Systems | Development | Analytics | API | Testing

AI is evolving faster than organizations can keep up, and often it’s the most important operations that fall the furthest behind. That’s exactly why Appian is focused on AI for complex, enterprise-grade processes—to make applying AI automation to important work easy. Over the last quarter, Appian has continued to invest in capabilities that help you deploy AI fast without sacrificing control: Over 80% of developers regularly use AI coding tools.

An intelligent document processing (IDP) platform uses AI technologies to automatically capture, classify, extract, validate, and route data from unstructured documents such as invoices, contracts, emails, PDFs, or even hand-written scans. As organizations continue to adopt AI into their daily workstreams, IDP is on track to shift from a back-office efficiency tool to a core component of enterprise AI automation.

Accounts payable metrics are the numbers a finance team uses to judge how well AP is working. They show what each invoice costs to process, how fast and accurately it moves to payment, and how well payments are timed. The most useful accounts payable performance metrics fall into five families: cost, productivity, speed, quality, and cash. These metrics apply whether invoices are keyed by hand or captured with accounts payable automation. No single number tells you whether AP is healthy.

An API is a defined way for one piece of software to request data or actions from another. The Model Context Protocol (MCP) is an open standard that gives AI agents a consistent way to discover and use tools and data sources, so they don't need a custom integration for each one. The short version: MCP doesn't replace APIs. Most MCP servers call APIs behind the scenes. What MCP changes is who the interface is designed for. APIs are designed for developers who read documentation and write code.

Agentic AI security is the set of controls that keep AI agents from accessing, changing, or sharing more than they should. It covers who an agent acts for, which data and tools it can use, which actions need approval, and how its activity is recorded and reviewed. AI agents are moving from pilots into daily operations. They read invoices, update records, answer customers, and trigger workflows across business systems.

LLM observability is the practice of collecting and analyzing data about how a language model application or AI agent behaves, including prompts, responses, tool calls, cost, speed, and errors, so teams can find problems, measure quality, and improve results. AI agents can now plan tasks, call tools, and work across business systems. That makes them powerful, and it also means a single request can involve many steps that no one sees directly.

Many invoice problems don't start in accounts payable. They start earlier, when someone buys without a purchase order or a delivery goes unrecorded. AP is simply where those gaps become visible, usually as an invoice that can't be paid. That's why it helps to see the accounts payable process end to end. It's how a business receives, checks, approves, pays, and records the bills it owes suppliers, whether those steps run by hand or through accounts payable automation.