New year, better AI practices
In this newsletter we ask: Looking ahead in 2024, how can your organization better utilize AI?
Scroll to discover:
Putting AI To Work For Customer Service
In this episode of AI Academy, hear from Manish Goyal , VP of Global AI and Analytics at IBM. You'll learn about AI-powered omni-channel experiences, scaling the efforts of your field service agents, and how to augment your contact center operations with AI.
IBM Global AI Adoption Index 2023
Hype around #AI grew exponentially during 2023, but how are enterprises adopting the technology?
Our fourth annual IBM Global AI Adoption Index found that early adopters are already experiencing the benefits of AI, yet ongoing challenges for AI adoption in enterprises remain. Let's dig into the details:
?? About 42% of enterprises are actively deploying AI
?? 59% of companies exploring or deploying AI are accelerating their investments
?? Limited expertise, data complexity, and ethical concerns remain top barriers
领英推荐
??? More accessible AI tools are the top factors driving AI adoption
Rethink Customer Service With AI
What if generative AI could help you achieve your customer service aspirations? It can scale the best aspects of customer service— responsiveness, empathy, knowledge and personalization— from one agent to hundreds. With the ability to understand complex inquiries and generate more conversational responses, it enables frictionless self-service experiences and smarter contact centers.
Modernize Your Workforce With Generative AI Skills
A massive workforce and skills transformation is underway across many organizations. Recent innovations and the emergence of generative AI reveal that the enterprise of tomorrow may not be able to run with traditional skills. In this webinar, understand the role of HR in preparing the workforce in the age of generative AI.
Taking On AI’s Hallucination Problem
Jaxon AI is taking a significant leap forward with the introduction of Domain-Specific AI Language (DSAIL), which represents a new approach to AI development, targeting on of the most challenging aspects of AI technology: hallucinations in large language models (LLMs). With the help of watsonx, Jaxon’s developer-friendly system seeks to help reduce hallucination related inaccuracies.
none at none
9 个月Sounds like IMB is doing good AI
Data Science | Machine Learning | Data Analysis | Python | Mysql | Pytorch | Completed 100 Days Data Science Challenge—scroll through my posts to check out my journey | Started WinterArc Data Science Challenge.
9 个月Well said
MAM, MSChE, Outcomes and Performance Measurement, Energy, Oil, Gas, NGL, Chemical, Petrochemical, Fertilizer,Real Estate
9 个月Thanks for sharing. IA Envisioning a Bright Future. IA Challenges without paradigm. Modern AI algorithms, such as deep learning and reinforcement learning, leverage vast amounts of data to learn and make predictions. Additionally, advancements in computing power, especially with the rise of cloud infrastructure, have enabled complex AI models to be trained and deployed at Scale. As AI continues to evolve, researchers and developers must stay abreast of these paradigm shifts to drive future progress. Changing the Paradigm in AI Implementation. What it takes to succeed with AI. How do we define success? How will I get there? Which talent and what technology will get me there? How much will this cost, and what will be the return?