KNect365 Finance is part of the Informa Connect Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 3099067.

Informa
RiskMinds365

Leading the global risk management discussion

Artificial intelligence: big data mystery meat for the board

The AI hype is real. Artificial intelligence is on the radar for every industry imaginable, but what is it that excites you about AI? Are you sure that what you’ve heard are not just fairy tales and exaggerations? In this article, Scott Mathis, CISO at RBC Bank US, sets the record straight and separates artificial intelligence from machine learning, big data, and other buzz words that got mixed up in AI.

Artificial intelligence (AI), an implicit worldwide super phenomenon in quantum intelligence, fast decision making, self-driving cars, and strategic thinking, is one of the world’s most hyped and mysterious technologies used by the most brilliant enterprises. Don’t be chagrined if you don’t know or understand much about it. The board members or executive management of your company probably don’t know or understand much about it, either.

We can all hear the loud call from the board and business executives to explore, understand, and experiment more with AI. But their call is still enveloped in so much misunderstood hype that it creates a lot of confusion. It is a mystery, really, how they think that AI is anything that involves computation with data. I think it is worth bearing in mind that there is still a huge amount of education that is required in executive meetings and the board room.

What is artificial intelligence?

There are so many people that conflate AI with different words: big data, computer tools, machine learning, and automation. But actually, AI is not all that. Machine learning and large volumes of data are nothing more than just computers computing data input by humans. Data input by humans is not intelligence. In other words, big data garbage-in is still big data garbage-out. I see a misplaced hunger for guided or misguided data, yes. But, is this artificial intelligence? No. It really is a tough row to hoe to expect that the more data you input, the more artificial the intelligence gets. But the intelligence isn’t artificial because it is all based on human input.

AI will not replace humans. In other words, AI will augment the existing way humans solve messy business issues which require exploiting tools like machine learning, big data, data science, robotics, analytics, and so on, and AI will enable humans to ask the right questions to innovate.

There is a huge issue between the enterprise side of AI and the public side. In my travels, meeting with board members and executives, time and again, we start talking about AI and I see a little bit of a blank expression come over their face. And then I think, ok, we have to go back to basics, right? I really must get them to understand what the core components of AI are, and stop making problems look over-complicated. If they don’t understand that, then a deliberate approach to think through the business problems is absent, and they can’t take any practical use case and implement AI.

Happily, there is a better way to think about AI. Imagine a conversation with a machine to figure out a new way to solve a problem you care about. For example, the way Luke Skywalker interacted with R2D2 to become the superhero Jedi and save the Rebel Alliance from the evil Galactic Empire. AI must help humans progress further. AI will not replace humans. In other words, AI will augment the existing way humans solve messy business issues which require exploiting tools like machine learning, big data, data science, robotics, analytics, and so on, and AI will enable humans to ask the right questions to innovate.

When the technology is more mature, it is not just about jumping a technology divide from client services to cloud. It is about this current business model which does not exist anymore.

It is hard to point to an industry that doesn’t have some kind of use case for AI today. You will find these in manufacturing, shipping, education, banking, logistics, healthcare, pharma, government services and everywhere you go. It is also hard to locate a jurisdiction or geography where there isn’t some level of experimentation as well. I believe AI is going to revolutionise how humans innovate and solve new problems and become more intelligent.

Artificial intelligence: from concept to reality

Looking ahead, firms need to ask what their innovation capacity is. Many companies have a limited innovation capacity. If they have a capacity to experiment, then I think they clearly should. The challenge, if you will, with AI technology is that it’s not just technology. It is:

  1. Introducing a whole new way of interacting with machines,
  2. Introducing a whole new way of interrogating our surroundings,
  3. Providing sight in the blind environment of machines,
  4. Creating a new world where physics does not need to fit the problem,
  5. Creating a new world where we do not need to build increasingly complex data models to account for everything that makes our world “defective” – and drown in data along the way,
  6. Creating an ability to digitise physical things with small amounts of data, and
  7. Using the Internet of Things to conduct micro transactions so you can monetise data.

It is not just about the technology and data, so stop just focusing on the technology and data.

The concern is that firms could say that AI is not mature enough and the CEO could say that the firm needs to wait until it is. And enterprises do nothing. Of course, when the technology is more mature, it is not just about jumping a technology divide from client services to cloud. It is about this current business model which does not exist anymore. Or it’s about a new economic system or a completely new trust paradigm. If firms have an innovation capability and capacity, then I think they clearly need to be doing POCs to avoid falling behind.

We shouldn’t ignore the risk management side of it either. Not because AI is a newish technology intelligence paradigm, but because it will clearly change the way you do business. It will change the terms and conditions of doing business. There is an implicit assumption in the marketplace that everyone should be using AI smart contracts. But they have no clue what that means from a legal standpoint. How do you manifest a written, negotiated, legally understood contract written in lines of code that can be distributed around the marketplace? Firms should not stop, but they should take a pause to understand the risk management implications, the legal implications, and yes even the tax and accounting implications of what AI provides.

The key thing about AI implementation

I was talking to a banking regulator recently and we were discussing what the most important thing is when they think of AI. She pointed at the temple at the side of her head. The idea of this is that it is not just about the technology and data, so stop just focusing on the technology and data. Focus on the psychological changes and the cultural changes that need to occur within your organisation. If you go through a process of interacting with machines, what are the implications on your employees? What are the implications on your customers? What are the implications on your supplier partners? If you use an AI smart contract, you are changing the locus of the decision-making process. In a sense, you are delegating decision rights to a line of code. That has a massive implication on your organisation at a psychological and cultural level. We must stop just focusing on the technology and data piece, and really think about some of those softer organisational issues. These challenges are very important.

Meet Scott Mathis and find out more about cyber risk and technology risk at RiskMinds Americas this September!

Closing banner for blogs RM Americas