Artificial intelligence has the potential to make work incredibly efficient—which means it’s the perfect complement to process automation technology. Process automation, and related approaches like business process management, already aim to improve productivity by automating what can and should be automated.
What are the differences between generative AI vs. large language models? How are these two buzzworthy technologies related? In this article, we’ll explore their connection. To help explain the concept, I asked ChatGPT to give me some analogies comparing generative AI to large language models (LLMs), and as the stand-in for generative AI, ChatGPT tried to take all the personality for itself.
Maya Angelou once said, “You are the sum total of everything you’ve ever seen, heard, eaten, smelled, been told, forgot—it’s all there. Everything influences each of us, and because of that I try to make sure that my experiences are positive.” Experiences matter. They matter to us in our personal lives but also in our work lives as employees, customers, and software users. That’s why total experience is such an effective business strategy.
Large language models (LLMs) are all the rage, fueled by the release of OpenAI's ChatGPT in late 2022, initially powered by the LLM GPT-3. Aside from the news hype, what can LLMs actually, getting-down-to-brass-tacks, nitty-gritty do for your business? Here, we’ll look at three examples of problems they can solve. But first, a quick definition of LLMs.
Today, organizations must do more with less. The pace of innovation has increased exponentially, yet resources remain the same (or are dwindling). Between talent shortages, long development cycles that rely on traditional programming languages, and technology teams that are already stretched perilously thin, many businesses have glaring operational problems they simply can’t solve with their current resources.
The product owner role is becoming increasingly common at government agencies implementing new software, such as government acquisition solutions. The product owner’s responsibilities include defining and prioritizing the product backlog—which includes a list of features, enhancements, and fixes—to make sure the product aligns with business goals.
Ever since the release of ChatGPT, which showed the potential of generative artificial intelligence (AI), enterprises have raced to operationalize generative AI within their organizations. In fact, AI represents the primary challenge for nearly every organization today. You will either be good at AI or bad at business. Appian was quick on the AI draw.
Hyperautomation is more than a buzzword. It’s the new gold standard for business processes. And to get your organization’s leadership on board, you need to be able to explain why. According to experts at Gartner, "by 2024, hyperautomation functionality will be the dominant competitive differentiator among low-code development tools." This change is raising the bar for basic tech stack expectations, breaking old rules, and opening up a world of exciting possibilities.
The days of back-to-school jitters might be behind you, but the new updates in Appian 23.3 will give you those warm and fuzzy feelings of anticipation all over again. These six updates in the Appian 23.3 release are so good you won’t want to wait to try them out. Ever wish that you could pass off tasks that require a lot of copy-paste and endless formatting? Thankfully, with AI, now you can. Read the 23.3 release notes on Appian AI Copilot.
