How to turn AI-Reaid data into a mainstream tool for business success
Why are more than 40% of UK manufacturers still struggling with the complexity of integrating AI into their systems despite their ability to increase efficiency, reduce costs and ensure quality? Worse, by the end of 2025, at least 30% of AI projects are expected to be abandoned due to poor data quality, inadequate risk management, escalating costs, or unclear business value.
It's time for manufacturers to realize that AI is not plug and play, but a mainstream manufacturing solution, and systematic planning is the key to success. To avoid being part of the project abandonment statistics, there are four key steps you can take to implement AI strategically and seamlessly, from aligning AI with business goals, breaking AI journeys, remembering employee values, and achieving AI dreams with teamwork.
The transformational power of AI can only be achieved when manufacturers change their attitude towards AI. This is a mainstream solution that ensures data integrity, keeps data safe and secure, and requires a robust data foundation in a unified data model. There are many use cases where AI tools are becoming headwinds in the manufacturing industry.
Beyond AI hype, AI is not a plug-and-play option, but it provides a real-world example where tool makers can use it to their advantage when properly integrated. Only then can manufacturers begin to integrate AI-enabled data into digital technology throughout their operations.
- Say goodbye to unexpected downtime: Machine downtime costs British manufacturers £180 billion a year. By utilizing data algorithms, predictive maintenance can help analyse sensor data to predict equipment failures and address issues before they occur. This helps reduce downtime and costs.
- Ensure Tiptop quality: When integrated with production line data and clean labeled images, AI Vision Systems can identify product defects in real time faster than humans.
- Always on time: AI systems help manage inventory and supply chain logistics, helping manufacturers avoid confusion, reduce costs and improve delivery times.
- Enhanced Energy Consumption: As the UK approaches its Netzero 2050 target, manufacturers can aim to reduce energy consumption with AI by monitoring smart meters and sensor data. AI helps manufacturers identify peak energy usage times and adjust operations accordingly.
But success isn't as simple as it looks. Implementing AI is neither quick nor easy. Success requires thoughtful planning. There are four key elements in the planning process that will help you ensure success.
1. Make a plan and measure!
One of the most common pitfalls when an organization implements AI is that it focuses solely on technology, rather than aligning AI to business goals. AI projects must be treated as an ongoing initiative. This contributes to manufacturers who achieve their overall business goals and goals. From the start, manufacturers need to set clear goals they want to achieve for AI. This allows you to track and monitor performance and adjust it according to feedback and results.
For example, manufacturers can measure AI's return on investment (ROI) by tracking KPIs such as downtime, quality, power, and cost. These results cannot be communicated only to leadership.
Factory workers get buy-in, but can also be used to highlight areas that help optimize AI use.
2. It's a journey – manageable integrations and break them down into shocking chunks to bring you the best value
Manufacturers need to take baby steps in their AI implementation strategies. They can't just go to Gangho and implement it in all the processes. Manufacturers need to identify the right digital tools that will impact the most first and help them achieve their AI goals and objectives. These are challenges in predicting monotonous processes that can be automated, areas where variability affects quality and productivity, and outcomes and maintenance needs.
Once the right AI tools are selected, manufacturers should first perform pilot tests on small projects to avoid costly mistakes. For example, if you consider AI-based quality control, manufacturers can apply this to one of their production lines. From here, manufacturers can treat this as a test case, learn what works when implementing AI, and expand their data platform to other production lines or operational areas.
3. People are just as important as the AI implementation journey tools
One of the biggest hurdle manufacturers can be faced when AI implements employee resistance. This was highlighted in a Gartner study, finding that employees who feared that AI would replace their jobs were 27% less likely to remain with their employers. Strong leadership is extremely important at this stage as manufacturers need to set up change management plans to address employee resistance. Change Management Plans allow leadership teams to communicate with employees the changes they experience in workflows from AI, the benefits of new AI tools, and to help employees resolve any concerns they have before they begin implementing AI.
Getting employee buy-in is key to successful AI implementation. Because they work with new digital tools. Engaging employees must also be a priority throughout the implementation process, as collecting inputs can help improve your approach.
4. Teamwork is the dream job of AI
Successful AI integration requires a team of skilled people, but a recent report found that talent and skill are two of the main constraints of AI scaling in the manufacturing sector. Here, manufacturers need to bring together teams of skilled workers to ensure smooth AI integration. This can be achieved by training current employees with the skills needed to use new AI tools and encouraging trans-worker teams to work together to share insights.
A successful implementation of AI requires four important skillsets. Data scientists will enable data engineers, domain experts and manufacturers to gain insight into the process to build and refine AI models, to keep data streams and systems safe, allowing AI project managers to oversee technical and operational efforts.
The AI tools are ready and waiting, so manufacturers need to take advantage of them
Implementing AI is not a simple process, and it requires time and systematic planning to achieve measurable results. Manufacturers who align AI goals with business outcomes, break down implementations into manageable chunks, prioritize employees, and assemble teams by combining the skills they need, witness the benefits of AI and are ahead of the competition.
About the author
Nicholas Lea-Trengrouse is Head of Business Intelligence for Columbus UK. Columbus is a consulting company that helps organizations promote business value by defining, executing and evolving the entire business. We deliver digital value through human intelligence, enabling our customers to innovate and grow. From strategy and technology to organizations and leadership, we discuss the most talked about topics that shape today and tomorrow's organization.
