Chief Information Officers Will Become Chief Intelligence Officers

Original
Chief Information Officers are the rulers of corporate data. They are responsible for picking the right data to process into information, and then squeezing out meaning that will drive business forward.

And the way they do this is about to change.

CIOs have done their job under a command and control paradigm. Their number crunching machines, no matter how sophisticated, can only do exactly what they are told. These systems can’t recognize patterns they weren’t specifically instructed to look for, leaving CIOs with limited freedom to automate decision making and instead investing their time into constantly updating their models.

But AI software changes this. It can independently identify unexpected patterns or changes in the data and update itself, which will forever change the corporation’s relationship to data.

Moving from Information to Intelligence

Andrew Ng has argued that you ought to hire a whole new AI officer to handle this transformation. Yet, I am of the view, along with a few others, that implementing AI will simply change the way CIOs do their jobs.

The change I would like to see is that Chief Information Officers become Chief Intelligence Officers. (They won’t even have to change their acronym.)

Once companies are equipped with AI technology, the CIO’s role will necessarily change. Their relationship with information systems will be more like a two-way conversation than a set of one-way commands. It will require more of that unique human quality, nuance.

The Process of Data

Data is created by sensors perceiving the world. That data is then turned into information through cognitive tasks, or analytics. Based on those results, we can make decisions and measure the results, which we then assess to train and fine-tune our models.

For now, humans manage the cognition, decision making and training of information systems. What AI will do is amplify cognition by crunching ever-larger and more complicated possibilities, while automating decision making and training. This leaves the CIO, the human, to focus on the data inputs created through perception and assessing the final results for the business.

For example: Before you can start using AI to solve any problem, the software model needs to be trained with the right dataset. Discovering what data will make the AI work best will be the CIO’s job. Of course, the CIO has always been in charge of deciding what data is most relevant to their business goals; but, with software that can adapt to new data as fast as it’s produced, the CIO must adapt right alongside it.

Though, not all data are created equal. A huge job for CIOs will be data governance, which will include “cleaning” the data so that the AI can do its job. AI is so valuable because of how quickly it scales, but this puts that much more importance on identifying gaps and filling them with quality, relevant data.*

The upshot of this hard work is that the CIO will be able to deliver higher-level meaning with AI. Information is data in context—points in a graph. Meaning, or intelligence, is information in context. For instance, what does a certain pattern in a graph mean in light of our previous experience of similar patterns? Or, put another way: what does rain in the forecast mean for sales given our previous experience of rainy days?

Comparing similar experiences so that we can make better decisions is the cognition task that AI amplifies. The CIO’s role will be to help make sure that that “similarity” gets very specific to the decisions the business is making. “Rain in the forecast usually leads to a drop in sales” is somewhat helpful; whereas “Rain on April 15th means x% drop in sales in region A, with y% increase in sales in region B, and the system is making z adjustment” is a lot more valuable for reaching business goals.

Early on in the two-way conversation, the AI’s sense of similarity will be rather hazy, or may even be inaccurate. As the conversation continues and the CIO is able to better align business goals with the data, that similarity will become sharper and sharper. As this tuning progresses, the CIO’s role will elevate from managing information to managing intelligence.

The New CIO in Corporate Organizations

I think CIOs will need a mandate, not unlike a company’s mission statement; however, don’t expect a C-level coup in established organizations implementing AI. CIOs will still collaborate with Chief Technical Officers to implement the AI software in the existing technical architecture of the company.**

The CIO will also start encountering ethical issues head-on. AI systems depend on enormous amounts of data, bringing up an important consideration of how that data is secured, and cleaned, on a large-scale basis. This will be even more relevant if any of that data is considered sensitive or private, such as payment information.

The evolved role of the CIO will not require an entirely different skillset. CIOs will still need to keep up with sophisticated views of leveraging technology to drive business outcomes. While a background in artificial intelligence isn’t an absolute must—they will need that nuanced skill of adapting and using new technology on the fly.

Because otherwise, the AI software will eclipse them, and that’s not very intelligent.

*This article was originally posted on Jean-François blog and can be read here.

Interested in learning more?

At the Deep Learning Summit in Montreal this October 10 & 11, Jean-François will be discussing his current research at Element AI and will explain 'How AI will shift operations from 'command & control' to 'learn & adapt'. Passes are still available for the summit, but previous editions have sold out so register now to guarantee your place

Original
Deep Learning Pattern Recognition A I Deep Learning Summit Business Applications

0 Comments

Search

Recommended Posts

Latest Posts

Upcoming Events

Track 1: Deep Learning Summit Montreal

10 October 2017, Montreal

What are the latest advancements in deep learning research? Where are the most recent scientific breakthroughs? Hear the latest research news from global pioneers in Natural Language Processing, GANs, Reinforcement Learning, CNNs and Unsupervised Learning at the summit.

Track 2: Deep Learning Summit Montreal

10 October 2017, Montreal

Deep Learning is quickly becoming a core technology for businesses from helping to optimize efficiency to increased forecasting capabilities to providing increased automation. What problems can deep learning solve in your business? How can it be practically applied in industry?

Deep Learning in Finance Summit London

15 March 2018, London

The Deep Learning in Finance Summit is a multidisciplinary event bringing together data scientists, engineers, CTOs, CEOs & leading financial corporations to explore the impact of deep learning in the financial sector. Applications include identifying and preventing risks, revolutionising financial forecasting & compliance. Explore the latest technology trends & innovations with influential research scientists, startups & business leaders across the industry.

Connect

Be Sociable

  • Twitter
  • Facebook
  • Linkedin
  • Youtube
  • Flickr
  • Lanyrd
  • Instagram
  • Google plus
  • Medium