Economic Evolution's AI Blog

CEO of Microsoft on "Turning artificial intelligence into value"

This is the excerpt a McKinsey Quarterly (April 2018) interview with Microsoft CEO Satya Nadella. 


Simon London (Mckinsey Quarterly): Can we pivot and talk a little about AI? What advice do you give to executives that you talk to about how to leverage AI in their businesses?

Satya Nadella (CEO Microsoft): I believe AI is one of the more defining technologies of our time. One of the things I am most excited about is AI technology helping with inclusivity. For example, in the latest release of Windows, we have something called Eye Gaze, which allows anybody who is suffering from ALS [amyotrophic lateral sclerosis] to be able to type just with their gaze. We have learning tools inside of Word and OneNote that allow anyone with dyslexia to improve their reading. It’s powerful stuff, and it’s a very practical way for executives to deploy some of these tools so that more people in their workforces can fully participate, which is important.

But there is no question that automation and the efficiencies of automation are tremendously important. For example, if you go to support.microsoft.com, it’s a bot. It uses some of the latest techniques of reinforcement learning to answer questions that customers may have. And of course, if it runs out of gas, it turns over to the customer-service representative, who is also using the bot to help answer the question.

So, we have the full gamut of technology that is getting deployed. We now really have human-level speech recognition. In January, there was a contest at Stanford University for machine reading and comprehension. Microsoft was number one. This means a machine can read a piece of text and start answering questions, like a reading-comprehension test, without necessarily being fed the answers that are indexed in the text.

The advances are enormous, and they will lead to productivity gains broadly. Therefore, every CEO—every executive—should be thinking about how to get more analytical power or predictive power inside his or her business process or organization. That’s ultimately what’s needed to translate AI capability into productivity.

Simon London: What types of IT configuration and IT capabilities do companies need to do this?

Satya Nadella: I would say there are two big considerations. One of the fundamental things is that there’s no way to create AI if you don’t have data. If the data inside your organization is siloed, it’s going to be a challenge to create AI. This goes back to your point around company configuration.

Take customer connection as an example. In order to be much better at omnichannel customer connection—and it doesn’t matter whether you’re a retailer, a CPG [consumer-packaged-goods] company, or a bank—everything from the log data from your website, to your mobile analytics, to your CRM [customer-relationship-management] system, to all the other data streams, it all has to come together in order to create the next best touchpoint action with the customer. This is both an AI problem and a data problem. One of the things that we like to stress is: how can we help our customers first get their data estate in many cases into the cloud? Then they can reason on top of it and create these transformative outcomes, whether it is connecting with customers, or operational efficiencies, or even changing the nature of their products. This is a super important thing.

I would also add that trust is going to be of paramount importance. Not just the security side of trust but also the trust of the business model. You need to pick partners who are going to help you with your capability building, whose interests are aligned with your interests in the long term.

Simon London: If you’re a senior executive at a big industrial company, for example, there are a lot of different potential use cases for AI. Do you have any generalizable advice about how to look across those use cases and what to go for first?

Satya Nadella: When I pattern match and look at some of the best and easy-to-get-started use cases, it would be anything related to customer experience. This is a good use case. Let’s say there’s omnichannel customer data. The ability to do the next best action, whether it be a sales force, or inside sales, or your website personalization, can come in a variety of different ways.

Connecting with your customers more deeply—using your data and your ability to reason over data—using the latest AI techniques is one use case. The second use case is supply-chain or operational efficiencies. The IoT [the Internet of Things] is a fascinating thing. If you think about it, most of these projects are where you have a good or a service, you’re collecting operational data from it, you’re doing preventive maintenance, and then you’re going to connect it to field service, because once you can predict something, you want to connect it to somebody coming and fixing it before it’s broken. That’s a thing that can drive both top-line and bottom-line efficiencies. That’s a great use case, and we see a lot of it, especially in industrial companies. We also see a lot of deployment of technology to empower people inside the organization. I’m fascinated to see how HoloLens is being used for doing oil-field inspections or training. So, AI can be deployed not just against traditional knowledge work but also in what I will call frontline work.

Sometimes organizations have this “cobbler’s children” problem. They talk about all these great things they’ll do for customers, for [business] partners. Except you also need to do fantastic things for your employees so that they can do all these great things for customers and partners.


Full article entitled "Microsoft's next act" 

We need to Watch Artificial Intelligence

Last year MIT technology review published a meta-analysis when artificial intelligence  (AI) experts expected certain categories of jobs to be taken away by AI. 

If these estimates are true, there is a fundamental question for society: Will the economy be able to create new jobs fast enough to compensate for the loss of jobs due to AI. Economic history has shown that western capitalist societies have always created new jobs in the face of rapid technological change. 

But what if this time is different? 

The goal of Economic-evolution AI blog is to watch the progress of AI and help interpret what future scenarios are more and less likely to unfold.  We want to help develop an informed understanding whether the AI optimists or pessimists are more likely to be correct. 

Among the optimists are many economist historians such as Joel Mokyr (Interview) or  Caspar Hirschi (Interview in German)  who take the position that there is nothing to worry about. Societies will not have to change radically.  
The pessimists fear that AI will destroy jobs and unprecedented magnitude and speed. If the rate of job destruction is going to be far larger the rate of new job creation, then we will have to radically redesign society because jobs will no longer provide the money to pay livelihood of most people as is the case today. Aija Leiponen is an example of the pessimistic voices. 

Our goal is to watch very closely the evidence and help everyone make up their minds how optimistic or pessimistic we should be. And if society needs to be redesigned, we better start thinking and experimenting with new forms of supporting the livelihood of all members of society without relying mainly on paid jobs.