Generative AI and Business Technology

Following the abrupt uptick of generative AI innovation in the past couple of years, organizations have ramped up research and development efforts to meaningfully explore the value of integrating generative AI into organizational systems, with a particular interest in achieving operational hyper-efficiency. This effort requires a deep understanding of the steps towards operationalizing generative AI—from development to adoption to deployment—involving benefits and risks that must be carefully considered to augment performance and financial gains. 

Insights from the January 31st, 2024 session on Gen AI use cases in the Digital Health sector In the third session of the Generative AI in Healthcare series, speakers Nikhil Bhojwani (Recon Strategy) and Satish Tadikonda (HBS) provided a thought-provoking overview of the current digital health landscape, followed by an engaging panel discussion led by […]

The Digital Value Lab at Digital Data Design Institute, in collaboration with Capgemini Research Institute, unveils a joint research initiative poised at the intersection of innovation and value creation in the burgeoning eco-digital economy. This comprehensive research paper navigates through the transformative impact of generative AI, digital twins, edge computing, immersive technologies, quantum computing, and […]

Since the launch of ChatGPT, GenAI has taken center stage, prompting business leaders to explore its potential for creating value. In a recent stimulating discussion featuring Bratin Saha, Iavor Bojinov, and Edward McFowland III, the conversation delved deep into the radical potential of GenAI and its seismic impact on businesses.

The following insights are derived from a recent Assembly talk featuring Samir Sharma, the CEO of datazuum.  

In this talk, Samir emphasizes the importance of connecting data strategy with business objectives via a detailed discussion of operating models, which are structured frameworks or organizational approaches that govern how an organization manages and utilizes its data analytics and related processes. Samir draws from his expertise in organizational psychology to highlight the challenges organizations face in embedding data and analytics into their core practices and considers the critical role of talent, systems, processes, and upskilling in building a successful operating model.

See table for a summary of the five types of operating models Samir discussed in this talk along with the advantages and challenges that come with adopting each model in a business setting.