埃森哲：AI建立规模(英文版)（22页）.pdf

1、Provocative thinking, transformative insights, tangible outcomes AI: BUILT TO SCALE From experimental to exponential Achieve competitive agility About the authors Ketan leads Strategy data modelers; machine learning, data and AI engineers; visualization experts; data quality, training and communicat

2、ions specialists. Its a lesson Strategic Scalers have learned well. In fact, a full 92% of them leverage multi-disciplinary teams. Embedding them across the organization is not only a powerful signal about the strategic intent of the scaling effort, it also enables faster culture and behavior change

3、s. In contrast, those still in Proof of Concept are more likely to rely on a lone champion within the technology organization to drive AI efforts. The “A” and “I” in team A leading global convenience store chain with $60B in revenue and 16,000 locations sought to gain a more competitive position to

4、stave off more agile rivals. They leveraged data science to price their products more competitively to better match customer demand across global markets. Leveraged machine learning and automation to increase pricing frequency. And established virtual agents to interact with their global category ma

5、nagement teams to drive adoption of the new pricing approach. All of these changes were made possible thanks to multi-disciplinary teams with skills in areas like data engineering, visualization, data quality, and human-centered design. The initiative is expected to deliver an expected US$300 millio

6、n in gross profit uplift annually once fully scaled. of Strategic Scalers leverage multi-disciplinary teams 92% 15AI: BUILT TO SCALE All hands on deck Companies achieving success scaling AI initiatives are more likely than their Proof of Concept counterparts to ensure their employees are prepared fo