Key Takeaways

• “We know from our chief data officer research and surveys that data and analytics leaders that are involved in strategy development are able to deliver far more business value than those who aren’t.”
• “Data and analytics leaders struggle to optimize business value, and there’s often a lack of alignment to mission-critical priorities.”
• “You as data and analytics leaders need to function as your organization’s ‘chief value officers.’”
• “The ROAR (Risk, Opportunity, Appetite, and Return) Model gives you a systematic way to score both benefits and risks for each initiative and then think about the combination of those initiatives in terms of an optimal portfolio.”
• “While we have models that can help you think through and prioritize, it’s what goes behind those models that’s important. Thinking trumps doing.”
• “Optimizing value from data and analytics is a continuous process. It’s not just a once a year activity, it’s not just a quarterly activity — you need to think about it as central to everything else that you manage.”

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Key Takeaways

• Myth #1: AI capabilities surpass human capabilities. “The deception is that they allowed the computer top five accuracy metrics. This means that the computer was allowed to make five guesses, and only one of those guesses has to be correct for the system to score ‘correct.’”
• Myth #2: AI is disrupting industries. “When you apply AI it has so many use cases, which all will result in better cost savings, customer satisfaction, downtime reduction, risk reduction and more, but all these things that we’re going to see be better don’t necessarily translate into huge disruption.”
• Myth #3: AI is about intelligence. “The systems that we create don’t understand much, they only react.”
• Myth #4: AI can do anything. “The spectrum of problems that AI can tackle is surprising, but we have to say that AI is a point solution. A solution for fraud detection is not going to drive cars.”
• Myth #5: AI will replace human intelligence. “With our flexibility to learn things, to understand things, and to be super fast and adaptive, it will take a long while before AI will replace human intelligence.”
• Myth #6: AI can learn on its own. “If you look at the whole lifecycle of AI, only the advanced analytics is fully automated.”

Key Takeaways

• “Acquisition, retention and development of AI talent is a challenge for most organizations.”
• “According to Gartner survey research, 1 in 5 organizations believe they have the AI talent they need. The talent gap is narrowing, but procuring the right skills and experience will still be an ongoing challenge.” 
• “Poor hiring strategies, inflated expectations and mismanagement are among the pitfalls of acquiring AI talent.”
• “The four main buckets of roles within an AI group: 1) domain expert, 2) data scientist, 3) developer or ML specialist, 4) data engineers.”
• “Create a culture and team structure that leverages and values supporting AI roles. Expect most data science teams to be small and distributed in the business.”
• “It’s important to be realistic about the turnover that will come with good AI talent. Competitive salaries alleviate this as money is the #1 reason AI talent leaves.”
• “Skill development and career building are vital to AI talent retention and optimal collaboration.”