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Interesting 10 AI Business Models – Overview of Artificial Intelligence (AI) And Machine Learning (ML) Market

The impact of artificial intelligence (AI) and machine learning (ML) on our lives and industries is undeniable.  Each day unveils fresh AI advancements that are transforming our interactions with technology and business operations.  Companies are leading the charge in this AI evolution, spearheading innovation and drawing substantial investments through their pioneering progress.

Per market report, the global AI market is on an upward trajectory, with industry analysis companies forecasting revenues exceeding $300 billion by 2024, boasting a five-year compound annual growth rate (CAGR) of 17.1%.  This article delves into ten remarkable AI business models that are reimagining how value is created in the realms of AI and ML.

Real-Time ‘CogniSense’:  In an era of ‘real-time CogniSense,’ AI is seamlessly integrated into our experiences, providing services that transcend the ordinary.  Imagine walking through an airport with indoor navigation, visiting a zero-touch grocery store like Amazon Go, or benefiting from cardless banking and autonomous cars.  All these innovations are now a reality thanks to cognitive edge intelligence platforms, which are offered by public cloud providers, enterprises, and telecom companies.  Imagimob, for instance, has introduced edge AI SaaS to cater to this model.

Immersive AI:  Humanizing experiences with AI are at the forefront of this model.  By 2030, immersive AI has the potential to co-create innovative products and services, doubling cash flow.  Notably, companies like GAFAM, NATU, and BAT have deeply embedded AI into their core business operations.  Google’s Maps and Indoor Navigation, Google Translate, and Tesla’s autonomous cars exemplify the power of immersive AI to enhance user experiences.

Global AI Marketplace:  The global AI marketplace is an innovative business model that serves as a common ground for AI product vendors, AI studios, and sector/service enterprises.  This marketplace operates through a multisided platform, similar to Google Play or Amazon.  SingularityNet, Akira AI, and Bonseyes are examples of such multisided marketplaces.  Amplitude, a B2B platform, enables businesses to tap into AI solutions worldwide, marking a significant step in this direction.

Host, Harbor and Harvest:  This model centers on developing and delivering machine learning models without the need for expensive compute-intensive work.  These zero-capital expenses machine learning models are purpose-built, fast, scalable, and come with predictable run costs.  Leading companies in this space include NVIDIA, Google, Microsoft, Intel, and Qualcomm.  Run:AI has introduced computing as a service (CaaS), while TinyML allows increasingly complex deep learning models to run directly on microcontrollers.

Tools and Platforms:  Adopting AI isn’t just about developing isolated models or algorithms; it involves enterprise-wide collaboration and operationalization.  Tools vendors offer comprehensive no-code and low-code platforms, facilitating the deployment of AI as a service (AIaaS) and machine learning as a service (MLaaS).  The global AIaaS market is expected to reach $6 billion to $7 billion by 2023, with the MLaaS market forecasted to hit $8.48 billion by 2026, growing at a CAGR of 43% over the forecast period (2021-2026).  Key players in this field include Google Cloud ML, Azure ML, AWS ML, IBM Watson, and Alibaba, each offering unique XaaS solutions.  OpenCV (Open-Source Computer Vision Library) accelerates embedded computer vision in commercial products.

‘Glocalize Hiveminds’:  This model focuses on connecting local and global talent in AI, ML, and data science.  The concept of “glocalization” facilitates a connected economy where businesses can collaborate with data scientists and AI/ML specialists worldwide, achieving the best solutions as per their needs.  Platforms like Kaggle, bitgrit, and Click Worker exemplify this global network approach.

Embedded AI:  Pervasive augmented AI is another groundbreaking model, expected to generate $2.9 trillion in business value and 6.2 billion hours of worker productivity globally by 2021, according to Gartner, Inc.  Embedded AI integrates AI computer vision, natural language processing (NLP), segmentation, clustering, recommendation, and prediction algorithms into products and services.  It finds applications in contextual recommendations, financial and insurance products, micro-learning in fintech, edtech, regtech, and medical AI products.  Starbucks’ Deep Brew and its brand personalization engine and optimization of labor allocations serve as examples of AI embedded in business operations.

The world of AI and ML is evolving at an unprecedented pace, reshaping industries and experiences.  These ten AI business models are a testament to the transformative potential of AI and its ability to reimagine value creation.  As enterprises, startups, and innovators continue to leverage these models, they are not only driving economic growth but also making AI an integral part of our daily lives.  The future promises a world where AI is deeply integrated, providing solutions that address unique needs across various sectors.  These models showcase the remarkable potential of AI to enhance efficiency, democratize technology, and foster innovation, setting the stage for a digital landscape where AI and ML are central to progress and success[1].

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