This example shows that AI is a technology always on the move. Marc Carrel-Billiard, Global Lead of Accenture Labs and Accenture Extended Reality, discusses the future of artificial intelligence. What will its impact be on Industry 4.0 or what we call Industry X.O, as part of the Fourth Industrial Revolution? And how can you scale your AI solutions?
The next phase of AI
There is a lot of discussion about what AI can do right now, but for Marc Carrel-Billiard, it’s not only important about what AI can bring now, but also about what it can bring in the future.
“There are a lot of clients that talk about AI, but very few that talk about AI at scale; there’s a big difference between doing AI pilots and then doing AI at scale,” Marc explains.
The promise of Industry X.O
The Accenture R&D labs focus on applied research with a timeframe of 3-5 years, with a focus on working on research applicable to current and upcoming business problems. Marc has many examples ready: “One in particular is in the automotive industry. There is one thing I believe will be really big in the future and is already growing hugely and is something we’ve increased capacity in at the lab and that’s Industry 4.0 or Industry X.O. How are things going to be manufactured in the future and how it will be experienced by end-users?”
I think it’s important to reflect on what is important to automate and what is AI going to bring as a progress.
“An aspect that we are very interested in is quality control. Think for examples at detecting defects on leather seats which are manufactured in the automotive industry? Nowadays, there’s a quality controller who will check those, but this is difficult to spot—I mean the seats are soft they’re not made of a hard material—so sometimes it’s not even a defect it might be the leather itself. For this example, we use high-resolution cameras and machine learning to help operators in their quality control tasks.”
“We've been able to drastically improve the level of quality control that's come out of this process. Another example could be in life sciences, for example in tumor detection, using it also for classification of amoebas, viruses et cetera with petri boxes, and we can then couple this with a robot who can decide where these should be stored and classified.”
AI for internal and external use
Thinking about the future of AI isn’t just about the industries impacted, but about rethinking who the innovation is for. For some of our internal processes, we’ve created great AI-related solutions. “Another area where we’re using AI is to develop a personalized training plan for our employees, which is really useful because when you need to run a company which is close to 500,000 people then everyone needs to be looked at in terms of how we can drive their training and learning.”
Using these solutions for the greater good is in our DNA. When companies can support the broader society with their innovations, outcomes are better for everyone, Marc explains. “There are a lot of programs that the lab is doing to change the world that we are doing through our ‘tech for good program’, such as how do we address world hunger, how do we address poverty, how do we give disabled people the capability to engage, to get work, to do all these things."
"I really believe in applying digital transformations for under-developed communities because this is where the future of the world is, and the future of the business. We have a lab in Bangalore where we initiated our tech for good program, which then became viral and that is now in every lab around the world.”
When is AI a ‘must have’ and when is it a ‘nice to have’?
When thinking about the future of the technology, Marc thinks about when AI is a ‘must have’ versus a ‘nice to have’. “Sometimes, there are mundane tasks where if you eventually forgot how to complete them if they were taken over by AI then you wouldn’t be missing out on much, you can relearn it fast, it’s not a big problem and can give you more time to be more creative. However, those tasks that bring us creativity, help us learn, those are the tasks that we should really not automate because that’s where we can draw a line between machine and humans.”
“Here’s an example: I recently got a new car, and it has an automatic parking assistant. I could use this capability all the time, every day. My question is: should I? Because at some point I may have to drive a car which is not fully automated, and I will have completely forgotten how to park a car manually. I think it’s important to reflect on what is important to automate and what is AI going to bring as a progress (such as in healthcare) and what are the other ways where we need to keep these for ourselves.”
Overall though, Marc has an optimistic view on the AI-powered future ahead of us: “I’m a positive person, otherwise I would probably not be leading our R&D labs: if you're pessimistic you probably shouldn’t work in technology! So, I really have a positive view of where technology is going.”
It’s not just the technology that he’s optimistic and excited about, though: “I really believe, and I trust in, human beings…that we’ll be able to leverage AI in a good way.”
I really believe, and I trust in, human beings…that we’ll be able to leverage AI in a good way.
From AI to Applied Intelligence
Make your AI vision a reality by knowing where to start and how to scale. A strong strategy is key to making the right investments for transformation.
AI is not a stand-alone miracle solution as we’ve learned above. That’s why we talk about how AI can reach its full potential, by being applied as a concrete, real-life and extensive solution for many challenges. We call it applied intelligence. It's our unique approach to combining AI with data, analytics and automation under a bold strategic vision to transform your business. To not work in silos but make AI work responsibly across every function and every process, at scale.