Artificial intelligence (AI) has taken the world by storm. ChatGPT was the ultimate proof of concept, demonstrating the power of large language models and AI in easy-to-understand terms. So naturally, business leaders are eager to unlock the productivity benefits that come from integrating AI into business operations. But despite their eagerness, organizations still need to do some work to prepare for AI integration.
Business process management (BPM) can help organizations in any industry streamline processes in any department. Even though the discipline has been around for years, the benefits of BPM are as real as ever. In the short term, organizations can use BPM to decrease costs and improve efficiency, which can lead to more revenue and faster growth. In the long run, a strong BPM practice helps organizations create and maintain competitive advantage by improving their agility.
Generative AI seems like it's shaking things up for process automation, like other industries. But in reality, artificial intelligence is less of a shake-up and more of a natural complement to the capabilities that support a process automation initiative. Imagine a world where AI can turn a PDF into a digital interface, or sort all the emails in an inbox and generate responses for an employee to review.
Artificial intelligence (AI) has led to a seismic shift in the business landscape, largely due to the surge in popularity of large language models like ChatGPT. From predictive models that foster better decision-making to generative AI code tools that enable teams to build applications faster, AI offers incredible benefits to organizations. Businesses need to embrace this technology or risk falling behind their competitors.
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.
