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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 361
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? encouraging and rewarding) a culture of experimentation across the organization. Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens).

Strategy 290
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The path to socially responsible AI

CIO Business Intelligence

Developers need tools and resources to produce quality, safe, and secure products, while enterprises need to trust the tools and resources to not introduce risk to their business. AIs are more than the base knowledge or underlying “data” layer of the LLM or its “experience” layer. Artificial Intelligence

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3 key digital transformation priorities for 2024

CIO Business Intelligence

If CIOs don’t improve conversions from pilot to production, they may find their investors losing patience in the process and culture of experimentation. Third, in the CDO Agenda: 2024: Navigating Data and Generative AI Frontiers , 57% of respondents haven’t changed their data environments to support generative AI.

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10 digital transformation roadblocks — and 5 tips for overcoming them

CIO Business Intelligence

Inadequate data management and governance Data is at the heart of digital transformation, and companies that don’t have adequate data management processes in place are likely to struggle. Ensuring data quality, privacy, and security is essential.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale.

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Success Stories: Applications and Benefits of Knowledge Graphs in Financial Services

Ontotext

Let’s consider an example about risk and opportunity event detection. Case studies The risk and opportunity event detection use case discussed above combines all of Ontotext’s capabilities: storing and managing large amounts of data adding meaning to it (e.g.,, The solution brings many business benefits.