Future of BPM Enterprises in the Age of Generative AI

This report seeks to understand the current state of the semiconductor industry in India, based on factors such as the market size of the industry, the ecosystem of the semiconductor industry, and human resources.

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The onset of the information age transformed the way automation was perceived, particularly in relation to tasks involving data. It changed the way processes were executed, reducing the amount of human intervention required.

Tasks that were previously thought to be too complex or time-consuming for machines also became automated. The modern data platforms were capable of quickly and accurately processing large volumes of multi-formatted data across systems, and detect anomalies, learn patterns, and capture hidden insights.

This suddenly ushered in a huge market expansion for Business Process Management (BPM) solutions, since almost every organisation wanted to avoid the drudgery of doing laborious tasks such as data handling and forecasting, collecting customer feedback, performing repeatable actions, etc.

With generative AI, we are in for another revolution. One of the most significant aspects about it is the ability of large language models to generate output based on natural language prompts. As a result, there has been a rise in models that can follow instructions and provide detailed responses (such as ChatGPT and Bard), create autonomous AI agents for various tasks (such as AgentGPT), generate 3D models (like Point-E and GET3D), simulate voices (such as Vall-E), design, code generation, avatar creation, and countless other possibilities. This will provide applications in virtual assistance, research, data analysis, robotics and autonomous systems, personalised recommendation systems, etc.

With each new dawn bringing in a novel development in the field, businesses are unable to keep track. What they do realise however is the need to deploy these solutions responsibly. There are a number of issues that need to be considered, such as privacy and security of data, accuracy of the model, and bias in the model. Hence, this is not a simple ‘integrate and use’ reality just yet.

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