Systems | Development | Analytics | API | Testing

AI agents can manage a wider range of tasks than any automation tool yet developed, thanks to their decision intelligence and context reasoning capabilities. Agentic workflows, or processes where at least some of the work is automated by AI agents, make some IT leaders enthusiastic and give others pause. There are valid reasons for both feelings. And the stakes are even higher when you begin orchestrating multiple AI agents.

The landscape of enterprise automation is undergoing a major shift, moving beyond bots and predefined workflows. The 2025 Gartner Hype Cycle for Enterprise Process Automation provides a clear roadmap for this new era, assessing the key innovations that will redefine how businesses operate. The central theme? As automation becomes more intelligent, autonomous, and democratized, the need for strategic orchestration has never been more critical.

In this year’s Appian Developer Sentiment Survey, nearly 1,000 Appian developers from six continents shared their insights—a 53% increase from the last survey. The result? The most representative snapshot of the global Appian developer community yet. This report captures what matters most to you—from views on AI and certifications to career goals and go-to resources—and how these inputs are shaping what’s next for Appian developers.

IT leaders have long believed that integration is the best way to connect a chaotic enterprise technology landscape. However, that approach is falling short. It's not powerful enough to manage the complexity, which has only increased with the introduction of AI. What’s needed now is an overarching, holistic view of people, data, processes, and AI—a view that’s made possible with end-to-end process orchestration.

Financial institutions face growing operational demands in an environment defined by regulatory complexity, legacy system inertia, and the rapid evolution of customer expectations. At the same time, IT leaders are under pressure to not only maintain infrastructure but also demonstrate value to their operations counterparts. The opportunity is clear: use technology to drive operational agility without disrupting existing systems. This is where Appian excels.

Low code automation promises to be more palatable and less time-consuming. There is a wide range of technology options for low code automation; as more "no code" tools become available, and more tools that claim to be "low-code" are rolling out on the market, it is rational for developers to become worried in terms of productivity deficiencies, overtime costs, and the costs borne by consumers if downtime occurs.

A new MIT study reveals 95% of gen AI pilots fail. But that’s not an AI problem. It’s an implementation problem. The real issue is the messy, fragmented way AI is used. Too many organizations treat AI as a helper on the sidelines—chatbots, copilots, and assistants that wait to be called upon. While helpful, this approach barely scratches the surface of what’s possible. Real transformation happens when AI is embedded directly into the core operations of your enterprise.

Before jumping into AI data cleaning directly, let’s first understand data cleaning itself. Data cleaning, also known as data scrubbing, is a critical data preparation step where organizations remove inconsistencies, errors, and anomalies to make datasets ready for analysis. The cleaning process may involve actions like removing null values, correcting formatting, fixing syntax errors, eliminating duplicate data, or merging related fields like City and Postal Code.

How to make your enterprise data accessible, usable, and insightful.