CIO Business Intelligence
DECEMBER 9, 2024
This is particularly true with enterprise deployments as the capabilities of existing models, coupled with the complexities of many business workflows, led to slower progress than many expected. Measuring AI ROI As the complexity of deploying AI within the enterprise becomes more apparent in 2025, concerns over ROI will also grow.
O'Reilly on Data
APRIL 19, 2021
During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. In the past year, AI in the enterprise has grown; the sheer number of respondents will tell you that. Yes, enterprise AI has been maturing. But has it matured?
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O'Reilly on Data
NOVEMBER 28, 2023
In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI. AI is the next generation of what we called “data science” a few years back, and data science represented a merger between statistical modeling and software development. What’s the reality?
O'Reilly on Data
MARCH 18, 2020
The update sheds light on what AI adoption looks like in the enterprise— hint: deployments are shifting from prototype to production—the popularity of specific techniques and tools, the challenges experienced by adopters, and so on. The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses.
Speaker: M.K. Palmore, VP Field CSO (Americas), Palo Alto Networks
In most cases, the COVID-19 crisis has sped up the desire to engage in digital transformation for medium-to-large scale enterprises. He will use a combination of industry insights through statistical observations and direct customer feedback to emphasize the importance of adopting new technologies to battle an ever changing threat landscape.
David Menninger's Analyst Perspectives
JANUARY 15, 2025
Enterprises do not operate in a vacuum, and things happening outside an organizations walls directly impact performance. I use the term external data to include any information about the world outside an organization (including economic and market statistics), competitors (such as pricing and locations) and customers.
O'Reilly on Data
JULY 15, 2019
We should clarify that SR 11-7 also covers models that aren’t necessarily based on machine learning: "quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates." Sources of model risk.
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