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Different Ways to Use AI/ML in Operations

All our customers have tremendous interest in AI and ML – artificial intelligence and machine learning – across every industry and in companies of every size. Yet the very power of the phrase, and the breadth of interest, reveal one of its challenges: The term is too vague to be really useful for planning purposes. Is AI the same as ML? Most AI techniques are also machine learning techniques but many experts still differentiate between ML and AI – often based on the details of the approach taken. 

To try and cut through some of this, we use this graphic to show the different ways AI and ML can play a role in operations. As always, we take a DecisionsFirst approach to this – our objective is to develop a decision service that automates all of part of your decision making accurately, consistently and legally while empowering the business to evolve it and manage it. In that context there are three roles for AI/ML: 

Interface AI – the use of ML/AI to improve the interaction of the computer and its decision service with the outside (real) world. 

Research AI – investigating data (structured or unstructured) to see what can be learned about the business or situation that generated that data. 

Operational AI – ML and AI components that can be deployed into a decision service to make it more accurate, better able to consume and understand certain kinds of data and to replace or augment human decision-making. 

I’ll talk more about each one in a subsequent post, starting with Interface AI. 


Amit Rawool

Amit Rawool

AI/ML developer

Amit Rawool

Amit Rawool is a seasoned Python Developer, with nearly a decade of experience in the field of machine learning, computer vision, natural language processing (NLP), reinforcement learning, and large language models (LLMs). His technical prowess is complemented by his ability to develop scalable applications using modern technologies like FastAPI, React, and Next.js.

Amit’s academic journey includes a Master of Technology from the Indian Institute of Technology Bombay and a Machine Learning Certification from Stanford University. Throughout his career, Amit has held various impactful roles, including Consultant Machine Learning Engineer, Research Engineer, and Lead Engineer, across prestigious organizations such as General Electric, General Motors and Sandvik Asia. He has led and contributed to several high-profile projects, including a real-time data processing system, and an AI-based product recommendation system.

Amit’s work is characterized by his innovative approach to solving complex problems and his commitment to integrating cutting-edge technology solutions. His expertise extends across a broad spectrum of software skills, including Python, JavaScript, PyTorch, TensorFlow, and OpenAI GPT models.