artificial intelligence

Digital isn’t merely an add-on. It’s a way to think differently about business models, customer journeys, and organizational agility.

Achieving A Digital State Of Mind

Companies have more data at their disposal than ever before, but are they doing enough with it? Taking full advantage requires tackling legal, regulatory, and talent challenges.

How advanced analytics can drive productivity

There’s greater potential in big data. What’s ahead as the field matures?

How to win in the age of analytics

The McKinsey Podcast

How a digital factory can transform company culture

Artificial intelligence in business: Separating the real from the hype

Over the last year, we've covered a lot of marketing applications. Many people know of our deep marketing research we've done on the landscape of machine learning in marketing applications and which industries will be affected first. But marketing doesn't tell the whole story when it comes to B2B sales. At some point, we need to take these clicks and turn them into appointments, for example.&nbsp;In this episode of AI in Industry, we are joined by Vitaly Gordon, VP of Data Science and Engineering at Einstein, Salesforce’s customer relationship management application driven by artificial intelligence. We speak with Vitaly about where AI is serving a role in sales enablement today and how the CRM and sales tool ecosystem might&nbsp;be different in the near-term future; how will salespeople be able to leverage AI to make themselves more productive? Vitaly paints an interesting picture of where he sees the low hanging fruit and the unique challenges with sales data and B2B data that are quite different from the challenges those in the B2C world might deal with.

The State of AI for Sales Enablement, and the Evolution of the CRM

Artificial intelligence has the potential to create trillions of dollars of value across the economy—if business leaders work to understand what AI can and cannot do.

The real-world potential and limitations of artificial intelligence

Whether we're talking about customer service, marketing, or building developer teams, what we try to do on our AI in Industry&nbsp;podcast is bring to bear lessons that are transferable. There are few more transferrable ideas than what makes a company ready to adopt AI. When it comes to the willingness and the ability to integrate AI into a company strategy and to fruitfully adopt the technology to really see an ROI, what do the companies that do so successfully have in common? What do the companies that are not ready or too fearful to do it have in common? There are probably few companies in the AI vendor space that are aiming to sell AI more ardently into the enterprise than Salesforce, and there are few people that know more about how that process is going than Allison Witherspoon, Senior Director of Product Marketing for Salesforce Einstein, which is their artificial intelligence layer on top of the Salesforce product. We speak to&nbsp;Witherspoon&nbsp;about the telltale signs of a company that understands the use cases of AI in their industry and that have a good chance of driving value with AI. We also talk about the common qualities of companies that might not ready for I adoption. Read our full interview article on Sunday at Emerj.com

What It Looks Like to Be Ready for AI Adoption in the Enterprise

Executives and companies can enjoy the benefits of artificial intelligence while also being aware of potential drawbacks and taking careful steps to mitigate their effects.

The ethics of artificial intelligence

A lot of AI in the press is CMOs or marketing people talking about what a company can do in a way that really is aspirational. They're speaking about what they can do, but in reality, the things that they're talking about, the capabilities won't be unlocked for maybe a year or more. These are just things on the technology road map, but people speak about them like they exist now. This week, we speak with Abinash Tripathy, founder and&nbsp;Chief Strategy Officer at Help Shift. They've raised upwards of $40,000,000 in the last six years to apply artificial intelligence to the future of  customer service, and we speak about the hard challenges of  chatbots and conversational interfaces, as well as how long it's going to be until those are actually robust. This in opposition to how people at large companies might put out a press release touting their own chatbots that simply aren't capable of doing what they say they can to any meaningful degree. We also talk about where AI can augment and make a difference in existing customer service workflows.&nbsp; Even if we can't have all-capable chatbots to handle banking or  insurance or  eCommerce questions from people, where can AI easily slide it's way in and actually make a difference today? In this episode, we draw a firm line on where the technology currently stands. Overall, though, this episode is about the challenges of actually innovating in AI. We talk about why it really is the big companies that do a lot of the actual cutting edge breakthroughs of AI and why others are going to have to license those their technologies from large firms like Google and Amazon. We also discuss why companies maybe need to have a realistic expectation about where they can apply AI, as well as why actually innovating and coming up with new AI capabilities on their own might just be wholly unreasonable given their data, their company culture, and their density of AI talent. Read the full interview article on emerj.com

Why It's Exceedingly Difficult to Build and Adopt AI in Business

I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't. This episode, we interview Brooke Wenig, the machine learning practice lead at Databricks. Databricks was founded by the folks who created Apache Spark. Those of you who are technically savvy with AI will be familiar with Apache Spark as an open source language for artificial intelligence and distributed computing. Wenig works with a lot of companies with Databricks. Databricks is now close to 700 folks&nbsp;and helps implement AI applications into, oftentimes, large enterprise environments. Wenig speaks with us this week about what to look for in an actual data scientist and how to find data science folks with the right skills to be able to communicate to business people, not just to work with models. What should people be capable of; how should they be capable of thinking? Hopefully, some of you will have better interview questions by the end of this podcast. In addition, we ask Brooke about what the value of covering the cutting edge applications of AI is, looking at what's working in industry. How does that help us in our own business make better decisions? Read the full article on Emerj.com

artificial intelligence

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