Machine learning extends beyond resolution of technical problems, into the business realm transforming the way companies think, operate and act.
The disruption potential of industrial artificial intelligence and machine learning (ML) goes beyond technology to discover and address unique challenges with new oversight perspectives, according to the latest Industry IoT Consortium white paper.
Industrial artificial intelligence is the application of AI, machine learning in particular, to IoT applications in industry, in areas like smart manufacturing, robotics, predictive maintenance and autonomous vehicles.
With the ever-increasing amount of internally and externally sourced data available to organisations, ML is a critical tool for leveraging this data in transformative ways, such as: helps uncovering insights from data intensive environments; acts an enabler of digital transformation and acts an agent for future-proofing the organisation.
ML provides analysis capabilities and enables continuous monitoring to provide a deeper understanding of the data and can improve this understanding over time with minimal human involvement or knowledge of the process. ML can also continually inspect incoming data to gain new insights and then issue recommendations based on those insights.
The general process can be described as: digitise, extract, transform raw data for analysis, sharing, archiving or distribution; analyse the data—typically to identify anomalies; tune parameters to find an optimum point/output; generate acceptable solutions; prescribe the next course of action; use historical data to predict the likelihood of future events and identify an appropriate action given a condition.
ML enables new areas of innovation since it can be applied in many different ways. For example, predictive analytics and process optimisations allow new thinking about design, operation, and management that have not been possible before.
The Industrial IoT AI Framework referenced earlier provides further examples of the transformative nature this technology. ML complements big data, composable architectures and decision intelligence practices. It provides radical new ways of solving existing problems, such as process optimisation, as well as addressing new problems, such as logistics problems caused by COVID-19.
With these new perspectives and new proposed solutions, ML encourages novel ideas and insight about business operations. Gartner lists five key ways that ML can deliver business value : innovation, exploration, prototyping, refinement and firefighting. Of these, the business effect of innovation and exploration are in clear alignment with digital transformation goals, with main objectives that focus on disruption of current business practices and the exploration of new ideas.
As ML hunts for patterns in an organisation’s vast data reservoir and uncovers novel insights, it stimulates new perspectives for achieving business goals, and new methods of addressing business challenges emerge.
By disrupting current thinking, ML can transform business practices and assist with decision-making in ways that may not be possible by human actions alone. These managed services have uses across industry—in design, manufacturing, operations, workplace management and in other applications. ML excels in advanced decision making and process optimisations.
By driving the discovery of new business ideas, assisting with governance practices, enriching customer experiences, accelerating productivity increases and simplifying prioritisation of business objectives, ML has emerged as a key enabler of organisational digital transformation and the associated new business outcomes.
