91% of manufacturers say AI important for future of sector, but data and expertise are lacking
While interest and budgets are increasing, half in survey say they are lacking skills needed to further AI initiatives and only 37% are confident in their underpinning data
A survey of manufacturers in Canada, the US and UK has found that Artificial Intelligence (AI) is making strong inroads into manufacturing, with 70% reporting operational deployments and 82% expecting to increase investment in the next 12-18 months.
This is being driven by an increasing recognition of the applications of AI and its potential, as 91% say that it is an important technology for the future of manufacturing and 79% expect it to increase their productivity and value generation.
An automated admin assistant
Currently, AI is most likely to be used in a supporting role, with 60% saying that they use AI-powered software to automate repetitive tasks and administrative tasks. This is also reflected in the applications reported by respondents, with the most common applications being to support production (31%), employee training (24%) and customer service (23%).
Further down the list are some important tasks that AI would be well suited to assist, such as production planning (19%) and inventory management (17%), suggesting that there are substantial gaps that AI could rapidly expand into, especially as Enterprise Resource Planning (ERP) was rated as the most impactful technology to furthering AI capabilities (47%).
Powering production
Behind automating administrative tasks is AI’s increasing role in the production process. Here, 47% said that they are deploying industrial Internet-of-Things (IoT) systems connected to AI, which is in turn driving notable gains, as 29% reported rising production efficiency, while 44% noted improved efficiency as a result of utilising AI.
Furthermore, when asked where they will be making additional AI deployments, the most likely answer was into production lines (36%), followed by quality control (24%) and process optimisation (26%).
Skills, data key barriers
This potential seems set to be held back by a lack of institutional knowledge, however, as the biggest barrier to further adoption was internal skillsets, while many reported shaky confidence in their underlying data.
Half the 100 respondents reported that their organisation was struggling to adopt AI as a result of gaps in their knowledge base, while 36% said that they would attempt to tackle the issues of skills via investing into highly user-friendly technologies, underpinning that there is a recognition of a lack of workforce capabilities. Meanwhile, 60% said that workforce training and upskilling would be part of their approach.
Furthermore, when asked whether their underpinning data was both accurate and timely, just 37% said they ‘extremely’ or ‘very confident’, while 23% reported little to no confidence, with the remaining 40% merely ‘somewhat’ confident.
It therefore seems that while AI is going to become a cornerstone of future manufacturing capabilities and can boost productivity, organisations are going to have to focus on catching up their labour force and datasets to their ambitions.