As AI technologies become more advanced, previously cutting-edge -- but generic -- AI models are becoming commonplace, such as Google Cloud's Vision AI or Amazon Rekognition. While effective in some use cases, these solutions do not suit industry-specific needs right out of the box. Organizations that seek the most accurate results from their AI projects will simply have to turn to industry-specific models. There are a few ways that companies can generate industry-specific results. One would be to adopt a hybrid approach -- taking an open-source generic AI model and training it further to align with the business's specific needs.
India is one of the leading markets for analytics and data science services, and various data science vendors have seen significant growth over the last few years, as organisations across verticals are investing billions of dollars in data science and analytics to optimise their processes. In fact, despite the pandemic and the resulting quarter-long slowdown, the data science functions across organisations have not been significantly impacted, suggesting that data science and analytics are a mainstay of business processes and value generation. The pre-COVID numbers suggest that as of March 2020, the analytics function in India earned consolidated revenues of $35.9 Bn, a 19.5% growth in revenue over last year. In the post-COVID world, companies are veering towards digital transformation to ensure business continuity – and the analytics and data science functions are playing a crucial role in this journey. As companies are looking to establish AI and Data Science capabilities, the market is further maturing, driving the need for data science vendors/service providers to facilitate the booming market. Over the last few years, multiple vendors with unique capabilities in data science, have not just grown from small operations but also matured in terms of capabilities.
The artificial intelligence space is increasingly competitive with new AI companies and products being developed every day. Every industry is getting more and more crowded with products from startups and from established companies. Although there are unique considerations for adopting AI in the enterprise, the fundamentals of going to market with an AI product are the same no matter if a company is a large, established corporation or an AI startup. Both will face similar challenges in developing a winning product and marketing it toward an audience that wants to buy it, be they existing customers at a bank or large banks themselves. Emerj works with AI startups and large companies that want to be known for AI in order to create winning sales collateral that appeals to their buyers and generates more pipeline by reaching our audience of dedicated AI-focused business leaders.
Artificial intelligence (AI) is poised to redefine how businesses work. Already it is unleashing the power of data across a range of crucial functions, such as customer service, marketing, training, pricing, security, and operations. To remain competitive, firms in nearly every industry will need to adopt AI and the agile development approaches that enable building it efficiently to keep pace with existing peers and digitally native market entrants. But they must do so while managing the new and varied risks posed by AI and its rapid development. The reports of AI models gone awry due to the COVID-19 crisis have only served as a reminder that using AI can create significant risks.
An IoT platform is a form of middleware that sits between the layers of IoT devices and IoT gateways (and thus data) on one hand and applications, which it enables to build, on the other (hence why IoT platforms are also called Application Enablement Platforms or AEPs). The reality is a bit more complex as we'll see after an overview of the essential capabilities of all IoT platforms and what you, as a potential buyer, should know about IoT platform market evolutions and selection criteria to pick the IoT platform that fits your needs. Let's already say the IoT platform has become an important part of IoT deployments and that there are several types and vendors with their own focus and go-to-market strategies. Moreover, the reality and market of IoT platforms is complex as IoT projects, applications and solutions come with different architectures, ways of connecting and managing devices (IoT device management), possibilities to manage and analyze data, capabilities to build applications and options to leverage IoT in a meaningful way for any given IoT use case in any given context: consumer applications, enterprise IoT applications and Industrial IoT or Industry 4.0. As, in the end, IoT is part of an integrated approach to leverage data from devices, assets and environmental/contextual parameters, in combination with other data, in a meaningful and valuable way, more technologies are added in the scope of IoT deployments, depending on the use cases and industries.