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In recent times, the supply chain industry has undergone a remarkable transformation, driven by the relentless march of technology. Among the many technological advancements, two have stood out prominently: Digital Transformation and the emergence of Generative AI (Gen AI). The fusion of these two forces has the potential to revolutionize supply chain management, optimizing operations, enhancing efficiency, and fundamentally reshaping business models. In this article, we'll dive deep into the profound impact of Gen AI on the supply chain industry.
If you are following the trends in the realm of Gen AI, you would notice that every day, a ton of LLMs and tools are being introduced in this space. The opportunity unleashed by the Gen AI space is promising and disruptive but the initial models are confined to the space of producing text, images, code, videos, speech and music with some investment also made in the game and 3D objects.
With the rapid growth and transformation in technology that the modern world is experiencing, every second enormous amounts of data are generated for both professional and personal purposes. We check our emails, enter passcodes to unlock our devices or make a simple phone call, and in these everyday routine actions, significant volumes of data are transmitted. When shifting to the business side, data is vital for the day-to-day operations for all kinds of businesses.
Our client, a leading biotechnology company, was facing operational inefficiencies due to siloed teams working independently with little collaboration. The organization lacked well-defined operational processes, and the instability with its existing data platform resulted in high platform costs and usage inefficiency. The client needed a strong data engineering unit to standardize and define its processes, team structure, tools, and product roadmap, which would enable them to improve business value, performance, and efficiency.
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Due to the complexity of data sources and the vast size of the client’s data, they faced significant challenges in consolidating and harmonizing customer data into a unified and trusted source. The client also struggled with analyzing and understanding customer journeys and identifying opportunities for upselling and cross-selling insurance products to their customers.
In modern data management, data observability is a critical component. It involves continuous monitoring and analysis of data pipelines and workflows to ensure that data is accurate, reliable, and meets quality standards. Data observability aims to make sure that the data being used by organizations is trustworthy and can support strategic, data-driven decisions. Modern companies need to monitor data across many tools and applications. Data observability can help companies understand, monitor, and manage their data across the full technology stack and data ecosystem.
Dr. Tariq Khan is an enterprising AI leader with multidisciplinary subject matter expertise. Dr. Tariq has used AI to catalyze the transformation of processes for Fortune 500 companies; achieving returns on investment totaling over one billion dollars. He has proven experience of leading AI transformations, with one standout being the establishment of a 5-year AI COE for a Tier 1 Life Sciences company headquartered in New York.