Manufacturing firms worldwide are convinced about the immense value of Artificial Intelligence (AI) in capturing market opportunities and future growth plans. Even so, they are discouraged by the high failure rate of the AI initiatives. They also struggle to scale AI projects.
We spoke to Sachin Lulla, Global Digital Strategy and Transformation Leader, Ernst & Young, to get his insights on the crucial factors that industrials must consider to ensure successful AI projects.
We engage with manufacturing clients worldwide, and a fundamental dilemma they face is this idea of the duality of growth. How do we transform today's business with technologies like AI, unlock capital, reinvest and drive a growth agenda for tomorrow and beyond?
Essentially, three themes continue to come up. The first is manufacturing companies are looking to put customers at the center of everything they do, as B2B companies are now shifting to a B2B2C model, as consumers have adopted digital channels and demand similar experiences for industrial products, such as cars.
This has put a lot of pressure on manufacturing companies, this whole idea of resilient supply chains, and building for not just in time, but just in case with optionality and dual sourcing, applying AI to respond to the pandemic and global trade issues we face today. Then the third and the most interesting topic is how do we scale digital.
As part of our research, we interviewed more than 500 CEOs from leading manufacturing firms across the world, and 86% of the CEOs acknowledged that AI is crucial to the success of their company. Paradoxically, only 30 of them have actually been able to scale AI and other emerging technologies to truly drive business value. Around 400 of them are lagging and stuck in the world of what is often referred to as pilot purgatory.
And what's interesting is, with the considerable focus on continuous improvement and manufacturing, companies have either taken a lean-first approach by rolling out Kaizen, and other similar initiatives or are taking a technology-first approach to deploy technologies such as AI, IoT, machine learning, and other capabilities, but have failed to drive continuous improvement.
As manufacturing companies are designing and engineering connected products, they've implemented in various technologies and platforms, they've done the same on the Operational Technology side. And now, with the big push on cloud and edge with a big focus on security and bringing it IoT together, there's a big push from the CIOs on rolling out IT solutions.
So, the reality is that manufacturing companies have made significant investments in technology. What's happening is the investment has occurred in silos, across engineering and operational technologies.
However, sometimes, even after investing millions in technology across operational environments and engineering, chief executives tell us that they don't see value from digital.
Now combined with AI, machine learning, 3D printing and IoT, all of these technologies are putting a lot of pressure on manufacturing companies to really think about where and how they get started to drive value at scale.
What we are seeing is that AI is ready; we've deployed AI and machine learning use cases across our clients globally, both on the process side and discrete side. When combined with other digital technologies and standard ways of working, we believe AI will drive and enable zero-touch operations and zero defects. It's how you combine capabilities that AI offers with the right cloud-enabled purpose-built platforms and the right digital infrastructure but above all a business led approach to go after high value opportunities first.
Four things are absolutely critical when you think about starting your digital transformation journey enabled by AI. Most companies make the mistake of jumping into experimentation without really having a clear vision and a roadmap tied to a business case. It is crucial to define what value you plan to unlock as you deploy AI.
Secondly, is using the Return on Investment (RoI) lens to align all the stakeholders. Don’t pursue anything that doesn’t have a clear ROI. It is also important to constrain your use case roadmap with a clear understanding on value to customers, value to employees, and organizational readiness. Also at the end of the day, data quality is going to make or break your AI journey. So, investing upfront in a holistic approach to data engineering, data quality is critical. Then look at your roadmap to have a deeper understanding of what data you need to deploy those use cases at scale.
The third critical thing is the platform, both from a cloud, edge and AI perspective. A purpose-built industrial-grade platform that will accelerate your journey because they've thought through these use cases before building a data dictionary of how you enable those use cases at scale. Further, they can also do that on a global scale and give you the security that is fundamental as you connect the manufacturing shop floor to the cloud. It's important to pick the right purpose-built platform that is secure as you start your journey.
The fourth and most important thing is that you need to put people at the center of your AI-powered transformation. In the end, it is the people that will make decisions based on that data and take action. Data without insights is irrelevant, and insights without somebody acting on them are useless, which is why we believe people are at the center of driving transformations with AI at scale, as you create a high-performance culture and become a data-driven company.
To learn more about how to transform your operations with the power of AI in 60 days check out this panel discussion, featuring Sachin Lulla, Humera Malik - Canvass's CEO, and Michael Gardiner from Microsoft, here.
.png)
