Three key points for successful implementation of industrial AI strategy

2022-04-26 0 By

Today’s industrial manufacturing industry increasingly relies on industrial ARTIFICIAL intelligence, which is seen by operational decision makers as an important driver of their digital transformation strategies.However, making the most of industrial AI depends on successfully scaling up industrial AI strategies across business organizations.The following are key steps to help companies achieve these goals.Building an enterprise data management strategy For a long time, enterprises have been stuck in the mindset of large-scale data collection.The notion that “more data is better” has always been the default approach to data management strategies, but this is wrong.Because of this way of thinking, the industry sector has been accumulating vast amounts of unused, unoptimized, unstructured and useless data for years.Creating a strategy that can derive maximum value from industrial AI means a fundamental shift in an organization’s data strategy — from mass data collection to strategic data management.This means establishing an enterprise-wide strategy that focuses on managing, integrating, and processing disparate, unstructured sets of data, and then making that data actionable across the organization, ultimately allowing all teams to extract and leverage this vast pool of data for industrial AI applications.Reducing friction between functions, data, and technology Building an enterprise-level data strategy also includes reducing or even eliminating friction caused by team disagreements due to the existence of data silos.Layers of friction are added when teams separately store and use their data, domain expertise, and storage technology, exacerbated by decades of massive data collection.Unfortunately, this leaves industrial data Mired in silos and data swamps.Data sets may be relevant to multiple teams, but exist in a single team’s database, providing little visibility to the rest of the organization.This also forces other teams to either tediously seek out relevant information from different corners of the business, or to redundantly collect the same data for their own data silos.A data lake, originally a transient hub for enterprise data, has become a permanent data swamp where information exists in an unstructured format that makes it difficult to conduct relevant search queries.At the same time, because the data is stored in multiple formats and security restrictions, no one in the organization can access the data stored in different businesses.One of the best ways to reduce or eliminate this friction is to deploy the next generation of data scientists.Helps improve data access and processing power by placing all industrial data in a common, standardized, and secure formatting phase.Instead of individual teams and technical departments deciding the format and structure of data, all data is stored in the same format throughout the organization so that all users have equal access — and equal ability — to use it to create new value.This common format is a core component of the industrial AI strategy, effectively eliminating data silos and ensuring that industrial data access is not dependent on a single technology or expertise.Companies across a wide range of industries are feeling the pinch, but Labour shortages are hitting industrial sectors particularly hard.Even before the global outbreak, the industrial sector was in the midst of a generational shift, with older workers retiring after decades at the same plants and not being replaced by younger workers with the same level of operations or expertise.Industrial organizations can stem this brain drain by providing their employees with industrial AI infrastructure.This has two unique benefits for employee retention and training: it ensures that employees are provided with the tools they need to succeed in their jobs.Even if they don’t have years of experience, industrial AI can fill that gap with historical data access and insight, enabling young workers to perform their roles just as well as their predecessors.Make industrial AI the core of the experience and, in turn, a recruiting tool.When employees can see the tools that will help them succeed, it is more attractive than companies that throw new employees into the old technology pit.To survive and thrive in today’s marketplace, industrial companies need to put industrial AI at the heart of their operations and workflow, driving their digital transformation.Creating a unique strategy to execute industrial AI applications is the only way to maximize the value of industrial AI.