Welcome to FinTech TV.
I'm Remy Blair.
Artificial intelligence is changing the way that software is built and trusted.
At the same time, with full-scale adoption comes with opportunity and risk.
Some of the challenges are growing as fast as the technology itself, as the world leaders in data and AI are gathering at the NYSC today for the AI summit, Future of AI is top of mind.
Joining me on the trading floor at the New York.
Stock Exchange is Mona Human, who is co-founder and CEO at Nagaro.
Thank you so much for joining me today.
Thank you for having me.
Thanks.
Yes, and Agente AI, there is no doubt that we are seeing this growth across all industries.
So what is the opportunity, but also some of the challenges as well.
See, I think the opportunity is to drive a large productivity gain across different industries, and I think every board, every CEO is just really focused on that.
But it's not a very short term goal.
It's a relatively medium term goal, a bit like digital transformation.
It just takes some time.
And I think that the more digital native jobs and industries are quicker onto it.
It's easier for them to adopt, whereas more traditional industries, you see this happening at the periphery, you see this happening individual knowledge workers or but it's the enterprise class change is yet to really get started, but I do expect that in the next few years it's going to be really a big, big deal.
But there are also challenges, right, and you have the challenge of the fact that human beings working in a team.
It's one thing to empower the human being, but to empower the team and to work out the dynamics between them and the or designs and the structures.
They're all, they all have a long history, and they have each team has its own budgeting and objectives and incentives and how do you actually cut across all of that and drive the enterprise forward?
That's that's a lot of what needs to be done.
And we're here on the floor of the New York Stock Exchange, and we're thinking about the companies that are listed here on this exchange and it's about the bottom line.
So when we're talking about people and productivity, walk us through the term fluidic intelligence and why does that matter?
Yeah, so that's our idea that you know it's not that AI is bringing its own intelligence to the enterprise, it's actually unlocking the intelligence the enterprise already has, the people already have.
And in order to unlock it, you have to sort of break down these silos and you have to think not in terms of these individual pilots, which there's a lot of talk about these pilots not working out and stuff like that.
We'd rather to see it as a transformation program, right, which you have to set the vision and you have to get the alignment and move in that direction, all the change management and all of that that goes along with it, fully knowing that technology will keep improving along the way, but you can't wait to set off on the journey, right?
So.
I think that floating intelligence is about, you know, unlocking that intelligence that exists in the enterprise and elevating the enterprise as a result of that.
And obviously this might come as second nature to you, but can you explain to us why human input actually matters when it comes to implementing artificial intelligence?
So artificial intelligence is like a very powerful tool, right?
But at the end of the day we want to use it in the service of human beings, right?
And I think the human being is the one, at least for the foreseeable future.
That's trying to figure out what they want to do, what innovation they want to drive, what direction they want to go into.
So there's one aspect of that which is all the all the AI is doing at the moment is more or less processing human knowledge and human learning and innovation and giving serving it to us, serving some variants of that up to us.
So the innovation will continue to really be coming from the human being for a lot of the things that we are doing.
At the same time there's this check and balance, right, because we have all seen the dangers of AI and so for example we're trying to teach people not just to write code, which you've always taught, but now to read code because you have to check what the AI is doing.
So it's quite quite an interesting sort of state of affairs, but the human will stay in the loop for the foreseeable future.
And can you share a success story that does actually include achievements as well as improvements?
Oh, it's just lots of stories across different industries, there's stories on, you know, better forecasting, better pricing, better they're all a bunch of different stories on that side, but now more agentic workflows where you're trying to see how to really improve productivity in end to end chains of operations.
So, so this.
Some of these are with clients that don't want their names to be mentioned, but it's there's a fair amount of good case studies that are starting to emerge now from from industry.
And obviously when we're talking about agentech AI infrastructure is key here.
So explain to us why it matters so much and what we can expect as we head into 2026 and beyond.
So infrastructure is the platform on which all of this is being built, right, and And you may say it's there's a bubble in infrastructure or not, and there's a whole lot of discussion around it, but what I do believe is that in the medium term there's going to be a huge amount of escalation in demand for this infrastructure and it's difficult to predict, you know, when the takeoff will come, whether it will come.
I mean some of it has already started, but if it will really take off in the middle of next year or.
The end of next year, but in general the the need in the medium term is unquestionable.
And Manas, depending on who you ask about adoption with artificial intelligence, they may say we're in the early innings, but when it comes to success, there are also many failures that we might not see.
So why are companies failing at AI and what lessons can you take away from that?
See, I think where you've seen successes are in the Individual worker, the individual knowledge worker using AI for their own productivity, that's the most common success.
We all use it ourselves, but I think where the companies are failing is where they're not really seeing, they're pushed by the boards to go and run some experiments, but this is not the kind of thing that especially when it comes to multiple people or multiple teams working together and you're trying to build agentic workflows on top of that.
This is not a technology topic.
It's an organizational vision and organizational design and change management topic as much as a technology topic, and I think that if you don't see that together.
I think you run the risk of failure.
And finally, you're here at the New York Stock Exchange today to attend the AI summit.
What are thought leaders and you yourself going to be learning and actually talking about today?
I think we have a huge variety of leaders from different industries, different.
Types of roles and I think the opportunity today is to share what we have all seen in our own context and see if you know we can emerge or draw from that some broader learnings about where this whole revolution is headed because it's nothing less than a revolution.
I think it's something which we all need to find the patterns that are working and then to sort of amplify them and that's kind of what the summit is all about.
Well, thank you so much for joining me here at the New York Stock Exchange today and thank you so much for sharing your perspective.
Thank you.
My pleasure.
