AI is transforming the global economy, disrupting traditional industries, and creating new opportunities for growth. The potential for economic advancement is undeniable as businesses adopt AI to boost productivity, enhance decision-making, and meet evolving consumer demands.
What tools and knowledge do business leaders need to navigate the complex landscape of AI and capitalize on its potential? In our exclusive interview, Mohamed Roushdy, Digital Transformation and Fintech Advisor at IFC – International Finance Corporation, United Arab Emirates; shares valuable insights and answers.
What is the overall economic impact of AI? Which sectors are benefiting from AI the most?
AI will impact the economy in four ways. The first thing is efficiency improvements, and we’re going to see a lot of use cases for AI eliminating repetitive work. Efficiency here is key, with the help of AI, machine learning, and deep learning. Secondly is risk mitigation. AI has been used for years to detect fraud. Next is revenue growth, how much will AI contribute to my GDP? The revenue streams will come because of AI. This ties to the fourth element which is customer experience. These are the four pillars when we talk about AI in any industry.
The most impacted industry would be healthcare. Going back to COVID-19, AI and big data brought the vaccine to life within a few months. Other sectors include financial services where there are many use cases already. And then retail and e-commerce, agriculture, and transportation.
Where do you think AI is on the Gartner Hype Cycle?
I agree it’s now at the top of the hype cycle, but AI development started a long time ago. We are now moving within two to five years from the hype cycle to the mainstream and actual use cases. We are coming out of the hype, and I would say there is a triangle of technology — AI, blockchain, and IoT. AI is the one that is going to deliver great value and pass the hype.
Going back to efficiency — what are your thoughts on AI’s impact on the workforce?
Most repetitive work will go. This will displace a great number of employees, I think studies are saying by 2030, AI will displace 85 million workers. This number looks scary, right? But AI will also generate 97 million jobs. So, the net is positive, not negative.
Seeing what’s happening here, we need to ensure organizations and governments are reskilling and upskilling the workforce.
The same workforce that does repetitive work in your company knows your business well. You cannot just let them go.
You have to take the same people, reskill them, and put them elsewhere in your organization. AI will affect the workforce but it’s a positive impact. Upskilling and training are very much required.
It’s difficult for people in the workforce to imagine this new way of working. What’s the role of a business leader in this transition?
Training and awareness are important to open new avenues for them, and this extends to governments as well. Business leaders have a social responsibility to get this segment trained and moving ahead. As I said, the number of jobs generated by AI and machine learning will be higher than displaced jobs. But we have to make sure that employees are being trained.
I remember one professional I met at a forum saying, “You have to learn how to learn.”
This is really the time we must learn how to learn.
Nowadays, people don’t only learn by going to school or taking a course. There are many ways you can learn online. There needs to be resources and guidance for employees around AI and machine learning. This is very important for the new era.
What are your thoughts on the relationship between data regulation and innovation?
A very important concern about AI and machine learning is data privacy.
We are seeing great moves from countries and organizations to regulate AI use and provide guidelines. For example, the EU AI Act. So, regulatory frameworks and privacy laws are in place to help data privacy. But at the same time, we’re trying to make sure this won’t stop innovation. If you get hit by regulation and stop innovation, you won’t see the benefits. That’s why we’re trying to see how we can balance regulation, convenience, and the ability to innovate.
We must also focus on principle-based regulation. This is what most countries are going for. One of the most important things here is AI ethics. For example, there are guidelines for your AI platform or whatever you’re developing that must have AI ethics in place. What’s important is that innovation shouldn’t stop.
How would you advise business leaders to handle risk mitigation around AI?
If there is no risk, there is no reward. It’s important to accept that there is some kind of risk if you want to integrate AI and move forward. But it has to be calculated risk and you have to know how to mitigate them if they happen. Without risk, you cannot innovate. As I said, any guideline should be principle-based and help foster innovation. The developments we see today with AI and machine learning happened because people took calculated risks. Yes, there is a dark side to it like deepfakes. The bad and the good will always be there. But that doesn’t mean you have to stop. I would say today’s technology brings more good than bad.
Can you share an experience of maximizing profit using AI?
I come from banking, and AI enables customer personalization.
You can know what the customer needs today and in the future. You’ll be able to do things more effectively and generate more revenue because you are doing things differently. The data you have provides more insights; maybe your customer does not need financial services abroad or maybe he needs another product your competitor has. So, you can start doing new things today because of AI, machine learning, and data like bring a new product to the market and potentially bring more revenue to your organization. AI also creates seamless customer experiences.
When you bring IoT, blockchain, and big data together with AI, the value becomes exponential.
This combination enables faster transactions, and you’re able to make more deals and make the customer happy with seamless or frictionless experiences.
What is your advice to business leaders on implementing AI successfully?
Implementation is easier said than done. It also needs cooperation from stakeholders. My advice would be to know exactly what you want to achieve and look for a good use case.
Start with a small use case, bring all the stakeholders within your organization in, and educate them to get the C-level buy-in. Show them the value AI will bring to your organization and the market. The issue with AI is that the algorithm development takes time, as with value and results. You won’t see results six months after implementation. You’re tuning the algorithm, you’re getting the right data, and avoiding AI bias as well.
There are many resources needed to train a model — budget, people, and more. At the same time, if you don’t have the expertise, try to find good partners to help you get use cases. As soon as you get results and management sees something happening, then you can scale up. You have to go into the journey with a good plan and convince people with results. I think that’s very important.
*The interview answers have been edited for length and clarity.