Intelligent automation requires an integration layer between new solutions and existing data and technology. This system of engagement connects humans to machines and data. This critical layer can manage multiple processes, disparate data, and legacy systems to create a unified user experience.
With this in mind, user experience should be the guiding principle behind automation. That's how you avoid automating for the sake of automating. Instead, you can make changes that help employees do their jobs more effectively and create more efficient and personalized services for customers and partners. When you think about automation through this human-centric lens, you deliver experiences that exceed expectations, inspire loyalty, and increase profits.
Governance is also essential. It ensures that intelligent automation performs as expected, uncovers reasoning behind outcomes, helps build trust in automation among employees, and protects your return on investment (ROI).
The other key components of an intelligent automation engine include:
- Process insights: Though initial process mining and discovery helps you find opportunities for transformation, continuous process insights lead to ongoing process performance improvements
- RPA: Bots automate rules-based processes that require structured data. By taking over routine and transactional activities, such as data entry, bots free human workers to focus on higher-value, more strategic work
- Orchestration: Dynamic workflows enable human workers, RPA bots, and machine learning (ML) to work together. For instance, if there's a transaction that a bot can't process, a human is notified to find a solution. In this way, bots can self-manage exceptions to eliminate bottlenecks and speed up operations
- AI: Using AI, you can extract and process unstructured information into a structured format. Conversational AI can understand customer intent and handle interactions. These cognitive solutions are what make automation truly intelligent
However, you cannot achieve intelligent automation without a strong data foundation. You need data engineering to source, clean, and prepare data and to ensure that your core business technology services – such as enterprise application services and cloud databases – work in harmony with automation.
When you bring it all together, you'll have created an intelligent automation engine (figure 1).