AI across industries and small/medium manufacturers

Smart machines are reinventing how work is done across industries. Companies across sectors and regions are seeing an initial boost in process speed and performance by implementing Artificial Intelligence (AI) technologies. However, there is significant untapped potential in reimagining business processes from the ground up as self-improving procedures that can sense, comprehend, act and learn—all in real time.
Until now, many business leaders have taken too narrow a view of AI. To maximize the potential of AI and be digital leaders, healthcare organizations must reimagine and reinvent their processes from scratch—and create self-adapting, self-optimizing “living processes” that use machine learning algorithms and real-time data to continuously improve. Machines themselves will become agents of process change, unlocking new roles and new ways for humans and machines to work together.
Machine Learning in Manufacturing is making significant investments in machine learning-powered approaches improving all aspects of manufacturing. The technology is being used to bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed. So-called “smart manufacturing” (roughly, industrial IoT and AI) is projected to grow noticeably in the next 3 to 5 years.
From what our research suggests, most of the major companies making the machine learning tools for manufacturing are also using the same tools in their own manufacturing. This makes them the developer, the test case and the first customers for many of these advances. This is a trend that we’ve seen in other industrial business intelligence developments as well. 
This same in-house AI development strategy may not be possible for smaller manufacturers.  However, small and medium manufacturers can learn Process reimagining on three overlapping areas: process, data and the workforce.

    Process change: Reimagining processes from scratch
  • Applying AI to process change management
  • Rethinking standardized processes as continuously adaptive
  • Bringing AI-based change to multiple processes across the enterprise
Data and data models: Capturing the exponential power of data
  • Using data to train and sustain process change
  • Making processes self-adapting and self-optimizing
  • Discovering new patterns of opportunity
Workforce: Unlocking the full potential for human/machine interaction by inventing new jobs
  • Enlisting the C-Suite to remake the culture with AI
  • Helping employees keep pace
  • Emphasizing distinctively human capabilities when hiring
  • Making sure that algorithmic decisions are ethical, fair, safe and auditable
 
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