What are the Benefits and Limitations of Augmented Intelligence?
Last updated: January 11, 2024 Read in fullscreen view
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And while some AI technology is intended to operate autonomously, one of the most useful types of AI — augmented intelligence (also known as Intelligence Augmention or Intelligence Amplification, or IA) — uses machine learning and predictive analytics of data sets not to replace human intelligence, but to enhance it.
Augmented Intelligence is a synergy between man and machine. The core idea behind augmented intelligence is that whatever the AI algorithms are doing on their own, there has to be some sort of human intervention that can actually monitor and optimize those algorithms with continuous feedback.
Artificial intelligence is the idea of systems using data and predefined rules to operate autonomously. While true autonomous technology doesn't exist today, Augmented Intelligence technology is developing rapidly and changing the world.
What Are the Benefits of Augmented Intelligence?
Augmented intelligence sets the stage for a range of benefits that ultimately empower human action and decision-making, such as:
Increased Customer Satisfaction
There are many ways businesses use augmented intelligence to boost customer satisfaction. Common examples include:
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Reviewing past purchases and suggesting items by preferences or shopping behavior
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Using chatbots to answer common queries and resolve issues
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Summarizing customer feedback data to identify areas for improvement
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Using analytics to predict future trends or detecting potential problems to meet customer expectations
Enhanced Decision Making
A.I. generated insights are based on data and can help businesses can gain a fuller understanding of their situation to:
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Forecast future profit
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Reduce market uncertainty
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Supplement decision making
Better Planning and Efficiency
A.I. analytics are data-driven to provide a deeper understanding of:
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Business operations
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Customer behaviors
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Market trends
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The competitive landscape
These insights can enhance forecasting and demand planning for teams. Using our manufacturing example, a plant can use these analytics to:
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Optimize inventory levels
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Track production capacity
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Monitor logistics schedules
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Identify production bottlenecks
What Are the Limitations of Augmented Intelligence?
Dependence on Data Quality
Augmented intelligence relies on quality data to function. Incorrect, biased, or incomplete data can lead to misleading insights and recommendations.
Lack of Contextual Understanding
Unlike humans, A.I. cannot understand nuances, cultural references, or subtle cues evident to people. The majority of today's A.I. tools lack contextual understanding, which can lead to errors or incorrect outputs.
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