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Building the AI-Powered Organization (Making the shift (One of the biggest…

- - - - Some of the pilots manage to eke out small gains in pockets of organizations.
  - - - Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues.
  - - - But for this approach to work, people at all levels have to trust the algorithms’ suggestions and feel empowered to make decisions—and that means abandoning the traditional top-down approach.
        
        If employees have to consult a higher-up before taking action, that will inhibit the use of AI.
  - - - On the first iteration, AI applications rarely have all their desired functionality. A test-and-learn mentality will reframe mistakes as a source of discoveries, reducing the fear of failure.
        
        Getting early user feedback and incorporating it into the next version will allow firms to correct minor issues before they become costly problems.
        
        Development will speed up, enabling small AI teams to create minimum viable products in a matter of weeks rather than months.
- - - - For example, if a company has relationship managers who pride themselves on being attuned to customer needs, they may reject the notion that a machine could have better ideas about what customers want and ignore an AI tool’s tailored product recommendations.
  - - - The company invested simultaneously in AI model development and in helping the center’s employees transition to the new approach.
        
        Instead of just reacting to calls canceling service, they would proactively reach out to customers at risk of defection, giving them AI-generated recommendations on new offers they’d be likely to accept.
  - - - This is particularly critical with AI projects, because fear that AI will take away jobs increases employees’ resistance to it.
    - - Workers must understand why AI is important to the business and how they’ll fit into a new, AI-oriented culture.
        
        In particular, they need reassurance that AI will enhance rather than diminish or even eliminate their roles.
  - - - Automated processes that don’t need human intervention, such as AI-assisted fraud detection, can deliver a return in months, while projects that require human involvement, such as AI-supported customer service, are likely to pay off over a longer period.
        
        Prioritization should be based on a long-term (typically three-year) view and take into consideration how several initiatives with different time lines could be combined to maximize value.
- - - - Most academies initially hire external faculty to write the curricula and deliver training, but they also usually put in place processes to build in-house capabilities.
  - - - Here the focus is on constantly sharpening the hard and soft skills of data scientists, engineers, architects, and other employees who are responsible for data analytics, data governance, and building the AI solutions.
    - - Analytics translators often come from the business staff and need fundamental technical training—for instance, in how to apply analytical approaches to business problems and develop AI use cases.
        
        Their instruction may include online tutorials, hands-on experience shadowing veteran translators, and a final “exam” in which they must successfully implement an AI initiative
    - - Most academies strive to give senior executives and business-unit leaders a high-level understanding of how AI works and ways to identify and prioritize AI opportunities.
        
        They also provide discussions of the impact on workers’ roles, barriers to adoption, and talent development, and offer guidance on instilling the underlying cultural changes required.
    - - Frontline workers may need only a general introduction to new AI tools, followed by on-the-job training and coaching in how to use them.
        
        Strategic decision makers, such as marketers and finance staff, may require higher-level training sessions that incorporate real business scenarios in which new tools improve decisions about, say, product launches.