4 reasons metals leaders should invest in AI education
Before taking a next step or making technology investment decisions, metals leaders should fully inform themselves about the uses and challenges of AI in manufacturing.
Individual tools can be short-lived, but education builds flexibility
Some AI features, although still new, are advanced enough to unlock immediate value. However, metals leaders might remain apprehensive because of the unknowns and possibilities of headline-making risks associated with full adoption.
Many software companies are offering AI features they might have rushed to get into the market to keep up with the competition. Often, these features are underdeveloped and present user adoption challenges once they’re deployed.
Metals leaders can empower their employees to ask vendors critical questions by providing workshops that educate stakeholders on the basic concepts of machine learning and AI. Educated employees participating in selection discussions can then help mitigate the risk of an investment failing to achieve desired outcomes.
AI education is a process
Gaining a proper understanding of AI solutions involves deliberate, multiphased education efforts. When employees know how to apply AI tools, they can use them more effectively.
The first step is to provide a training session about the fundamentals of AI and how its capabilities apply to a specific application. Tool-specific purchasing discussions, internal vision-setting, and more extensive training can be more efficient if employees already understand universal considerations when working with AI tools within a given framework. In addition, metals leaders can provide input on expectations regarding changes to employee roles and day-to-day workflows based on AI use.
The training and adoption process for any software takes time. Metals leaders should expect the same when educating employees on their first AI-powered applications. Given the need for a community of advanced users in the future, executives would be wise to start building employee knowledge now.
AI education can uncover new use cases and inform road maps
Employees are often deeply familiar with company data, and education efforts can be a powerful investment by providing an outlet to think through areas in which value can be added and to build a road map for desired use cases.
Without an educated network of users identifying opportunities for AI use, metals companies risk investing in tools that solve the wrong problems or that business data is not ready to support. An educated group of users can direct exploratory discussions to relevant use cases and can guide projects that capture or integrate data in new ways to enable future AI use.
Education about the right AI tool to invest in now can save money later
Metals companies with tight margins can’t afford to invest in tools that don’t produce results. Leaders should be equipped with the right information before they lock in recurring licensing costs, and they should consider gradual rollouts to minimize commitments.
The list of different AI solutions with varying focuses and costs is long – and growing. Smaller AI models can sometimes outperform more expensive models at specific tasks and can run on company computers instead of requiring the cloud. Vendors are competing with licensable solutions such as user interfaces and workflow platforms, but there’s still a lot of innovation happening with the models themselves.
As AI adoption increases, it’s reasonable to expect that vendors might provide more competitive pricing by using less expensive infrastructure. For example, it would be a shame to get one year into a multiyear vendor contract only to find that another vendor is offering a less expensive solution for the same problem.
