Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Executive Briefing: Organizational design for effective AI

Mariya Yao (Metamaven)
14:35–15:15 Wednesday, 10 October 2018
AI Business Summit
Location: Blenheim Room - Palace Suite
Secondary topics:  AI in the Enterprise

What you'll learn

  • Learn how to align AI initiatives with business and product goals and encourage and facilitate interdisciplinary collaboration
  • Explore examples of useful data versus less useful data for the purposes of training performant ML models, common pitfalls when it comes to workforce retraining (and how to plan for and overcome them), cultural practices that support open, experimental mindsets and methodologies, and different organizational models to consider when managing both human and AI employees


Executives are being asked to "innovate with AI,” but the barriers to successful adoption for most enterprises are organizational, not technical. Mariya Yao explains why effective application of AI requires extended interdisciplinary coordination between executive and functional teams, investments in retraining your workforce, and the cultivation of an open, experimental, data-driven culture.

Whether an organization chooses to build or buy AI, there are five critical areas that are nearly always underinvested in:

  1. A goal-driven AI strategy: AI is not a magical technology that, when plugged in, automatically yields enormous business benefit. In many cases, poorly designed automation hurts your business and workforce. Executives must carefully assess where (and where not) to apply AI in their operations.
  2. Business-driven data collection: Having the right data is more important than collecting high volumes of data, but data in most enterprises is collected without the development of machine learning models in mind. Overcoming this requires that the business and product owners collaborate closely with AI technologists and data stewards.
  3. Technical training for frontline employees and middle managers: Executives are often well educated on emerging technologies, but enterprises inevitably stumble when the employees tasked with adopting new software and methods are not sufficiently supported.
  4. A culture of data-driven, probabilistic decision making: Business leadership historically relied on instinct and vision, but in our increasingly complex world, executives need to make more nuanced decisions that balance analytics with experience.
  5. Organizational design: Roles, responsibilities, and workflows change dramatically when automation assumes increasingly more human tasks. Few organizations have spent the requisite time rethinking how to design, operate, and scale the automated enterprise.
Photo of Mariya Yao

Mariya Yao


Mariya Yao is chief technology and product officer at Metamaven, a company that intelligently automates revenue growth for global companies like Paypal, LinkedIn, L’Oréal, LVMH, and WPP. She’s also editor-in-chief of TOPBOTS, the largest publication and community for business leaders applying AI to their enterprises, a Forbes writer covering the interplay of human and machine intelligence, and coauthor of Applied AI: A Handbook for Business Leaders, which she launched onstage at CES 2018.