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.
Technology is an indispensable ally for navigating process compliance challenges with confidence. That’s because as digitization expands and regulatory requirements change, process compliance becomes more and more difficult to address manually. In this blog post, we’ll examine common process compliance challenges and how technology can help solve them. The most significant challenges for process compliance are manual and paper-based systems.
The effects of disconnected data are many: lack of data-driven decision making, inaccurate information, and slower processes, to name just a few. When you quantify the total cost of data silos, you’ll find that organizations have a lot to lose. While many data silo statistics predict a gloomy future where organizations struggle to unite their enterprise data, new approaches to data management, like data fabric, can help.
If your business has a lot of complex workflows, you’re likely relying on a number of tools and manual processes to get things done. And if you’re seeing that processes are slow, inefficient, or require a lot of manual work, business process modeling tools can help. Business process modeling tools are specifically designed to help you model and optimize your business processes.
If you’re not using artificial intelligence (AI) in your organization right now, you’re behind. But the reality is that beyond inputting some ideas into a large language model like ChatGPT, AI just isn’t that simple to operationalize across your business (although the benefits are real).
Why is private AI so hot right now? Organizations are reluctant to share their data with public cloud AI providers who might use it to train their own models. Private AI offers an alternative that lets them reap the transformative benefits of AI on process efficiency while maintaining ownership of their data.
