Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Smartening up with artificial intelligence

Apr 25, 2017 | 3828 Views

Artificial intelligence (AI) is finally bringing a multitude of capabilities to machines that were long thought to belong exclusively to the human realm, such as processing natural language or visual information. In this report, we explain how and where AI could affect the German industrial sector by exploring several questions: Which subindustries are most strongly affected by the automation potential of AI? What are the most promising use cases? What are pragmatic recommendations for managers of industrial players planning to harness the power of AI?

Highly developed economies like Germany, with a high GDP per capita and challenges such as a quickly aging population, will increasingly need to rely on automation based on AI to achieve GDP targets. About one-third of Germany's GDP aspiration for 2030 depends on productivity gains. Automation fueled by AI is one of the most significant sources of productivity. By becoming one of the earliest adopters of AI, Germany could even exceed its 2030 GDP target by 4 percent. However, if the country adopts AI more slowly-and productivity is not increased by any other means-it could lag behind its 2030 GDP target by up to one-third. AI is expected to lift performance across all industries and especially in those with a high share of predictable tasks such as Germany's industrial sector. AI-enabled work could raise productivity in Germany by 0.8 to 1.4 percent annually.

We selected eight use cases covering three essential business areas (products and services, manufacturing operations, and business processes) to highlight AI's great potential in the industrial sector. Use cases include topics such as autonomous vehicles, predictive maintenance, collaborative robotics, and supply-chain management. Five pragmatic recommendations help with getting started on the journey toward a fully AI-enabled organization. Read More


Source: Mckinsey