − anticipating and adopting these technology advancements is not easy. This article explores ways to approach the latest information technologies effectively in a manufacturing context. The ultimate question for leaders is how to embrace technology innovation without compromising operational stability.
Key automation trends for 2025
Some automation trends that will have an impact in 2025 are:
AI and machine learning: Beyond predictive maintenance, the rise of generative AI agents is enabling real-time design optimisation and anomaly detection. For instance, companies are using AI to generate optimal equipment configurations based on current environmental conditions and material properties, increasing responsiveness to disruption and reducing design cycles.
Cloud computing: The shift towards edge-integrated cloud solutions allows for decentralised data processing and reduced latency. Instead of solely relying on centralised cloud servers, manufacturers are using edge devices to perform immediate data analysis, enabling faster responses in critical processes. Gartner predicts increased adoption of these hybrid models. As manufacturers plan their cloud strategy, it’s prudent to also consider potential disruptions to cross-border data flows due to evolving international trade policies and geopolitical considerations.
Cyber security: The focus is shifting towards AI-driven threat detection and response. Instead of solely relying on traditional security measures, manufacturers are implementing AI-powered systems that learn and adapt to new cyber threats in real-time, significantly enhancing security postures.
Sustainability: The emphasis is on closed-loop manufacturing systems powered by renewable energy sources. Manufacturers are using automation to optimise material usage, recycle waste and reduce energy consumption in a fully integrated manner.
Digital twins: The development of self-aware digital twins capable of autonomous optimisation and predictive adaptation is gaining traction. These advanced digital twins not only simulate physical systems, but also proactively adjust parameters based on real-time data and predicted future conditions.
Smart manufacturing: The convergence of 5G connectivity, AI and advanced technologies is enabling more autonomous and self-reconfiguring production lines. While fully ‘lights-out’ factories remain a long-term goal, these early advancements allow for significant automation and optimisation of production processes.
Implementation strategies
To ensure these technologies deliver real value and a strong return on investment, manufacturers require well-defined strategies that thoughtfully integrate innovation with proven methods. The following approaches can help:
Start small, scale gradually
Begin with pilot projects to test new technologies in low-risk settings. For example, a chemical plant could implement AI-powered predictive maintenance on a single, non-critical compressor before expanding it to the entire facility. Or, in oil and gas, a company might deploy drone-based inspection systems on a limited section of a pipeline to assess their effectiveness before full-scale deployment. This approach allows for validation and adjustments, reducing the financial and operational risks of full-scale deployment.
Leverage existing infrastructure
Integrate new tools with current systems to maximise existing investments and ensure a consolidated operational overview. For instance, in process manufacturing, adding IoT sensors to legacy equipment for real-time data collection can be significantly enhanced by integrating the data streams into the existing scada system. This allows operators to monitor new data alongside established process parameters within a familiar and unified interface. In addition, new AI-driven analytics tools can be connected to the scada system to provide advanced insights without requiring a complete system overhaul. This hybrid approach minimises disruption, leverages existing operator expertise, and delivers immediate benefits by providing a unified view of operations.
Focus on training and skill development
A skilled workforce is essential for successfully adopting disruptive technologies. Invest in targeted training programs that go beyond basic operation and maintenance. For example, with the rise of generative AI in manufacturing, train engineers and technicians how to effectively prompt and validate AI-generated designs, simulations and process optimisations for chemical processes or mining operations. This ensures they can leverage these powerful tools while maintaining critical oversight and quality control. Similarly, as process manufacturers implement advanced process control (APC) systems driven by machine learning, invest in training programs that teach employees how to interpret AI-driven recommendations, understand the fine-tuning of control algorithms, and identify potential biases in the AI models. This will help employees adapt to changing workflows and remain productive in increasingly automated environments.
Prioritise cyber security
As connectivity grows, so do vulnerabilities. Implement strong security measures such as encryption, regular audits, and employee training to recognise and avoid online threats in order to protect operations. In heavy industry, where remote monitoring is common, secure communication channels are vital to prevent disruptions.
Collaborate with vendors and partners
Partner with technology providers for tailored solutions and support. In chemical manufacturing, collaborating with established automation vendors can ensure that new systems meet stringent safety and regulatory requirements, smoothing the transition to advanced processes.
Monitor and evaluate
Track performance using KPIs that reflect the impact of new automation technologies such as uptime, yield and energy use. However, it is crucial to also incorporate metrics specific to the disruptive technologies being implemented. For example, if using AI-powered predictive maintenance, track the reduction in unplanned downtime and the accuracy of failure predictions. If implementing a digital twin, measure the time saved in process optimisation and the improvement in simulation accuracy. Continuous evaluation identifies successes and areas for improvement, ensuring the technologies deliver the intended value. A 2023 Rockwell Automation study found that manufacturers that closely monitor their automation projects see a 15% higher ROI compared to those who don’t, highlighting the importance of data-driven decision making.
The power of data analytics
Data analytics underpins many of these trends, offering insights that drive efficiency and innovation. By analysing data from sensors and production lines, manufacturers can predict equipment failures, optimise workflows and reduce waste. With the advent of AI agents and advanced machine learning techniques, manufacturers can now unlock even deeper insights. For example, AI agents can autonomously explore vast datasets to identify hidden correlations and anomalies that human analysts might miss. These agents can also generate real-time recommendations for process adjustments, leading to more agile and responsive operations. In the food and beverage sector, advanced AI can not only track consumer trends but also predict demand fluctuations with greater accuracy, enabling faster production adjustments and minimising waste. A 2024 PwC survey found that 85% of manufacturers plan to increase analytics investments by 2025, highlighting its growing role and the increasing reliance on advanced AI-powered analytics.
Striking the balance
The key to success lies in a balanced approach. Start with proven technologies then layer in newer innovations like AI or digital twins as confidence grows, new skills are developed and value is demonstrated. This phased adoption leverages the stability of established systems while testing the waters of emerging trends. Financially, taking a strategic and phased approach to automation is often more rewarding. Industry research suggests that gradual automation investments yield a better return over time compared to rapid, all-in approaches.
Conclusion
As we move further into 2025, manufacturing automation offers immense potential, but only if implemented thoughtfully. By starting small, leveraging existing assets, prioritising training and security, collaborating with experts and monitoring progress, manufacturers can adopt trends like AI, cloud computing and smart manufacturing without undue risk. Data analytics further amplifies these efforts, turning raw information into actionable strategies. While initial costs and management focus may seem high, the long-term gains − cost savings, productivity boosts and sustainability − make the investment worthwhile. This balanced, practical approach ensures that manufacturers across industries can innovate confidently, staying competitive in a dynamic and changing market.
About Gavin Halse
Gavin Halse, an experienced chemical process engineer, has been an integral part of the manufacturing industry since the 1980s. In 1999, he embarked on a new journey as an entrepreneur, establishing a software business that still caters to a global clientele in the mining, energy, oil and gas, and process manufacturing sectors.
Gavin’s passion lies in harnessing the power of IT to drive performance in industrial settings. As an independent consultant, he offers his expertise to manufacturing and software companies, guiding them in leveraging IT to achieve their business objectives. His specialised expertise has made contributions to various industries around the world, reflecting his commitment to innovation and excellence in the field of manufacturing IT.
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