The development of artificial intelligence (AI) over the past 25 years should make us very curious about the future. Interest in AI and other high-end technologies is increasing across almost all industry sectors, with most companies looking to digital technologies to improve operational efficiency and productivity, enhance maintenance strategies, and optimize utilities to help drive greater sustainability.
But first, achieving all these goals will require overcoming cultural and organisational barriers, including resistance to change, values and mindsets. Change must come from within, and improvements need to be made quickly, not after the economy improves. The skills gained will also give the business a competitive advantage.
In addition, other data-related challenges need to be addressed, such as data collection and quality, infrastructure, government regulations, and data governance.
However, 76 percent of them admitted that they are working to expand the adoption of AI. As of now, there may not be a blueprint to turn proof-of-concept into production and scale, so for most industries, the transition becomes a struggle.
By integrating AI into core business processes, workflows, and customer journeys, it can optimize its daily operations and decision-making tasks.
In engineering skills, too, demographic challenges are now more prevalent than ever. How do you pass on expertise and experience, work attitude, discipline and quality, reliability and loyalty to the next generation of engineers? In an ideal world, well-implemented digital solutions bring huge benefits to businesses, and the latest generation of engineers would be digital natives.
In order to effectively utilize AI and ultimately achieve the goal of digital transformation, the first step should be planning properly. Policymakers need to look at the big picture and look for quick, value-based moves.
