ServiceNow is focused on making the world work better for everyone. More than 7,700 customers rely on ServiceNow’s platform and solutions to optimize processes, break down silos and drive business value. Achieving 20% year-over-year growth with a 98% renewal rate (as of Q1 2023) requires a data-driven understanding of the customer journey.
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.
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.
The best marketing is truly data-driven, creating powerful product promotions and offers through an understanding of customer needs and preferences. But for many organizations, building this understanding is more akin to solving an ever-growing jigsaw puzzle (with no easy edge pieces!) than reading data insights from a beautiful dashboard.
When businesses share sensitive first-party data with outside partners or customers, they must do so in a way that meets strict governance requirements around security and privacy. Data clean rooms have emerged as the technology to meet this need, enabling interoperability where multiple parties can collaborate on and analyze sensitive data in a governed way without exposing direct access to the underlying data and business logic.
Historically, only a few AI experts within an organization could develop insights using machine learning (ML) and predictive analytics. Yet in this new wave of AI, democratizing ML to more data teams is crucial—and for Snowflake SQL users, it’s now a reality.
BigQuery Omni’s new cross-cloud materialized views lets you perform cross-cloud analytics.
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.
Our recently released predictions report includes a number of important considerations about the likely trajectory of cybercrime in the coming years, and the strategies and tactics that will evolve in response. Every year, the story is “Attackers are getting more sophisticated, and defenders have to keep up.” As we enter a new era of advanced AI technology, we identify some surprising wrinkles to that perennial trend.
