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.
AI is a top action item on the priority list of every leading organization. And as the AI frenzy unfolds, some of the promises about what AI could deliver have become less clear, complicated by matters of security, data, and ultimately, business value. Appian 23.3 builds on Appian’s existing AI capabilities and makes it easier than ever to operationalize AI within your enterprise.
Machine learning watching generative artificial intelligence (AI) take off feels a little bit like an American Girl doll envying the Barbie movie excitement from afar. What is she, chopped liver? But we can’t forget about machine learning, because it’s the giant that generative AI is standing on. How? Well, machine learning is how generative AI learns. Generative AI takes machine learning a step further by leveraging those learnings to produce something new.
In 2022, financial services firms faced over $8 billion of fines for anti-money laundering (AML) process failures. And for many, things aren’t getting better—false positives keep rising right along with client expectations. Regulations continually increase in number and complexity and criminals are getting ever more sophisticated in their tactics and techniques.
The value of data is no longer debatable. But the secret to unlocking that value still evades many organizations. Only 44% of data and analytics leaders think their teams are effective in providing value, according to a new Gartner® survey. And business users are still struggling, too, citing accessibility issues and complexity as barriers to data use. Combine this with low executive confidence in data, and it’s clear that data challenges are ubiquitous.
Intelligent process automation (IPA) isn’t for everyone. Let me explain. Intelligent process automation is meant for large-scale digital transformations. So if you're looking to make small changes at the margins, like automating simple tasks, IPA probably isn't for you. IPA is better suited to large organizations with lots of data that want to streamline complex, enterprise-wide processes—to digitally transform their workflows, top to bottom.
Google the topic of artificial intelligence, and you’re likely to be taken down a deep, winding rabbit hole. If you venture only a little under the surface, you will encounter fantastical terms like perceptron, sigmoid neuron, and nonlinearly separable classifications. To save you from falling into that hole, this article will give a short, clear explanation of AI vs. generative AI.
