Cloudera is launching and expanding partnerships to create a new enterprise artificial intelligence “AI” ecosystem. Businesses increasingly recognize AI solutions as critical differentiators in competitive markets and are ready to invest heavily to streamline their operations, improve customer experiences, and boost top-line growth.
Customer service is an art—and a science. It isn’t just a transactional function. It's also a relationship building activity that’s deeply tied to physiological responses in our brains. And the stakes are high: these interactions shape the neural architecture of customer loyalty. So can you use AI for customer service? Let’s explore that question.
The rise of generative AI (gen AI) is inspiring organizations to envision a future in which AI is integrated into all aspects of their operations for a more human, personalized and efficient customer experience. However, getting the required compute infrastructure into place, particularly GPUs for large language models (LLMs), is a real challenge. Accessing the necessary resources from cloud providers demands careful planning and up to month-long wait times due to the high demand for GPUs.
Adopting and deploying Generative AI within your organization is pivotal to driving innovation and outsmarting the competition while at the same time, creating efficiency, productivity, and sustainable growth. Acknowledging that AI adoption is not a one-size-fits-all process, each organization will have its unique set of use cases, challenges, objectives, and resources.
Without a doubt, 2023 has shaped up to be generative AI’s breakout year. Less than 12 months after the introduction of generative AI large language models such as ChatGPT and PaLM, image generators like Dall-E, Midjourney, and Stable Diffusion, and code generation tools like OpenAI Codex and GitHub CoPilot, organizations across every industry, including government, are beginning to leverage generative AI regularly to increase creativity and productivity.
Businesses are starting to use generative AI more and more as a result of the fast development of AI. Automating software testing is only one example of how generative AI is improving efficiency and reducing costs. Here I'll go over several ways generative AI may speed up the software development process and automate software testing.
The excitement (and drama) around AI continues to escalate. Why? Because the stakes are high. The race for competitive advantage by applying AI to new use cases is on! The launch of generative AI last year added fuel to the fire, and for good reason. Whereas the existing portfolio of AI tools had targeted the more technically minded like data scientists and engineers, new tools like ChatGPT handed the keys to the kingdom to anyone who could type a question.
Not long after graduating college, in the late 2010s, I had a data analysis job that scarred me for life. It involved reading through hundreds of documents—some of them handwritten—and meticulously, painstakingly entering data into spreadsheets. Who among us hasn’t been scarred by a similar experience? I remember thinking, as I spent mindless hours copy-pasting rows of text from one screen to another, that there had to be a better way. Mercifully, there is.
