The art of artificial intelligence: How to drive successful AI projects in manufacturing

It has been an exciting few months with the release of a series of ground-breaking artificial intelligence (AI) language models. These advancements in the technology have driven up its popularity this year, with many on the internet questioning if it means an android apocalypse is on the horizon.

However, in reality, AI’s true potential lies in its ability to serve as a tool to streamline processes – no matter the size of the project. Whether that might be helping you craft a killer social media caption or curating an essential legal letter.

Proof of AI’s increasing integration was highlighted in McKinsey’s 2022 State of AI report. It showed that AI adoption has more than doubled since 2017, with 50% of organisations saying they now use AI in at least one business area.

And this adoption has been helping businesses in industries such as retail and energy to innovatively serve the needs of their organisations and their customers. But manufacturing is where it’s having one of the biggest impacts.

AI-based products such as Machine Learning (ML) and Deep Learning (DL) are facilitating smart factories that can optimise increasingly complex, multi-stage processes. These tools are enabling them to become more sustainable, efficient and cost-effective.

But the big questions for businesses just starting to integrate AI into their manufacturing processes are: how do I put these sophisticated tools to use in ways that optimises my operations – and where do I start?

Step one: Make data security your foundation

In recent years, businesses have neglected an essential pillar of high-performing AI solutions in manufacturing: secure data. Given the long-standing dependence on out-dated legacy systems, data storage has been much less of a priority.

But, mindsets have started to shift as manufacturing is becoming increasingly digitised. Businesses now understand the importance of not just collecting and inputting data, but safely storing it too – especially when it comes to protecting valuable intellectual property.

By prioritising this belief that effective manufacturing applications begin with data, we can more adequately plan for the successful delivery of AI-powered projects.

Enterprises need to ensure that they lay down the right groundwork for how to do this. They must keep the right balance between accessibility and safety to build up resilient solutions that shield partners and customers from growing cyber threats. With this approach, businesses can focus their energy on conducting experiments, while working to optimise and train algorithms.

Step two: Use data and AI as enablers

When it comes down to it, how you choose to approach your data management can make or break your efforts to enhance your projects. Currently, data scientists lose around 80% of their working hours on collecting, clearing, and detecting defective data – instead of creating actionable insights.

Clean data is essential for training AI algorithms so it can make more accurate predictions around priorities such as impending plant breakdowns or machine downtime. Better data hygiene helps businesses seamlessly integrate information into existing software programs. Then they can deploy AI to automate the process – driving better efficiency and productivity.

But this success rests heavily on the quality and quantity of the data it processes. So, the better the data, the more efficiently it will function.

Think of it like the process of knitting a jumper. Your wool is your stored data, your knitting needles are the system that processes the data and your jumper is your finished project (or insight). Each bit of data that you input is one stitch in the whole process, so if you slip up, or miss a stitch, it could result in holes or lumps that make it inconsistent.

So, the process of creating the project is as much about preparing what you need as it is inputting each loop. In the same way that mistakes in your stitching lead to a less practical jumper, allowing inaccuracies in your data could affect the quality of your analysis – ultimately impacting your business’ performance.

Step three: rationalise AI integration into your business operations

Implementing AI into manufacturing should strategically add value to the day-to-day functioning of your business. Leaders should consider what’s needed for AI integration to be successful, considering costs, challenges and limitations. But with the right approach, it can result in quicker, cheaper, and more sophisticated processes.

Across manufacturing, AI integration might look like introducing intelligent machine maintenance, improving the efficiency of quality control, becoming more agile with supply chain management or increasing AI-powered automation for running better processes

Ensuring success in these areas is also about the people using the technology – how they use it, the way they embed it into the process, and whether they use it to its full potential. So, leaders need to focus on investing in the right people and tools in edge and cloud computing to help them embrace this innovation.

In today’s market, AI has the potential to provide a huge competitive advantage for companies in manufacturing. But successfully achieving these benefits will come from their ability to truly transform their approach to data and digital strategy. If they can do this, it will prove AI’s potential to completely transform supply chains of the future.