Artificial intelligence has undoubtedly become a game changer for organizations worldwide today. We see that they plan to ramp up their use of this technology significantly over the next two years and invest significant dollars in doing so. This is Kevin Donahue, senior director with Protiviti’s Marketing Group. These are some of the key takeaways we learned in a global survey Protiviti conducted on the use of artificial intelligence and machine learning by organizations around the world.
I’m pleased to be talking today with Madhumita Bhattacharyya, who is a global leader for Protiviti’s artificial intelligence and machine learning practice. We’re going to be talking about some of the high-level findings and insights we gathered from our global survey. Madhu, it’s great to speak with you today.
Same here, Kevin.
Madhu, to start us off, these discussions or descriptions can vary by different organizations, so why don’t you give us your definition of “artificial intelligence”?
Sure. The word artificial tells you that it’s not real and intelligence is the power of thinking. So, to me, artificial intelligence is the science of training systems to emulate human tasks through learning and automation—or, in other words, we, as human beings, we are trying to make the machine think the way we would think.
That’s a great summary of AI, Madhu. Thank you. I want to dive into some of the results from our study now. There were a number of notable takeaways. I mentioned before that we know many organizations are using AI today and even more will be doing so in two years. We also know they plan to ramp up their investments and deploy AI technologies across their organizations. What findings and takeaways from the study were most notable to you?
At Protiviti, we work with companies to help them incorporate AI technologies into their current business model, and many of our clients actually find deploying and managing technologies like AI and machine learning to be a significant challenge. We wanted to understand how companies around the world are actually using AI to rethink their business strategies or even practices or organizational approaches, how they are building their business or transforming their business. What is it that they want to get out of it or expect to gain?
To do so, we did this survey that you mentioned. We partnered with ESI ThoughtLab to survey around 300 senior executives around the world from around 13 countries across different business functions, industries and company sizes. What came out of the survey that we did is that the adoption of AI hasn’t been very significant as of now, even across industries. It is still a growing thing, and people are trying to figure out, “Is it appropriate for me, or, if it is appropriate, then what is appropriate for me from an AI application perspective?”
Then there are certain leaders. They were like, “OK, let me just see where it takes in the next two years before I plunge into it.” That was the kind of sentiment that we got to see from the survey. In the majority of the type of industries that we focused on, we saw that from a maturity perspective and an AI-adoption perspective, the healthcare industry and the FSC industry have been pretty instrumental in adopting it, followed by technology as an industry, but the rest is still shaping up. They are still trying to evaluate. They are still trying to make sure that they have the right resources to really come up and adopt AI applications for their business decision-making. That’s the trend that we have been seeing.
That’s really interesting. Thanks, Madhu. There was one other topic I wanted to address with you, and that’s around ethical artificial intelligence. There’s extensive discussion in the market right now around that. Clearly, there are ethical issues around how AI is being used today and, more importantly, how to plan for its growth in the years to come and building ethical practices around that growth. Madhu, what are your views and some of the things you see companies having to think about in this area?
Actually, this has been a hot topic nowadays, and, like I mentioned in the initial definition, we, as human beings, we are trying to make the machine think the way we would think. It can be used for the good of the society, and it can be used for something which is not so constructive. Practically, with the advent of a huge amount of data that is available, people have accessibility to all the touchpoints, and we can really go ahead and try to find information from all the data, which is accessible by systems, by technology, and what people are trying to do is make use of the data to come up with insights.
Easily, it can be said that if you are not using the right data sets, if you are not using the right algorithm for your AI application, definitely, then, what happens is, the information or the output that you get out of it tends to be biased, and that’s exactly not what is going to help you make your unbiased decision, help with your unbiased decision-making. At the end of the day, we need to make sure that it’s not only important what we can do but it’s also very important to say that what we should do, and practically, I say this because we as data scientists, we as AI professionals, we need to know and we need to propagate the use of the right data with the right algorithm, keeping the business objective in mind, keeping the goal in mind to really come up with the right output that will enable us to make the right decision. That is beneficial for the business, which in turn is beneficial for the larger society.
Now, the reason why we are talking about controls, why we are talking about checks and balances, in this space is because very easily, you can use biased data and definitely the algorithm with the biased data that you are feeding it, and it will not give you the right output. The decision-making that comes out of that not-so-right output actually is not the right decision. A lot of discussions around ethical AI have been around, and I think rightly so, because people are concerned that with this huge amount of data that is available and accessible, we should be able to make the right use of it with the use of the right AI application. Ethics plays a very important role, and, just like I said, we should think about what we should do rather than what we can do.
That’s a great rundown. Thanks, Madhu, and thank you very much for your insights on our podcast today, sharing your thoughts on ethical AI’s roles, as well as some of the highlights from our survey.
I want to point our audience to a couple of different resources we have on these topics. First, Protiviti recently published a white paper called Artificial Intelligence: Can Humans Drive Ethical AI? We also have both our full report and stand-alone executive summary going over the results of our global survey. These are titled Competing in the Cognitive Age. Both of these are available at Protiviti.com/ai.