Put AI to Work
April 15-18, 2019
New York, NY

Bringing AI into the enterprise

Kristian Hammond (Northwestern Computer Science)
9:00am5:15pm Tuesday, April 16, 2019
AI Business Summit
Location: Sutton North
Secondary topics:  AI in the Enterprise
Average rating: ****.
(4.50, 8 ratings)

Who is this presentation for?

  • Managers, vice presidents, and C-level decision makers

Level

Beginner

Prerequisite knowledge

  • An understanding of the business problems that you are interested in solving

What you'll learn

  • Learn a practical framework for bringing AI into your company

Description

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. On the plus side, the technologies we thought were decades away seem to be showing up at our doorstep with increasing frequency. However, little effort has been made to clearly explain their value and genuine business utility.

Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making, focusing on how these technologies can be used, the requirements for doing so, and the expectations for their effectiveness. Rather than focusing on the technologies alone, this framework ensures that as you build, evaluate, and compare different systems, you’ll understand and be able to articulate how they work and the resulting impact.

Outline:

  • An overview of the current AI ecosystem, focused on the reason why the intelligent technologies of today are different than what we have seen in the past
  • A deep dive into specific technologies from a functional perspective, which unpacks the requirements for each and how to evaluate opportunities
  • A look at how enterprises can develop a value-driven cognitive computing strategy, detailing how organizations can grow their skills and organization in parallel with the introduction of the technologies they are deploying and how to ramp into the solutions by defining a growth path that starts with definable tasks, well-understood technologies, and clear ROI
  • A process that results in the data awareness and skills needed to bring even the most sophisticated and complex technologies into play
Photo of Kristian Hammond

Kristian Hammond

Northwestern Computer Science

Kristian Hammond is Narrative Science’s chief scientist and a professor of computer science and journalism at Northwestern University. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.

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Comments

Picture of Kristian Hammond
Kristian Hammond | PROFESSOR
04/23/2019 6:58am EDT

For those that want to talk about specific problems, you can reach out to me via LinkedIn.
Cheers,
Kris

Picture of Kristian Hammond
Kristian Hammond | PROFESSOR
04/23/2019 6:02am EDT

The link should bring you to a download site. I don’t seem to have problems with it. Might there be a firewall issue?

Kris

Picture of Olivier  Dancot
Olivier Dancot | VICE PRESIDENT DATA
04/22/2019 11:23pm EDT

Hi Kris,
The downloading link doesn’t work, could you please give us another one to download your slide deck ?
Best regard
Olivier

Chris Osborne | VICE PRESIDENT, PRODUCT AND INSIGHTS
04/16/2019 12:53pm EDT

Hi Kris,
Thanks for a great session, lots of great content. Where are you posting the presentations?

And question on the insurance claim work you were walking us through, is this something you have worked on? I run product and analytics at an Incentive company and we process close to $3B in claims annually for major manufacturers and I am replatforming our claims process and am very interested in the claims work you were referring to.
Thanks ,Chris

Imaya Kulothungan | SR. MANAGER
04/16/2019 12:41pm EDT

Would have loved to have the presentation available during the session to have embedded notes in them.. would appreciate if you could punish the content.. found it very valuable.

Picture of Kristian Hammond
Kristian Hammond | PROFESSOR
02/12/2019 8:35am EST

Søren,

The tutorial is designed to give an overview of the functional role of the different AI technologies that can be brought into the enterprise and how to look at your problems from the point of view of which technologies can be useful in solving them. It breaks down the questions you need to ask into those related to data, task, transparency and reliability. I use examples from industry sourcing them from my own experience and those of others that I have worked with.

In general, the class is aimed at giving participants skills at evaluating technologies and asking the right questions before embarking on a project.

Cheers,
Kris

Søren Bækgaard Hansen | DIRECTOR, ENTERPRISE ARCHITECTURE AND STRATEGY
02/05/2019 11:13pm EST

Hi Kristian,
The topic certainly sounds relevant. My finding is that often I/we lack the idea of which problem to bring, when reading about the prerequisite. (If I don’t know if the future brings me a car or a phone I am not sure if my challenge to bring is my transportation or my communication).

So to what extend do you bring use case ideas to the plate during this tutorial? Has that been part of your research? I would love to finally map outside-in inspiration to my own pain-points. Let me give an example. We are a FMCG company of 19,000 employees – the 5th largest dairy in the world. We operate a big shared service center for Accounts payable, Accounts receiveable etc. We have deployed hundreds of RPA robots to remove parts of the manual work. But have others managed to look entirely different at their finance shared service operations and radically transformed the experience using AI?
The same curiosity for how AI has been applied inside the organisation I could mention for e.g. contract management in relation to procurement, for IT service desk operations (AI in identifying most likely answer to a written/spoken issue, like what DigitalGenius claim), or other high volume transactional type work that involves some degree of probability certainty in finding the most likely answer.

So, do you cover a dense list of examples of what others have done as part of the session?

Best regards
Soren B. Hansen