Artificial Inteligence Data Generative AI Technology 
Leapfrogging
MARCH 19, 2024
From its inception to the current state, the processes governing the development of new products and services have continuously evolved to incorporate new methodologies and technologies. Traditional Phases and Gates Processes Traditionally, the phases and gates model has been a cornerstone in structuring innovation management.
Harvard Business Review
MARCH 26, 2024
While CDOs and data leaders are excited about generative AI, they have much work to do to get ready for it. Despite excitement, companies have yet to see clear value from generative AI and need to do significant work to prepare their data.
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Tullio Siragusa
APRIL 15, 2024
Embracing the Future: Fractional Executives and Generative AI The concept of fractional executives has emerged as a game-changer for companies of all sizes. The rise of Generative Artificial Intelligence (AI) has further empowered fractional executives, enabling them to produce full-time results in significantly less time.
Harvard Business Review
JUNE 6, 2023
Generative artificial intelligence (AI) has become widely popular, but its adoption by businesses comes with a degree of ethical risk. Organizations must prioritize the responsible use of generative AI by ensuring it is accurate, safe, honest, empowering, and sustainable.
Speaker: Dr. Greg Loughnane and Chris Alexiuk
Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.
Harvard Business Review
NOVEMBER 7, 2023
Generative AI can be a boon for knowledge work, but only if you use it in the right way. New generative AI-enabled tools are rapidly emerging to assist and transform knowledge work in industries ranging from education and finance to law and medicine. However, there is no need to wait for these externally-imposed changes.
Harvard Business Review
NOVEMBER 8, 2023
This calls for a rethink of the incentives and economics surrounding human-generated content. The real bottleneck in generative AI might not be computation capacity or model parameters, but our unique human touch. Yet, we are on the brink of a digital world that is increasingly filled with AI-generated clutter.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.
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