Enterprise versus Consumer Artificial Intelligence: Differences in Market Dynamics

enterprise-versus-consumer-artificial-intelligence-differences-in-market-dynamics

As artificial intelligence (AI) struggles to meet (often overblown) expectations and deliver reliable outcomes and commercial applications, it is essential for businesses to understand that not all AI is created equal. Not only is AI an umbrella term encompassing six distinct technology categories, market dynamics vary drastically by industry, not to mention regulatory, competitive, ethical, and end-user implications. Tractica’s analysis of revenue associated with more than 215 use cases for AI estimates that the consumer market is the largest in terms of revenue share today, accounting for approximately 29% of the total AI market.

As part of our research exploring this unique market, Tractica identifies a number of distinctions between consumer and enterprise/industrial AI that impact market development. Compared to enterprise/industrial environments, consumer AI is typically characterized by the following market dynamics:

  • Validation for Consumer AI Driven by Consumer Tech Giants: While Big Data and technological advancements have driven development, consumer awareness of AI was sparked by Apple’s Siri, accelerated by Facebook, and further validated by Amazon’s Alexa. Meanwhile, Google announced a fundamental shift from mobile-first to AI-first in 2017.
  • Seamless, Hands-Free, Integrated AI Will Lead Consumer Adoption: As AI infuses new user interfaces and experiences, such as voice, augmented reality (AR), and biometric authentication just to name a few, these will rapidly reset the bar for consumer expectations, faster than more visible applications like social robots or autonomous cars.
  • AI Means User Experience (UX) and Design Are Less about Hardware and More about Software: Form factors and aesthetics may always be important to consumers, but new features, experiences, services, security, integrations, and product innovations will be driven far more by software and machine learning (ML) than upgrades to hardware.
  • Less Information Technology (IT) or Operational Complexity: Consumers do not have hundreds or thousands of different datasets, devices, infrastructure, manufacturers, or regulatory frameworks to navigate. While there are plenty of Big Data applications for consumer personalization, they pale in complexity compared to heavy industrial environments with decades of legacy infrastructure.
  • Edge versus Cloud Architectural Debates Persist, but Offer Distinct Consumer Benefits: While computation, processing, and storage at the edge (device or firmware level) are essential in heavy industrial or remote environments, consumer applications can also benefit from edge processing in areas like privacy, security, energy conservation, and compliance. Certain non-consumer sectors, such as defense or logistics, may drive the chipset market more than consumer.
  • Shorter Product Lifecycles: Consumers typically purchase products at relatively low price points with the expectation they will be replaced within 3 to 5 years, sometimes far less. In industrial environments, millions of dollars of spending on infrastructure are expected to last many years, sometimes decades.
  • Consumers Less Focused on Big Data Analysis: One of the greatest allures of AI for enterprises is the ability to better leverage big, unstructured “dark” data sets and metadata. This is not only absent in the consumer mindset, but rarely understood as a benefit of AI.
  • Convenience over Return on Investment (ROI): Although the majority of consumers are cost-conscious, few make technology purchase decisions with predefined metrics for ROI. Consumers view value more in terms of convenience, ease, security, or entertainment.
  • Different Sensitivities: Consumers have different personal or cultural concerns than businesses. Areas like privacy, health, or surveillance are common concerns for consumers, while enterprises think about these issues primarily through a legal or regulatory perspective.

These broad differences are already impacting the way suppliers are marketing AI technology. While consumers are vaguely familiar with the term “AI,” there are very few products or services that are explicitly marketed as having AI capabilities, or purchased because of them. On the enterprise software side, AI is the hottest new marketing buzzword since “cloud” or “software-as-a-service (SaaS).” Tractica expects that AI will continue to be embedded into existing products and services in both industrial and consumer-facing applications. Of course, as the technology is designed to mimic our own capabilities, AI will be met with distinct barriers and enablers when consumers are in the mix.

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