By Alice Chambers |
The majority of process manufacturers are either already using or planning to adopt generative AI, according to a new industry report published by Microsoft with research by IoT Analytics. The study reveals that 80 per cent of organisations in the sector are embracing the technology.
The Artificial Intelligence in Process Manufacturing: Preparing for an AI Future report highlights four key AI use cases for process manufacturers that have measurable business impact. These include process optimisation; sustainability, energy efficiency and waste reduction; research and development; and predictive maintenance and analytics.
For example, one global chemical company reported that AI helped reduce the time to market for molecular enhancements from six months to just six to eight weeks. A rubber and plastics manufacturer reported significant improvements to plastic design for more efficient production. And another chemical company achieved a 90 per cent reduction in demand forecasting costs and accelerated knowledge retrieval, enabling employees to access answers in seconds rather than days.
The report also highlights two areas that require thoughtful planning for AI adoption: security and system complexity. Nearly half of the respondents say concerns around data protection impact their AI adoption decisions and legacy systems mean that the introduction of AI requires adoption of long-standing workflows.
To overcome these challenges, Microsoft recommends that manufacturers identify business needs, embrace structural flexibility, get their data in order and use AI to develop workforce capabilities.
“AI is gaining real traction in process manufacturing,” said Yury Gomez, global chief commercial and strategy officer of process manufacturing industry at Microsoft, in a blog post titled ‘AI in process manufacturing: From operational gains to strategic advantage’. “Building on investments in internet of things, automation and advanced process controls, manufacturers are focused on how AI can drive enterprise-wide decision-making and long-term value. This shift is no longer about if AI is worth pursuing – it’s about how to start effectively and drive measurable impact.”
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