Search
Homepage Rankings and Research Companies Channelcast Marketing Matters CRNtv Events Acronis #CyberFit Summit 2021 Avaya Newsroom Experiences That Matter Cisco Partner Summit Digital 2020 Cloudera Newsroom 2022 Intel Partner Connect 2021

IBM CEO Arvind Krishna: ‘Win-Win-Win’ When Partners, Customers, Big Blue Work Together

‘[Partners] all make a lot of money by helping our clients deploy these technologies—and by these technologies, it is Red Hat, it is automation, it is AI, it is cybersecurity, it is even mainframe and power and storage in some cases,’ IBM CEO Arvind Krishna tells CRN ahead of the company’s Think conference.

Back 1   2   3   ... 15 Next
photo

On IBM’s AI Plans

So as an example, here on the floor [what] we’ll be showing in Boston is Watson Orders as a partnership at McDonald’s.

When you look at that, it’s all about how in an environment you have to match what a person orders against the menu—it’s not general question and answer.

You have to do that and you have to understand how they might modify an order. As an example, we were just this morning trying it out just as a pretend customer.

You order a Quarter Pounder, then you take onions off. You might add extra tomato. You order a drink. It asks you ‘medium or large or small?’ When you get to combo meals, it asks you all the things.

So this is, I think, a great example of enterprise AI. It has to align to McDonald’s back-end process off the menu, which changes weekly. Or even more often sometimes.

It has a constrained set of answers and has to understand the workflow of what the restaurant wants.

A second great example is AI being applied to information technology. How could you make somebody in IT eight to 10 times more productive? When we think about running applications and understanding what can happen and understanding that a problem may be showing some signs of coming on—so being predictive, not just reactive—is how you get an uptime increase, as well as much better productivity. And that’s the second example we have around a set of products that I’ll put under the category of Watson AIOps.

So these are examples of how we’re solving problems. People are willing to put them into production. People are willing to try them out. With the current demographics of labor and skill shortages, that becomes even more relevant, as opposed to—by the way, I do believe that that’s why … Watson is alive and well.

I do believe that some of the health-care examples will happen, but they might take a half-decade or a decade more to come to fruition just given how hard those problems are. And the implications are life and death. So that’s how I categorize what we should be doing now. As well as keep working on the others, but they’ll take a lot more time.

 
 
Back 1   2   3   ... 15 Next

sponsored resources