Systems | Development | Analytics | API | Testing

When information is spread across disconnected systems, it becomes difficult and time-consuming to find the right information, make informed decisions, and move work forward. Take, for example, planning a weekend getaway with friends: you have to review airline portals, manage a shared Google Doc for the itinerary, orchestrate a flurry of WhatsApp chats, and book an Airbnb reservation. Making decisions with this much information sprawl becomes exhausting.

Customer service AI orchestration connects AI-driven intake to the backend systems and people who actually resolve a request, not just the chatbot that receives it. Every request should trigger an end-to-end resolution, not stop at an automated response. The real challenge with AI in customer service isn’t adoption; it’s fragmentation.

Enterprise IT leaders are often caught between the pressure to innovate and the demands of maintaining their current tech. When a system slows down or struggles to scale, it can be hard to know what to do. Rather than being stalled by uncertainty or getting distracted by fixes that don’t solve the underlying problems, Appian’s Customer Success experts encourage our customers to start with a core thesis: non-invasive application tuning should always be your first line of attack.

AI agents make thousands of decisions per day, at volumes no human-centered governance model can realistically supervise. According to IBM's 2026 Tech Leader Study of 2,000 CIOs/CTOs in 33 geographies across 19 industries: The study concludes that organizations face a trap: prioritize speed, and governance falls behind; or prioritize safety, and deployment stalls, weakening the organization’s competitive position.

Everyone can build an AI agent today. The hard part isn't getting an agent to answer a question or complete a demo. It's deploying one that employees trust, security teams approve, and operations teams can manage at scale. That's where many AI projects stall. As organizations move beyond experimentation, the conversation shifts from prompt engineering to production readiness. Can the agent safely access business data? Can you evaluate changes before deployment? Can you understand why it made a decision?

Document-centric workflows have been difficult to automate and required human intervention. Attempts to automate document handling often failed or did not scale, because legacy intelligent document processing (IDP) systems were fragile. They often required manually retraining models on dozens of documents just to identify specific fields—only to repeat the process whenever a format changes. The result was a costly cycle of maintenance and manual data entry.

Since its inception, agentic AI has felt like a distant aspiration. Today, agents are here, and enterprise adoption is accelerating. Gartner predicts that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025. Agents arrive with incredible, broad intelligence, but lack the knowledge of your operating model: your customers, policies, approvals, exceptions, business rules, systems, and operational history.

Your users increasingly work through AI assistants. When they ask an agent to check a case status, analyze last quarter's metrics, or kick off an approval workflow, that agent needs to access your enterprise systems. Enabling that connection is the core challenge of AI agent integration: giving AI assistants the ability to discover, understand, and safely interact with business applications and data on behalf of users.