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.
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.
Digital government and digital services are the top priorities for state governments, according to the recently published National Association of State Chief Information Officers (NASCIO) Top 10 Policy and Technology Priorities for 2024. Tied for first place is cybersecurity and risk management, followed by legacy application modernization.
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 conversation around data fetching and API management is increasingly dominated by two major players: GraphQL and REST. These technologies, while serving the same purpose of facilitating communication between client and server, offer markedly different approaches and philosophies. GraphQL, emerging as a powerful alternative to the traditional RESTful architecture, brings a new dimension to data retrieval efficiency and flexibility.
