PC-SEP24-PG18-19.1_Layout 1 16/09/2024 11:52 Page 18
INDUSTRY 4.0/IIOT
FOLLOW A ROADMAP TO AI INTEGRATION
For Nicholas Lea- Trengrouse, Head of Business Intelligence at Columbus, lasting success with AI projects requires an approach that delivers value at every level of a manufacturer’s operations
anufacturers face growing pressure to quickly produce increasingly complex and higher-quality products to meet customer demand. While AI appears to be a logical solution, the reality is that up to 80% of AI projects either never reach completion or don’t meet their objectives, resulting in failure or cancellation. Gone are the days of experimenting with AI. Manufacturing companies are now seeking to integrate AI tools permanently into operations. A recent survey revealed that 88% of UK manufacturers have already invested in AI and machine learning, compared to 84% of their European counterparts, or plan to do so within the next 12 months.
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UK manufacturers are primarily using these technologies in four key areas: quality control (38%), cybersecurity (37%), logistics (34%), and customer service (32%).
The hype is everywhere, but what is the reality on the factory floor? A product development roadmap can help manufacturers seamlessly integrate AI at every level of their operations. 1. Don’t get stuck in pilot purgatory A recent Gartner AI survey revealed that only 54% of projects progress from the pilot phase to production. So, what’s behind this? Manufacturers often identify use cases for AI and conduct proof of concept or pilot projects, but these efforts frequently stall – a phenomenon known as pilot purgatory. Gartner’s hype cycle for AI shows we are
18 SEPTEMBER 2024 | PROCESS & CONTROL
currently at the peak of inflated expectations. AI projects fail because they often overlook the anticipated value and the true implications of implementing the technology within the organisation. So, how can manufacturers avoid falling into the AI void?
Product development roadmaps help organisations scale effectively. The key is to aim for early wins by identifying business areas already primed for AI success and where significant impact is possible.
Consider this scenario: a manufacturer aims to increase its profit margins by 10% next year. Achieving this goal depends on meeting three objectives: reducing machine downtime, minimising wastage, and addressing supplier irregularities. From this assessment, the manufacturer can pinpoint opportunities to use AI analytics to predict machine failures, detect product quality issues, and optimise supplier routes. The focus is on understanding which processes can be transformed to successfully adopt AI and create new value. 2. Avoiding analysis paralysis – too much data, not enough insights
One of the biggest challenges in AI adoption for manufacturers is data management. Organisations often collect data from multiple disparate sources. Manufacturers that attempt to integrate all this data to gain a comprehensive view of the business and train AI models are left with analysis paralysis. To overcome this challenge, manufacturers need a robust data strategy that ensures
seamless data integration and accessibility. This requires manufacturers to standardise data formats, implement centralised data storage solutions, and employ advanced data processing techniques. A unified data ecosystem allows organisations to improve data quality, streamline workflows, and enhance the accuracy of AI models. When it comes to AI use case scenarios, manufacturers have limited reference examples, so they must assess the data they have available and determine what additional data might be needed to train the AI tools. To ensure data quality, manufacturers need to implement robust processes and policies for managing data correctly, ensuring its usability, consistency, and integrity.
This is where the outputs of machine learning models and associated decision- making data in end-to-end solutions can make a significant difference. These outputs can be integrated into dashboards tailored for everyday business use, seamlessly fitting into user workflows to provide actionable insights. Manufacturers can then use these insights to optimise operations, improve decision- making, and drive better business outcomes. 3. Overcoming employee resistance. No one likes change, particularly from a ‘machine’ One of the initial hurdles manufacturers face with AI adoption is employee resistance. When businesses introduce technological changes, the immediate concern for many is, “Will this take my job?” However, companies
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