Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Artificial intelligence algorithms are designed to make decisions, often using real-time data. AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.

Disruption Breakdown

Success Factors

Cost Efficiency

Based on the cost of production and speciality needed in machinery and job roles.

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Ability to Mass Produce

Based on ease of access to all components and level of personalisation required.

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Speed to Produce

Time taken from manufacturing start point to consumer ready.

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Concept Realisation

Based on proven case studies of the technologies and concepts used.

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Consumer Needs Met

Based on consumer interest, needs and demand.

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Disruption Factors

Ecosystem Potential

Potential to integrate into existing consumer ecosystems (digital or lifestyle) and potential to create new ecosystems.

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Potential to Change Consumer Behavior

Potential to change behavior of the consumer if delivered successfully. Based on creating new interactivity, delivery systems, a unique service or through new knowledge delivered.

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Potential to Change Industry Behavior

Potential to change industry behavior if delivered successfully. Based on creating new technology, leveragable delivery systems or through new knowledge gained.

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Uniqueness of IP

Based on amount of existing consumer products and services leveraging this idea and examples of successful case studies.

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New Knowledge Gained

New technical, development, manufacturing or consumer knowledge gained if delivered successfully.

Artificial Intelligence

Gallery

Examples and use cases

Trend Metrics

Trend Timeline (Last 4 weeks)