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Generative AI’s change management challenge

CIO Business Intelligence

Despite headlines warning that artificial intelligence poses a profound risk to society , workers are curious, optimistic, and confident about the arrival of AI in the enterprise, and becoming more so with time, according to a recent survey by Boston Consulting Group (BCG). A lot has happened since that last survey on attitudes to AI in 2018.

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

I was fortunate to see an early iteration of Pete Skomoroch ’s ML product management presentation in November 2018. Pete Skomoroch, San Francisco, November 2018. Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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Edward Jones’ CIO Frank LaQuinta plays to win

CIO Business Intelligence

He came to Edward Jones in 2016 after a 30-year career in technology on Wall Street and was named chief information officer in 2018. Leveraging the right technical solutions for unique business problems will require experimentation and will result in advances in the technology supported across teams.

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Managing Risk in Data Projects

Dataiku

In 2018, O’Reilly conducted a survey regarding the stage of machine learning adoption in organizations, and among the more than 11,000 respondents, almost half were still in the exploration phase.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. In contrast, in our 2018 report, Asia was behind in mature practices, though it had a markedly higher number of respondents in the “early adopter” or “exploring” stages. Bottlenecks to AI adoption.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Regulations and compliance requirements, especially around pricing, risk selection, etc., It is also important to have a strong test and learn culture to encourage rapid experimentation. Fractal’s 2018 Net Promoter Score is greater than 70. present a significant barrier to adoption of the latest and greatest approaches.

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