This transcript has been lightly edited for clarity.
What is shifting in tech-enabling operational transformations?
This is clearly the age of AI. With the rapid advances in generative AI, it has become very easy to consume AI, and therefore companies are making significant investments over the last few years in the cloud infrastructure across various gen AI technologies, as well as machine learning investments. And there’s an increasing difference between leaders, shapers, and laggards who are not able to adopt technology in their operations. Therefore, it’s a choice that companies have to either use technology and AI in their operations and truly step up productivity, step up value capture, or get left behind.
We’ve also proven this in several Lighthouses. Lighthouses are full-blown assets, factories, manufacturing locations where AI has gotten embedded into operations and technology has become a way of working. And these assets are clearly high performers. They are examples and role models for the rest of the industry. These stretch to a hundred-plus now. Therefore, it is now a proven model.
What does it take to scale and sustain tech-enabled transformations?
Our surveys have shown that companies typically capture only about one-third of the impact if they’re not purposeful about how they want to go about it. First, there needs to be a vision and a clear value-back road map. Where do they see the value? If it doesn’t impact the bottom line, if it doesn’t positively influence safety, I think companies shouldn’t invest in technology.
Second is this notion of “use equals value.” If your front line, if your operators, if your manufacturing workforce is not going to use the technology, there’s no value. Therefore, invest in making sure that your front line understands technology. You need user-centric approaches, you need design thinking to get them on board.
And third is the notion of 20/30/50. It’s only 20 percent about the technology—the technology that can create value for the business. It already exists, and it’s already scalable. It’s 30 percent about processes and changing the way of working, and 50 percent is about capabilities and mindsets. What do I mean by that? When I look at companies who scaled AI and who have become leaders, they’ve built really good capabilities on translation so that the end users can actually define a problem for the data scientists, and they’re able to translate the user requirement if it’s a complex process—the physics and the chemistry of the process—into data science language, and they're able to also interpret the results of a model and see whether that makes sense or not. They’ve also created digital factories, where you’ve got the digital and the analytics folks working with the front line and working with the engineers, and they’ve productized some of these solutions. They have actually created scalable models which can then reduce the cost of these solutions going forward. These are some of the approaches that we are seeing some of our leading clients use to capture value from operations.
