Too many cars, too few parking spots, and never enough time! Does that sound familiar? Well, this chaotic hustle-bustle gave rise to urban transportation apps like Uber. The mere mention of its name brings a sigh of relief to daily commuters like us, isn’t it? But wait a minute. Have you ever wondered how this magical app came to be? Do questions like- ‘How can I make an app like uber, and how much is the cost to build app like uber?’ ever crossed your mind?

The relentless rise of ChatGPT and other Large Language Models has brought a major breakthrough in the world of artificial intelligence. With key industry players like Meta, Google, Nvidia and Microsoft competing fiercely to dominate this space, it’s likely we’ve only seen the start of the innovation. Several big companies have made their AI Models and platforms available for the public to use and build on.

By now, every organization, regardless of industry, has at least explored the use of AI, if not already embraced it. In today’s market, the AI imperative is firmly here, and failing to act quickly could mean getting left behind. But even as adoption soars, struggles remain, and scalability continues to be a major issue. Organizations are quick to adopt AI, but getting it established across the organization brings a unique set of challenges that come into play.

Artificial intelligence (AI) is kind of a big deal. And when things are a big deal, they're ripe to be exploited. Fortunately, mounting concerns about AI security and privacy are met by plenty of guidance on best practices from the good folks in the open source world. The OWASP AI Security Project has emerged as a crucial initiative, offering developers clear, actionable guidance on designing, creating, testing, and procuring secure and privacy-preserving AI systems.

As the mobile app market continues to expand, the demand for robust and efficient testing solutions has never been greater. Traditional testing methods are proving inadequate in addressing the complexities and speed requirements of modern mobile applications. This blog explores how cloud mobile testing is transforming quality assurance (QA) in the mobile era, offering scalable, flexible, and cost-effective solutions that meet the demands of today’s fast-paced development environments.

Does your application function as expected across the many devices your customers use? People use the same application across a wide range of gadgets, including mobile phones, smart TVs and streaming devices, gaming consoles, and desktop web apps. It’s imperative that all versions of your application work consistently and as expected across various platforms.

In 24.2, we introduced our enterprise copilot. Enterprise copilot allows you to upload business documents and collect them in knowledge sets. From there, you can ask questions about information in these documents and receive answers quickly. For instance, an organization with a heavy regulatory burden could upload legislative and operational documents. Then, these employees could get insights from Appian AI Copilot to ensure they adhere to compliance requirements.

Ready or not, AI workflow automation is poised to transform business operations from the shop floor to the C-suite in the AI economy. As organizations embrace digital-first initiatives, IT teams will be able to do much more with less. The situation is a byproduct of the generative AI boom. And yet, so many companies have hardly scratched the surface of AI automation’s full potential in their business operations.