Appian AI

Overview

Appian AI is an integrated suite of artificial intelligence capabilities within the broader Appian Platform, a low-code development environment. The platform is designed for rapid application development, process automation, and case management, enabling organizations to build and deploy enterprise applications with reduced manual coding. Appian AI extends these capabilities by allowing developers to embed AI components directly into their workflows and applications. This includes functionalities such as intelligent document processing (IDP), natural language processing (NLP), and machine learning (ML) model integration.

The platform is suited for technical buyers and developers who require a visual, model-driven approach to application development and process orchestration. It aims to accelerate the deployment of AI-powered solutions by abstracting away some of the underlying complexity of AI model training and integration. For example, Appian AI can be used to automate the extraction of data from unstructured documents, classify customer inquiries, or provide predictive insights within a business process. This focus on integration within existing business workflows differentiates it from standalone AI/ML platforms.

Appian AI shines in scenarios where organizations need to rapidly infuse AI into operational processes, particularly those involving high volumes of data or complex decision flows. Its low-code nature allows business analysts and citizen developers, alongside professional developers, to contribute to the creation of AI-enhanced applications. The platform supports integration with various third-party AI services, including those from major cloud providers, allowing users to leverage specialized AI models or bring their own. This hybrid approach caters to diverse enterprise requirements, from automating repetitive tasks with RPA to building sophisticated case management systems augmented by AI insights. According to a 2023 report, enterprises are increasingly adopting low-code platforms to accelerate digital transformation initiatives, including AI integration, to address business demands for speed and agility (Gartner on Low-Code Development). Appian's offering aligns with this trend by providing tools that bridge the gap between AI capabilities and business process execution.

Key features

• Intelligent Document Processing (IDP): Automates the extraction, classification, and validation of data from various document types, such as invoices, contracts, and forms, using AI models.
• AI Skill Designer: Provides a visual interface for configuring and integrating pre-built or custom AI models into Appian workflows, abstracting complex AI/ML operations.
• Natural Language Processing (NLP): Enables applications to understand, interpret, and generate human language, supporting tasks like sentiment analysis, entity extraction, and chatbot integration.
• Machine Learning Integration: Facilitates the connection and deployment of external machine learning models from providers like Google AI, AWS AI, and Azure AI, or custom models, into Appian processes (Appian AI Skills documentation).
• Process Mining: Analyzes business process data to identify bottlenecks, inefficiencies, and opportunities for automation, providing insights that can inform AI implementation strategies.
• Robotic Process Automation (RPA): Integrates software robots to automate repetitive, rule-based tasks, often working in conjunction with AI to handle exceptions or unstructured data.
• Data Fabric: Connects to various enterprise data sources without requiring data migration, providing a unified view of information for AI models and applications.
• Low-Code Development Environment: Offers a drag-and-drop interface and visual modeling tools for building applications and integrating AI capabilities, reducing the need for extensive coding.

Pricing

Appian offers custom enterprise pricing for its platform, including AI capabilities. Pricing is generally structured based on usage, number of users, and specific features required. A free trial is available for prospective customers to evaluate the platform's capabilities.

For detailed pricing information and to obtain a custom quote, interested parties should contact Appian directly (Appian Pricing Page).

Common integrations

• Cloud AI Services: Connects with AI services from major cloud providers such as Google Cloud AI (Google AI Platform), AWS AI Services (AWS AI Services Overview), and Azure AI (Azure AI Platform) for specialized ML models, vision, and speech capabilities.
• Enterprise Applications: Integrates with existing enterprise systems like ERP (e.g., SAP), CRM (e.g., Salesforce Salesforce ERP Integration Guide), and custom databases via APIs and connectors.
• Robotic Process Automation (RPA) Tools: Works with Appian RPA to automate repetitive tasks, often complementing AI for handling unstructured data or complex decision points.
• Document Management Systems: Connects with systems like SharePoint or Box for intelligent document processing and content management.
• Messaging and Collaboration Tools: Integrates with platforms like Slack or Microsoft Teams for notifications, approvals, and chatbot interactions.

Alternatives

• OutSystems: A low-code platform for rapid application development, often competing in enterprise-grade application delivery.
• Mendix: Another low-code platform focused on accelerating enterprise application development and digital transformation initiatives.
• Pega: Specializes in intelligent automation, CRM, and digital process automation, offering a strong suite for complex case management and workflow orchestration.

Getting started

To begin using Appian AI, developers typically start by signing up for a free trial of the Appian Platform. Once access is granted, the primary development environment is visual and model-driven. While much of the AI integration is done through drag-and-drop components and configuration, custom extensions or complex integrations might involve Java. Below is a conceptual Java code snippet demonstrating how an external AI service might be called from a custom Appian plugin, assuming the plugin framework is set up.

This example illustrates a basic Java method that could be part of an Appian plugin to interact with an external sentiment analysis API. In a real-world Appian application, this plugin would be invoked from a low-code process model.

import java.io.OutputStream; 
import java.net.HttpURLConnection; 
import java.net.URL; 
import java.nio.charset.StandardCharsets; 

public class AIServiceIntegration { 

    public String analyzeSentiment(String textToAnalyze) throws Exception { 
        String apiUrl = "https://api.example.com/sentiment-analysis"; 
        URL url = new URL(apiUrl); 
        HttpURLConnection conn = (HttpURLConnection) url.openConnection(); 
        conn.setRequestMethod("POST"); 
        conn.setRequestProperty("Content-Type", "application/json; utf-8"); 
        conn.setRequestProperty("Accept", "application/json"); 
        conn.setDoOutput(true); 

        String jsonInputString = "{\"text\": \"" + textToAnalyze + "\"}"; 

        try (OutputStream os = conn.getOutputStream()) { 
            byte[] input = jsonInputString.getBytes(StandardCharsets.UTF_8); 
            os.write(input, 0, input.length); 
        } 

        int responseCode = conn.getResponseCode(); 
        if (responseCode == HttpURLConnection.HTTP_OK) { // success 
            // Read the response 
            java.io.BufferedReader in = new java.io.BufferedReader( 
                new java.io.InputStreamReader(conn.getInputStream(), StandardCharsets.UTF_8)); 
            String inputLine; 
            StringBuilder response = new StringBuilder(); 

            while ((inputLine = in.readLine()) != null) { 
                response.append(inputLine); 
            } 
            in.close(); 
            return response.toString(); 
        } else { 
            throw new Exception("API call failed with response code: " + responseCode); 
        } 
    } 

    public static void main(String[] args) throws Exception { 
        AIServiceIntegration service = new AIServiceIntegration(); 
        String result = service.analyzeSentiment("This is a great product!"); 
        System.out.println("Sentiment Analysis Result: " + result); 
    } 
}

For official documentation and detailed guides on building Appian applications and integrating AI, refer to the Appian Documentation.