AI adoption rates are on the rise, and 2020 has become the year that AI becomes business-as-usual rather than a science experiment. But while the adoption of AI has become widespread, not all AI projects are living up to the hype. “Despite the promise of AI, many organizations’ efforts fall short…only 8% of firms engage in core practices that support widespread adoption,” says McKinsey. Great software such as automated machine learning, can help lower the barriers to success, but businesses also need to learn from the mistakes of others.
Join us in this webinar, where we discuss 6 case studies in AI failure, and discover how you can avoid becoming yet another data science failure statistic.
You will learn how to avoid the following traps:
- Hype surrounding complex new algorithms
- Perverse incentives
- Human error
- Obsolescence in a rapidly changing world
- Ignoring business rules and human expertise
- Too much or too little transparency and explainability
SPEAKERS
Colin Priest VP, AI Strategy DataRobot
DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!
Omair Tariq
Data Analyst
Symphony Post Acute Network
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
Oliver Rees
General Manager
Torque Data at Virgin Australia
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
Akshay Tandon
VP of Strategy Analytics
LendingTree
DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
Deena Narayanaswamy
Head of Data Insights
Avant
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
Evariant