AI drives efficiencies through greater insight
There is growing interest from end-user organizations in the use of machine learning, AI and other emerging technologies to increase actionable information.
Artificial intelligence insights
- Artificial intelligence (AI) insights can change how manufacturers operate, but they need to overcome cultural and organizational barriers to achieve these goals.
- Data-related challenges such as data collection and quality, regulations and data governance also need to be addressed.
- Benefits include greater transparency and reduced worker efforts.
The evolution of artificial intelligence (AI) in the past 25 years should make us all very curious about the future. Since the onset of the global COVID-19 pandemic in 2020 there has been an increasing interest in digitalization, from organizations which may not have planned or foreseen this move within their budgets. The technology itself has also made big progress, according to Monica Hildinger, digitalization manager at Siemens.
“So yes, there has been, and we are still seeing, increased interest in AI and other high-end technologies across all industry sectors,” said Hildinger. “Most companies are looking for digital technologies to drive efficiency and productivity into operations; to enhance maintenance strategies; and optimize utilities to help with the push towards greater sustainability.”
But first, Hildinger argues that there is first a need to overcome cultural and organizational barriers to achieve these goals, including resistance to change, values and mindset. “The change must start from the inside,” she said. “Progress needs to be swift instead of waiting for an economic upturn. The skills gained will also give organizations a competitive advantage.”
Additionally, other data-related challenges – such as data collection and quality, infrastructure, governmental regulations and data governance – need to be addressed.
No one said this was an easy path. Indeed, an Accenture study across 12 industrialized countries found that 84% of business executives believe they need to use AI in order to achieve their growth objectives. However, 76% of them admit that they are struggling with scaling up AI adoption. Until now, there hasn’t been a blueprint for getting past proof-of-concept into production and scale and so the transition turns into a struggle for most industry sectors.
The demographic challenges are also currently more present than ever when it comes to engineering skillsets, according to Hildinger. “How can the know-how and experience, attitude to work, discipline and quality, reliability and loyalty be transferred to the next generation of engineers? In an ideal world, well-implemented digital solutions bring great benefits to enterprises and the latest generation of engineers are digital natives.”
Data transparency providing one source of truth in one location, that is visible to a variety of stakeholders.
Enabling the implementation of state-of-the-art solutions through the provision of the necessary infrastructure.
Reduced time and effort across various levels of an enterprise.
New areas of improvement though increased capabilities in pattern recognition and sophisticated mathematical computation – in areas which simply could not have been tackled before.
Creating new segments in the job market for new generations.
“AI for process data analysis has the ability to provide plant operators with much-needed insights for decision support and to inform predictive plant maintenance strategies,” concluded Hildinger. “For the productive use of artificial intelligence and to finally achieve the goal of digital transformation, the first step must surely be planning. Decision makers need to look at the big picture and seek to drive rapid initiatives, based on value.”