In May, a group of programmatic and people-based marketing technology providers vowed to create a standardized, universal identifier that would be used by buyers and sellers across programmatic channels and platforms. The new standard would replace cookies and other proprietary identifiers used in programmatic bidding so that buyers and sellers have a better idea of the people behind the data they are targeting—as well as their histories, preferences and intentions. How does a universal identifier change the game for buyers and planners? Will it cause them to shift spending away from Google and Facebook towards other media? What privacy and fraud concerns does a universal identifier raise?

The technology behind programmatic advertising is improving all the time. Add bots to the stack and suddenly campaigns are learning how to target, test and optimize on the fly, at scale, and in real-time. While advancements in automated marketing should be game-changers for brands, the fact remains that technology can’t do everything. Only humans can turn stacks of laser-targeted tech into a conduit for richer customer relationships by creating stories that appeal to real people. After all, humans still interpret context better than robots—at least, for now.

If identity graphs and ID matching can really bring buyers and sellers to the 1:1 Promised Land, and programmatic systems and machine learning can test, predict and optimize campaigns against identified segments at scale, then the future surely belongs to cross-channel marketing…right? But when you pair first-party, people-based identity data with second and third party programmatic technology does that necessarily empower and enable cross-channel data to flow freely?

The most powerful uses of AI in marketing most likely will be invisible to consumers and even to many marketers. They will come in automated decisioning about ad buys, adaptive responses to campaign performance across screens, and among departments and partners. How are brands reorganizing for this data-centric, customer-first future? Are their analytics, attribution and media systems learning to work together—or do the machines need to figure that out, too?

Overstock.com started out nearly 18 years ago by selling excess inventory online. Since then, the Utah-based company has grown well beyond its early days, now boasting more than 6 million products, ranging from furniture to cars. Underlying the robust commerce activities is a sophisticated AI engine with advanced machine learning to determine the actual life stages of customers. In this keynote interview, James Brohamer, Overstock’s marketing chief of staff, will provide insight into how AI and machine learning can be leveraged and paint a picture of where the world of AI, machine learning and bots is headed.

With 1,500+ members spanning its breadth of titles across devices and platforms, Electronic Arts already operates one of the largest and most sophisticated influencer marketing programs you will find anywhere—and yet, earlier this year, the gaming giant told investors it was planning to shift even more marketing spend away from traditional media towards its influencer program for the foreseeable future. In this keynote interview, Chris Mancil, Global Director, Community & Influencers, will walk us through EA’s “Game Changers” program, revealing how and why it has been such an unmitigated success for the game publisher.

Is influencer ROI an oxymoron or an achievable metric? With influencer payment rates all over the map and all marketers held to greater accountability, how are brands solving for measurability here? We ask several experienced influence campaigner to speak from example and share their recent cases.

Influencers are one-person media companies, but they often need legacy media to lend them greater reach and legitimacy with advertisers. Major media companies like Conde Nast often extend their marketing and sponsored content programs with networks of allied influencers. But how are legacy media working best with these new media pioneers? What is and is not working in the influencer networks? How are old media asserting some scale, uniformity, brand halo and even transparency to the influencer ecosystem?

Some industry watchers think influencer marketing needs a dramatic rethink, away from social media stars that deliver massive reach towards smaller micro-influencers, claiming the Web’s biggest stars are now just as inaccessible to regular people as Hollywood celebrities, making them (ironically) less influential. Is there really an inverse relationship between reach and accessibility when it comes to celebrity in America, and is that something you can measure?