Specific competency examples: Data set selection and subsetting, regression and classification methodologies, model accuracy estimation, model regularization and stabilization techniques.
5. Leadership and judgment
Machines are becoming the coworkers of the future and handle the day-to-day administrative activities that encompass so much time today. Workers across the enterprise will need to be able to not only embrace a world where machines are making everyday operational decisions, but also where they are expected to exercise judgment on more challenging decisions. A shift like this one requires more focused problem-solving abilities and the skills to construct questions in a way that machines will be able to process and then ultimately create responses that correctly guide decisions.
Specific competency examples: Communication and EQ (spell), judgement-based solutioning, collaboration and cross-functional knowledge
What should leaders do immediately to begin to build the relevant skills and competencies?First, create an internal learning campaign to support AI readiness in the workforce focused on introduction to the technologies and benefits of AI and reducing the fear of machines. Through a series of virtual activities and in-person activities, organizations can generate learning and competency development about the opportunities of artificial intelligence. The level of the activities can vary by workforce and skill level and allow people to progressively increase their skills.
Additionally, explore the workforce dynamics of AI by demonstrating that AI makes people work more efficiently and eliminates tasks, not jobs. There are even opportunities to deploy prototypes or actual demos in the campaign. Finally, conclude with workshops to increase creativity, open-mindedness, flexibility in the way that AI and robotics are leveraged in the workplace – particularly for managers.
The machines are here to stay and are coming to every company and government around the world. Leaders in those organizations will experiment with and adopt the automation and augmentation technologies for a multitude of reasons. The opportunity for IT executives is to transform their workforce and build the competencies required to enable AI in the future. By focusing on effectively managing machines, the data and algorithms that they use, and ultimately on leadership and judgment, technologists can be prepared to drive and optimize AI in their organizations.
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