CIO Business Intelligence
JANUARY 7, 2025
In todays fast-paced digital landscape, the cloud has emerged as a cornerstone of modern business infrastructure, offering unparalleled scalability, agility, and cost-efficiency. This serves as a baseline to drive cloud adoption and getting started with a shifting mindset on how to make money with cloud and not just cost savings alone.
CIO Business Intelligence
NOVEMBER 27, 2024
To fully benefit from AI, organizations must take bold steps to accelerate the time to value for these applications. Operational AI offers organizations significant benefits, including time and cost savings, and critical competitive advantages in today’s business landscape. This is where Operational AI comes into play.
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CIO Business Intelligence
JANUARY 21, 2025
CIOs were given significant budgets to improve productivity, cost savings, and competitive advantages with gen AI. CIOs feeling the pressure will likely seek more pragmatic AI applications, platform simplifications, and risk management practices that have short-term benefits while becoming force multipliers to longer-term financial returns.
datapine
MAY 2, 2023
Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. These applications are designed to benefit logistics and shipping companies alike. Did you know?
Speaker: Catalina Perez Leiva
Does it overly contribute to your costs? Has Excel reached its limits for mathematical optimization? Learn how AIMMS Network Design helps you run various scenarios to make informed decisions, all with personal, high-care implementation support and onboarding. AIMMS Network Design features, functions, and technology: Use cases.
O'Reilly on Data
SEPTEMBER 15, 2020
AI Benefits and Stakeholders. AI is a field where value, in the form of outcomes and their resulting benefits, is created by machines exhibiting the ability to learn and “understand,” and to use the knowledge learned to carry out tasks or achieve goals. AI-generated benefits can be realized by defining and achieving appropriate goals.
AWS Big Data
NOVEMBER 6, 2024
As applications process more and more data over time, customers are looking to reduce the compute costs for their stream processing applications. which enables you to reduce your stream processing cost by up to 33% compared to previous KCL versions. Additionally, we cover additional benefits that KCL 3.0 We then show how KCL 3.0
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Mathematical optimization is a subset of artificial intelligence and a type of prescriptive analytics. It determines ways in which business processes should evolve or be modified, providing implementable solutions with known cost and/or benefit. Want ballpark estimates of value and benefits achieved through optimization.
Speaker: Diane Magers, Founder and Chief Experience Officer at Experience Catalysts
She’ll explore how to pinpoint, measure, and attain benefits such as increased revenue per customer, call reduction, reduced attrition, referral growth, and reduced cost-to-serve through CX improvements, all while actively engaging your customers.
Speaker: Aanand Pandey and Catalina Perez
Need to lower your supply chain costs, speed up delivery times or decrease carbon emissions? Start optimizing your supply chain! Finding optimal locations for plants and other resources. Finding optimal locations for plants and other resources. Modeling carbon cost. Dealing with abrupt changes in demand.
Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network
In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. That’s where data-driven construction comes in.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
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. Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications.
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