What is AI Automation? A 5-minute Guide for Operations Leaders

AI Automation is the use of artificial intelligence to automate repetitive tasks, streamline processes, or handle exceptions that rule-based automation can’t anticipate. Traditional automation follows the steps someone scripted; AI automation handles the exceptions and people step in where judgment matters. 

Today, adoption is broad, but impact is not. A recent Deloitte study found that only 34% of leaders see it as deeply transforming their business. The organizations applying AI most effectively build it into their processes.

There’s much evidence to show that harnessing the speed, power, and scalability of AI automation has allowed enterprises to entrust decision making, business problems, and entire workflows of routine tasks to intelligent machines and software. In just one example from financial services governance, we’ve seen customers reduce processing times up to 75%, achieve 100% anti-money laundering compliance, and drive 57% lower cost savings through operational efficiency.

With all this potential, what should business leaders focus on to adopt AI automation? After reviewing three platform considerations, this guide will cover:

• Tips to get started

• Benefits from using automation in AI

• Practices to adopt

The most effective AI automations deliver more when they run inside a platform that orchestrates processes end to end, rather than as isolated tools. Aim to account for these three factors:

1. An AI process automation platform coordinates the people, AI agents, bots, systems, and data in a process. It assigns and tracks each task, routes work between them, and shows the full process from intake to resolution. Low-code development lets teams change the process as requirements shift. Gartner calls this category business orchestration and automation technologies (BOAT).
2. Platform governance must be able to apply your policies to AI. Guardrails govern what data can flow through a model, and what actions AI can take. They operate automatically across the environment, without requiring developers to build protections into each application.
3. Content processing, or intelligent document processing (IDP), extracts data from incoming documents, forms, or emails that start most processes and passes it to the next step; where rules, AI agents, and people act on that data. Essentially, IDP represents a transformation of unstructured content into usable data that powers your business processes. 

Anti-money laundering alerts: When monitoring flags a transaction, an AI agent pulls the customer history and related accounts, consolidating evidence throughout. Once complete, it sends to an investigator for review; all the while tracking every step for an audit trail.

Customer onboarding (KYC): Document processing extracts data from IDs, statements, and corporate filings. An AI agent reviews these against policy, and flags gaps and drafts the follow-up request. In this workflow, analysts focus their time on the exceptions since straightforward applications pass through cleanly.

Loan origination: AI classifies each incoming application by product and risk tier, then pulls the data an underwriter needs into one view. Underwriters still make the credit decision; they no longer assemble the file across systems first.

AI agents are limited by the contextual data they have access to and are told to prioritize. To make your data accessible to AI, you need a strong context layer (one unified place to see and interact with your data). Without a context layer (like data fabric), you won’t be able to connect AI agents to operational context and get the most out of your AI investment. A data fabric connects data across the enterprise, providing a managed data pipeline. When AI agents act, the results can be recorded in the systems of record so the next step starts with the latest data.

AI automation sets new benchmarks in the following areas:

1. Process optimization: AI acts on live data at the decision point in a given process. It reads the incoming request, checks it against policy, and routes it to the right person or system. In this way, decisions can be made without delay from information collection or comparison.

 2. Operational resilience: When a traditionally automated process hits an undocumented scenario, it stalls. AI automation absorbs more of that variability and can reason through errors and variables, handling greater process complexity without explicit instructions.

 3. Cost savings: Companies leveraging AI automation report lower costs and new revenue. A recent survey found over 80% of financial services professionals reported that AI contributed to both cost reductions and revenue increases.

Aligning AI automation with strategic business objectives is a powerful way to optimize business outcomes. If you’re looking to move AI automation from pilot to production:

• Embed AI in the process: AI delivers the most value when it works inside your business processes rather than tangentially. A standalone AI tool can summarize a document or answer a question, but that still requires a human to execute the handoff. When you build AI into an end-to-end process, it acts on your data, reduces handoffs, and operates under the same controls as the rest of the workflow. This directly drives faster outcomes, like case resolution or fewer exceptions, rather than isolated, end user productivity gains.

• Consider existing automations: It’s established that AI handles unstructured inputs excellently. Applied AI opens another opportunity for organizations that run robotic process automation (RPA) on systems with no API (i.e. mainframes, green-screens) or dynamic web layouts. Since these scenarios frequently break bots, an AI agent can be called on to guide a failed bot to success instead of stalling the whole process – no manual intervention required.

• Make decisions transparent: In a recent survey, over 60% of CEOs identified trustworthiness as a primary concern associated with AI automation. This indicates a growing need for traceable AI, decision history with point-in-time context records, and transparent algorithms to ensure the system is auditable.

Appian lets you build and automate your most important and complex processes. As the digital economy has evolved, it’s becoming clear that organizations who treat AI as part of their process architecture, not a standalone tool, will compound its value across every workflow it touches. The starting point is a platform that connects AI, automation, and your people in one place.