Time for manufacturers to realise AI is not plug and play! How to turn AI-ready data into a mainstream tool for business success

Why are over 40% of UK manufacturers still struggling with the complexity of integrating AI into their systems despite its power to boost efficiency, decrease costs and ensure quality? Worse still, by the end of 2025, it is predicted that at least 30% of AI projects will be abandoned due to poor data quality, inadequate risk controls, escalating costs or unclear business value. It’s time for manufacturers to realise that AI is not plug and play. Instead it’s a mainstream manufacturing solution that requires methodical planning for success.  

For manufacturers to avoid becoming a part of the project abandonment statistic, there are four key steps they can take to strategically and seamlessly implement AI into their operations, from aligning AI with business goals and breaking down the AI journey, to remembering the value of employees and achieving the AI dream with teamwork. 

The transformative power of AI can only be achieved if manufacturers change their attitude toward AI. It’s a mainstream solution that needs a robust data foundation that ensures data integrity, keeps data safe and secure and is in a unified data model. There are many use cases where AI tools are making headwind in manufacturing. They go beyond the AI hype to demonstrate real-world examples that AI is not a plug and play option, but a tool manufacturers can utilise to their advantage when integrated correctly. 

• Say goodbye to unexpected downtime: Machine downtime is costing UK manufacturers £180bn annually. Through utilising data algorithms, predictive maintenance analyses sensor data to anticipate equipment failure, helping manufacturers address issues before they occur, which helps to reduce downtime and costs. 

• Ensure tiptop quality: When integrated with production line data, and with clean and labelled images, AI vision systems in real time can identify product defects quicker than humans. 

• Always on-time: AI systems can help manage inventories and supply chain logistics to help manufacturers avoid disruptions, reduce costs and improve delivery time. 

• Enhance energy consumption: As the UK moves closer to its NetZero 2050 goal, manufacturers can look to reduce energy consumption with AI by monitoring smart meter and sensor data. AI can help manufacturers identify peak energy usage times and help adjust operations accordingly. 

But success is not as simple as it sounds. AI implementation isn’t quick or effortless. It takes thoughtful planning to be successful. There are four key elements to the planning process that will help ensure success. 

1. Have a plan and get measuring! 

One of the most common pitfalls when organisations implement AI is that they focus only on the technology and not on aligning AI with business goals. AI projects must be treated as ongoing initiatives and ones that contribute to manufacturers achieving their overall business goals and objectives. From the outset, manufacturers need to set clear goals they want AI to achieve. This will allow them to track and monitor its performance and give them the ability to make adjustments according to feedback and results. 

For example, manufacturers can measure AI’s return on investment (ROI) by tracking KPIs such as downtime, quality, output and costs. These results cannot only be communicated to leadership and factory workers to get buy-in, but they can also be used to highlight areas that are and are not performing well to help optimise the use of AI. 

2. It’s a journey – break integration down into manageable, impactful chunks to yield the highest value 

Manufacturers need to take baby steps with any AI implementation strategy. They can’t just go gung-ho and implement it into all their processes. Manufacturers need to first identify the right digital tools that will have the most impact and help them achieve their goals and objectives for AI. These could be monotonous processes that can be automated, areas where variability affects quality and productivity, and challenges in predicting outcomes or maintenance needs. 

Once the right AI tools have been selected, manufacturers should run a pilot test on small-scale projects first to avoid costly mistakes. Take AI-based quality control, for example, manufacturers could apply this to one of the production lines. From here, manufacturers can treat this as a test case to learn what works when implementing AI and can then expand the data platform to other production lines or areas of operation. 

3. People are just as important as the tools in an AI implementation journey 

One of the biggest hurdles manufacturers are likely to face when implementing AI is employee resistance. This was highlighted in a Gartner survey, which found that employees who feared AI would replace their jobs are 27% less likely to stay with their employer. Strong leadership is crucial at this stage, as manufacturing companies need to have a change management plan in place to deal with employee resistance. A change management plan will allow leadership teams to communicate and demonstrate to employees the changes they will experience in their workflow from AI, the benefits of new AI tools and enable them to iron out any concerns employees might have before they begin implementing AI. 

Getting employee buy-in is key to a successful AI implementation because they are the ones working alongside and with the new digital tools. Engaging with employees throughout the implementation process should also be a priority for manufacturers, as collecting input can help to refine their approach. 

4. Teamwork makes the AI dream work 

A successful AI integration requires a skilled team of people, but a recent report found that talent and skills are two of the main constraints of AI scaling in the manufacturing sector. Here, manufacturers need to assemble a team of skilled workers to ensure a smooth AI integration. This can be achieved through investing in training current employees with the skills required to work with new AI tools and by encouraging cross-functional teams to collaborate and share insights. 

A successful AI implementation will require four key skill sets: data scientists are key to building and refining AI models, data engineers to keep data streams and systems secure, domain experts to allow manufacturers to gain insights into processes and AI project managers to oversee technical and operational efforts. 

 The AI tools are ready and waiting, manufacturers now need to utilise them 

AI implementation is not a straightforward process, it takes time and methodical planning to achieve measurable outcomes. The manufacturers that align their AI goals with business outcomes, break implementation down into manageable chunks, prioritise their employees and assemble a team with a blend of required skills, will be the ones that witness the AI benefits and stay ahead of the competition.