There is no “one size fits all” generative artificial intelligence strategy. Instead, successful innovations will build on existing supplier strengths, competencies and business models.
Microsoft's core business arguably includes productivity software suites like Office 365. Copilot helps by suggesting completions, refactoring code and identifying potential errors when customers use the tools.
Apple’s business model, on the other hand, is driven by smartphones. So Apple is using AI for smartphone functions such as facial recognition (Face ID), voice assistant (Siri), and image optimization that are relevant features for people using iPhones.
Google's core competency is information retrieval through search, which creates the advertising revenue streams that dominate Alphabet’s business models. So Google will use AI for natural language processing and machine learning to understand search queries better, rank results more effectively, and personalize search experiences.
Meta makes its money from social media platforms. So Meta is likely to prioritize use of AI for content moderation, personalized recommendations, and targeted advertising.
That focus on enhancing current products and value means Apple is focused on “on-device” processing to a greater extent than Microsoft or Google (with the exception of Google Pixel devices). Google search and Copilot can generally rely on remote processing.
Amazon’s revenue reliance on e-commerce means AI will be deployed to improve product recommendations.
But Microsoft, Google and Amazon will rely on AI to support cloud computing as a service operations (AWS, Google Cloud, Azure), in large part to support model hosting and training “as a service.”
Generative AI, for example, can be used by many firms, for many purposes. By firms other than Google, Apple, Microsoft and Meta. But the actual degree of value will be tested over time.
Though GenAI could well be something closer to a game changer for suppliers of GenAI models and inferences, most firms and industries trying to use those models and inferences face more-difficult challenges.
As helpful as generative AI can be, it is not so clear that most implementations by most end user entities are going to produce measurable financial outcomes, at first. And where that happens, it might well be the case that cost reductions are the metrics, rather than revenue enhancements.
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