In just a few months, enterprise AI has rapidly evolved into a strategic partner for organizations that want to stay ahead of the disruption curve. Whether enterprises are leveraging predictive analytics for strategic decision-making, using machine learning algorithms for supply chain optimization, or applying generative AI and large language models to enhance customer service, it’s clear AI will help business in many ways.

The latest automation trends are paving the way for what Gartner defines as "hyperautomation"—the philosophy that everything that can and should be automated will eventually be automated. However, many businesses seeking to harness the latest automation technology don’t know where to start. This blog highlights five timely trends in automation that should be on your digital transformation roadmap in 2024.

Artificial intelligence (AI) can transform the way your enterprise does business—but if you can’t quickly implement AI in your business processes, you’ll just as quickly fall behind competitors. This is where low-code AI tools can help. Over the past decade, low-code platforms have enabled software engineers, professional developers, and employees with minimal coding experience to build new digital, automated solutions using drag-and-drop interfaces.

The business of governance is not easy. Public sector organizations face a range of obstacles from corruption to lack of transparency to red tape—obstacles that have the potential to erode public trust in institutions and hinder economic development. That’s probably why, in virtually every country around the world, popular culture lampoons the intricacies of government bureaucracy.

Insurers’ profitability (and survival) is being threatened by a myriad of challenges—rising policy lapses due to higher premiums, customer churn from tightening budgets, and skyrocketing customer expectations. Squeezed by these pressures, insurers must find ways to improve efficiencies in their core processes. But there is a way forward for insurers: Business process management (BPM), an approach to improving organizational processes that is designed to optimize work.

Over the past year, interest in artificial intelligence has surged due to the proliferation of generative AI and large language models. These tools captured imaginations, demonstrating a technology brimming with possibility. While many focused on the potential of these tools, some companies made AI practical. For example, last year, Appian released packaged AI tools for processing content at scale and quickly building interface forms.

Aligning HR practices with business objectives improves operational efficiency and enhances employee engagement. HR casework usually spans multiple disconnected departments and systems—exactly the kind of process end-to-end case management can help you connect. Some common use cases for HR case management include: Staying compliant with complex and evolving regulatory environments requires speed, flexibility, and a unified view of data and workflows that can be easily audited.

As business leaders cautiously navigate the uncertainties of 2024, marked by the mainstreaming of AI, heightened regulatory oversight, cost pressures, and supply chain disruptions, the need for agile and efficient business processes has never been more pressing. Amidst this backdrop of uncertainty, cloud BPM shines as a beacon of opportunity, offering organizations a transformative approach to streamline operations, drive innovation, and achieve better business outcomes.

Before low-code, enterprise IT teams had to use either traditional development or commercial off-the-shelf software to provide business solutions. But when low-code development entered the scene, teams discovered they could create custom, enterprise-grade software applications up to 10x faster.

Prior to 2022, a lot of the AI news centered on use cases like self-driving cars. Of course, use cases like these didn’t exactly apply to enterprise software. In 2023 ChatGPT showed the abilities of AI to mimic human language, but still, this only scratched the surface of AI applications for the modern enterprise. If you’ve ever asked yourself, “What is AI capable of?” then consider the following examples: How many emails do you get per day?