Artificial intelligence (AI) has reached a tipping point in the public consciousness. Much of this has been driven by technology developments related to large language models (LLMs) and the release of generative AI tools, including ChatGPT from OpenAI. However, for enterprises shaping forward-looking AI strategy, a critical part of the conversation that needs to be addressed is the issue of private AI vs. public AI.
If you think workflow orchestration sounds like tech jargon, check out this simple explanation and consider practical advice for how to apply it to your business processes. Let’s start with the basics: what is workflow orchestration? Put simply, workflow orchestration is the end-to-end management of people, digital workers, systems, and data in a process.
Process mining is a trusted tool for continuous improvement. It helps you understand your business processes as they actually are, shows you all of the variants and deviations, and provides suggested explanations for why process problems are occurring. Knowing how process mining works is one thing—putting it into practice is another. How do you know what makes a process a good candidate for mining? And how can you build a business case that a process needs to be mined?
A great experience isn’t just about quality products and services or fast response rates. Users today expect all of that and more. They want easy, on-demand access to information and services from any device at any time. And they expect transitions across devices, locations, and processes to be seamless.
In 2023, AI is everywhere, and not a day goes by without someone developing a new way to use tools like ChatGPT. With the increasing hype about the promise of AI, it’s more important than ever to cut through the noise and focus on how AI can help you deliver real business value. Modern process automation platforms go above and beyond the capabilities of AI alone.
Your teams need constant connectivity to your organization's data and systems to operate effectively and thrive. And with digital transformation spurring a more rapid pace of innovation and technology adoption, this need for connected, democratized data is on the rise. That makes data silos an enemy you can no longer tolerate. Data fabric is the modern answer for eliminating data silos. Data silos occur when data is stored in separate systems or departments without proper integration.
April showers bring May feature releases to the Appian Platform. Get ready for sunny skies ahead with some seriously great new updates to Appian in 23.2. View documentation, release notes, discussion board posts, and tutorials on Community. The AI buzz is everywhere. If you’ve been getting bombarded with requests to “use more AI,” we’ve got you covered.
Artificial intelligence (AI) and automation stand at the vanguard of the future of work. While it’s impossible to predict with certainty how things will change, a few current trends and stats provide a good sense of where the future of AI and automation will take us. As evidenced by the recent interest in generative AI, companies want to experiment with how to reap deep benefits from automation technology.
What can data fabric do for your organization and how does it differ from other data management approaches? This primer will get you up to speed on data fabric essentials and help you explain the basics of this top tech trend. What is a data fabric? A data fabric connects data sets across disparate software systems, whether they’re on-premises or in the cloud, and creates a complete view. It’s both a tool set and an unified architecture layer (also called a virtualized data layer).
Artificial intelligence (AI) is the revolutionary technology that powers assistants like Siri and Alexa, self-driving cars, and personalized recommendations on shopping websites. And it’s safe to say that the AI revolution is continuing to gain momentum across industries and the public sector. AI can be particularly transformative in government procurement, streamlining the acquisition process to save time and money.
