Intelligent process automation (IPA) isn’t for everyone.
Let me explain.
Intelligent process automation is meant for large-scale digital transformations. So if you're looking to make small changes at the margins, like automating simple tasks, IPA probably isn't for you. IPA is better suited to large organizations with lots of data that want to streamline complex, enterprise-wide processes—to digitally transform their workflows, top to bottom.
But 70% of digital transformation initiatives fail. So if you’re struggling, you’re in good company. Intelligent process automation might be the solution you’ve been searching for.
Intelligent process automation is a combination of end-to-end business process automation, task automation, and artificial intelligence. McKinsey called IPA “the engine at the core of the next-generation operating model.” It lets organizations work faster, reduce costs, and deliver higher quality customer and employee experiences. IPA involves three core components:
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Business processes are messy. They involve lots of people working with large volumes of data stored in all different formats and systems, from scanned invoices, emails, and Excel files to niche apps, modern CRMs, and old-school databases. To keep an automated workflow moving, you need technologies that can access and interpret all your data—structured, semi-structured, and unstructured. IPA fits that bill.
Intelligent process automation technologies can access and parse unstructured data using data connectors, RPA, optical character recognition (OCR), natural language processing (NLP), and artificial intelligence. Here's an overview of how IPA tools handle unstructured data:
Data interpretation: IPA tools use natural language processing (NLP) and AI to parse text and extract meaningful information. For example, from an email, an IPA tool can extract sender details, subject lines, timestamps, and message content and sentiment. They can also categorize unstructured documents based on their content or purpose. Machine learning algorithms can be trained to automatically identify document types, such as invoices, contracts, resumes, or customer feedback forms. This classification can trigger the next step in a workflow—document routing or processing, for example.
If you’ve done things right, your automations should run pretty autonomously. IPA technologies can make intelligent decisions or trigger appropriate workflows based on predefined rules or machine learning models. For instance, an AI tool may use previously extracted data to determine the next steps in an invoice processing workflow, such as validation, approval, or exception handling.
Why can IPA operate with little to no human intervention? Because they run on automated, pre-programmed rules about how data is processed and tasks are executed. Once these rules are established, the automation tool can execute the tasks consistently and accurately. They even have cognitive AI capabilities that allow them to make decisions at inflection points in a workflow.
While IPA can operate with minimal human intervention, it's important to note that human oversight, monitoring, and exception handling are still necessary. Human involvement may be required for handling complex decisions, addressing unique situations, managing exceptions outside the defined rules, and ensuring the overall performance and compliance of the automated processes.
Robots have not yet achieved consciousness—we think. But they are faster and more accurate than human workers. One bot can process huge volumes of data or make complex calculations almost instantly, with zero risk of human error.
And because of their rules-based programming, automations operate highly consistently. That’s great news for industries where compliance is a high priority. Automation tools can be configured to ensure compliance with specific regulations, policies, or industry standards. They can enforce standardized processes, track and log activities, and generate comprehensive audit trails, providing a transparent and reliable record of operations for regulatory purposes.
Automations also run around the clock, skyrocketing productivity and in many cases eliminating the need for shift work. For example, letting a chatbot handle customer service inquiries after hours means not only improving employees’ work/life balance but also faster response times for better customer experience. Intelligent process automation is all about win-wins like these.
Intelligent process automation is highly scalable, giving businesses the flexibility to meet the ever-evolving demands of their industry. IPA can be easily modified to meet the changing needs of an organization as the demand for automation rises. Two main factors that make IPA so scalable are its modular architecture and its cloud infrastructure:
Modular architecture. The modular architecture of IPA technologies allows different automation components (e.g., RPA bots, AI models) to be developed and deployed independently. This modularity enables businesses to scale their automation efforts by adding new components as needed. Organizations can integrate additional RPA bots to handle more tasks or incorporate advanced AI capabilities for more sophisticated automation. Each module can be tailored to specific processes or functions, providing flexibility and adaptability to scale IPA across different departments and business units.
Cloud infrastructure. Many IPA tools are cloud-based. Cloud platforms offer virtually unlimited processing power and storage, making it easier for businesses to scale their automation initiatives. Organizations can dynamically allocate resources based on demand, quickly provisioning additional computing capacity as workload requirements grow. This eliminates the need for significant upfront infrastructure investments and allows businesses to scale IPA rapidly and reduce costs effectively.
Thanks to the adaptability of IPA technologies, firms may automate tasks in a wide range of departments and functions, and because of its flexibility, IPA will continue to serve businesses well even as their needs change.
Practice makes perfect, even for machines. Machine learning capabilities let IPA technologies self-improve over time. IPA technologies can analyze large amounts of historical data to identify patterns, trends, and insights. By applying ML algorithms to this data, an IPA system can learn from past experiences and adapt its behavior accordingly. It can discover correlations, recognize anomalies, and make adjustments based on the knowledge gained.
IPA technologies are also able to incorporate and act on human feedback. Users can provide feedback on a system's outputs, correct errors, or confirm the accuracy of its decisions. This feedback is used to refine and optimize the system's performance.
Automation is inevitable. According to the World Economic Forum (WEF), 42% of business tasks will be automated by 2027, including not just information and data processing but reasoning, communicating, and coordinating. In order to automate those more complicated, reasoning- and analysis-based activities, you’ll need an automation strategy that goes beyond the simple task-based capabilities of RPA.
The WEF Future of Jobs Report 2023 found that “artificial intelligence . . . is expected to be adopted by nearly 75% of surveyed companies. Digital platforms and apps are the technologies most likely to be adopted by the organizations surveyed, with 86% of companies expecting to incorporate them into their operations in the next five years.”
An intelligent process automation platform will give you all of this in one place. Look for a platform with full process automation capabilities, including BPM, RPA, and AI. Not sure where to get started? Check out our end-to-end business process automation guide.