Businesses are in a persistent productivity slump that could last through 2030, according to a 2023 World Bank study. The tech boom that powered innovation and growth over the last three decades is fading. Many companies are counting on artificial intelligence (AI) to boost operational efficiency and counteract these alarming trends.
2023 was a breakout year for artificial intelligence. It dominated news headlines as well as LinkedIn feeds. But its impact goes beyond the professional—I often overhear conversations at coffee shops about AI from people who aren’t knee-deep in the field. Whoever you are, AI is likely having a transformative impact on your life.
Traditional AI models typically specialize in processing a specific type of user prompt, whether image- or text-based. However, a paradigm shift is underway with the emergence of a new generation of AI models known as "multimodal" systems. Unlike their predecessors, these advanced models can process a diverse range of inputs seamlessly. They can adeptly handle various media types, such as text, images, audio, video, and even code.
The business landscape is undergoing radical change across industries, driven by artificial intelligence (AI) and automation. This article will differentiate AI and automation, debunk misconceptions, and highlight what business leaders need to know to navigate the challenges of integrating AI and automation across the value chain. So what’s the difference between AI and automation? AI and automation have distinct purposes.
AI is a groundbreaking technology that is ready to modernize the way federal government agencies operate. By automating tasks and optimizing workflows, artificial intelligence (AI) promises to enhance efficiency, minimize errors, and boost productivity without adding resources.
Ready to implement generative AI in your business processes? Starting with the right generative AI use cases is key to your success. You’ll want to find areas where you can achieve quick wins as you grow toward your larger AI vision. In this article, we’ll highlight five use cases where you can incorporate generative AI for increased process efficiency.
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 US government software modernization policy encourages federal agencies, including the Department of Defense (DoD), to leverage investments already made in application development and authorizations to operate (ATOs). The goal of repurposing existing, working applications is to enable agencies to reduce development time and costs by leveraging software that has been purchased and implemented by another team.
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.
So you’re still processing invoices manually, huh? That's no way to live. If you’ve come here in search of a better way, you won’t be disappointed. Maybe you’ve heard terms like OCR and IDP thrown around and are looking for a little more information to help you get started. I’ll answer questions like What is OCR invoice processing? How is IDP different from OCR? And is one always better? Let’s get into it.
