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How can manufacturers ensure smooth integration?
Manufacturers are increasingly facing pressure from customers to produce highly complex, high-quality products quickly. AI may seem like the answer, but when nearly 80% of AI projects fail to meet their objectives or are not completed, is it really the solution to manufacturing problems? For Nicholas Lea-Trengrouse, Head of Business Intelligence at Columbus, success depends on the operational approach taken. To be successful, AI projects must deliver value at every level. So how can manufacturers achieve this outcome?
AI experimentation is a thing of the past. AI and machine learning are on the rise, with over 85% of UK manufacturers already invested and looking to integrate AI tools into their operations full-time. UK manufacturers are primarily using these technologies in four key areas: quality control (38%), cybersecurity (37%), logistics (34%) and customer service (32%).
The AI hype is there, but on the factory floor, the success of working adoption frameworks and journeys has been limited. This makes new adopters skeptical, but with the right product development plan, manufacturers can smoothly implement AI at all levels of their operations. Here’s how.
Head of Business Intelligence at Columbus.
1. Avoid getting stuck in the first phase
A recent Gartner AI study found that only 54% of projects move from pilot to production. What’s behind this? Manufacturers often identify use cases for AI and run proof of concept or pilot projects, but these efforts often stall – a phenomenon known as pilot purgatory.
Gartner’s AI Hype Cycle shows that we are currently at the height of inflated expectations. AI projects fail because they often overlook the expected value and real implications of implementing the technology within the organization. So how can manufacturers avoid falling into the AI vacuum?
What is the secret of AI success?
Successful AI adoption lies in a product development roadmap that helps organizations scale effectively. The key is to aim for early wins by identifying business areas that are already primed for AI success and where significant impact is possible.
Consider this scenario: A manufacturer wants to increase profit margins by 10% next year. To achieve this goal, you need to meet three objectives: reduce machine downtime, minimize waste, and address supplier irregularities.
From this assessment, the manufacturer can identify opportunities to use AI analytics to predict machine failures, detect product quality issues, and optimize supplier routes. The focus is on understanding which processes can be transformed to successfully adopt AI and create new value, benefiting both the bottom line and the workforce.
2. Finding the right balance between data and insight is crucial to avoid analysis paralysis
One of the biggest challenges for manufacturers in AI adoption is data management. Organizations often collect data from multiple disparate sources, including Excel spreadsheets, manual data entry, on-premise servers, and cloud-based systems. Manufacturers attempting to integrate all of this data to gain a comprehensive view of the business and train AI models are left with the significant hurdle of analysis paralysis.
To address this challenge, manufacturers need a robust data strategy that ensures seamless data integration and accessibility. To do so, manufacturers must standardize data formats, implement centralized data storage solutions, and leverage advanced data processing techniques. A unified data ecosystem enables organizations to improve data quality, streamline workflows, and enhance the accuracy of AI models.
End data clutter with robust data management
When it comes to AI use case scenarios, manufacturers have limited reference samples, so they must assess the data available and determine what additional data is needed to train the AI tools. Organizations are inundated with data, but quality is just as important as quantity. To ensure data quality, manufacturers must implement robust processes and policies to properly manage data to ensure its usability, consistency, and integrity.
This is where the outputs of machine learning models and associated decision data in end-to-end solutions can make a significant difference. These outputs can be integrated into dashboards that are tailored to everyday business use cases, fitting seamlessly into user workflows to provide actionable insights. Manufacturers can then use these insights to optimize operations, improve decision-making, and drive better business outcomes.
3. Employee resistance is expected – AI is here to enhance, not replace!
One of the first hurdles manufacturers face when adopting AI is employee resistance. When companies make changes, especially technological ones, the first concern for many employees is, “Will this take my job?” However, manufacturers don’t want to use AI to replace roles, but to make employees more efficient, reduce repetitive tasks, and improve overall productivity. So what can be done to increase adoption?
Strong leadership and communication can ensure smooth adoption during the pilot phase and beyond
It’s time for leadership to step up and demonstrate how AI implementation will benefit everyone. The focus should be on how employees can use the technology to enhance jobs, improve working conditions, and create new opportunities for career growth rather than being replaced by it. Leadership teams should align AI initiatives with business objectives, working backward from desired outcomes to identify applications that drive those goals. This product-based approach helps organizations understand what their people need from AI and how it will integrate into larger operational frameworks.
At this stage, it is vital to involve employees in the AI implementation process to drive adoption. Manufacturing companies can solicit input from employees and demonstrate how AI can address their concerns and make their jobs easier and more efficient to create a sense of ownership. Manufacturers can encourage cross-functional teams to collaborate and share insights to ensure a smoother AI integration process and alignment with organizational goals.
To ensure success beyond the initial pilot phase, leadership teams need to evaluate AI products using more than just standard performance metrics. User adoption rates can provide valuable insights. By examining the percentage of the target audience actively using the product, the repeat usage rate, and how well AI helps employees solve initial problems, manufacturers can assess whether both employees and the business are realizing long-term value.
4. A growing data skills shortage is holding back AI adoption – what’s the answer?
A third (33%) of UK manufacturers report that a lack of skills is the main barrier to implementing smart manufacturing technologies such as AI. This highlights a significant gap between the demand for these technologies and the ability to implement them effectively. So, how can manufacturers address this skills gap?
The manufacturer already has the skills, he just needs to learn them!
One approach is to invest in training programs to upskill existing employees. IBM research found that reskilling and workforce development (39%) are among the top AI investments for organizations exploring or implementing AI. Many manufacturing companies often don’t realize that they already have employees with the technical or transferable skills needed for AI roles. For example, a logistics coordinator could transition into a data engineer or data analyst role because they already understand the data, processes, ERP systems, and CRMs.
What is often missing is some targeted learning and support opportunities in areas such as data engineering, data lakehouses, data warehousing or coding. With this extra training, manufacturers can transform their employees into data engineers and effectively use AI to improve their work.
Additionally, manufacturers can seek out new talent with expertise in AI and smart manufacturing. A team composed of a mix of experienced workers and new hires with fresh, specialized knowledge can create a dynamic workforce capable of driving technological innovation.
Organizations that can leverage the existing skills of their workforce and provide new employees with the necessary training can more easily integrate AI technologies, improve operational efficiency and ultimately increase their competitiveness.
AI roadmaps are the key to success
Manufacturers that approach an AI project with a two-pronged approach will see continued success. First, they need to break their adoption into manageable phases. Second, they also need an AI roadmap that shows them where AI can add value, streamline operations, and improve production. Together, these two steps will help manufacturers achieve success from their implementation and avoid becoming another statistic of failed AI projects.
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