Task Automation: The First Stage in Intelligent Business Automation
Intelligent automation is a buzzword in tech circles and is becoming increasingly popular in business. For perspective, the global intelligent process automation market is a $13.6 billion industry expected to nearly double in value by 2027.
Why the growing interest in intelligent automation, and why now?
That can be attributed to these three factors:
The ongoing labour shortage
The unemployment-to-job vacancy ratio in Canada is at an all-time low of 1.4, and employers are already struggling to fill job positions. Intelligent automation offers a lasting solution to the labour crisis by delegating tasks and entire human roles to digital workers.
The mounting pressure for business agility
The modern business landscape is becoming increasingly volatile and erratic. In many ways, intelligent automation enables businesses to cope with market, industrial, and regulatory dynamics. Entrepreneurs are turning to AI and digitization to boost business agility, adaptability, and resilience.
The demand for excellent digital experiences
Employees and customers want nothing short of flawless human-centric digital experiences. There’s a heavy emphasis on bridging the gap between virtual and real-world business interactions. Businesses look to personalization, generative AI, and high performance to make digital interactions feel more “real.”
“There are a lot of companies making the push toward AI to become more productive and agile.”— Alexandre Lanoue, VP & Leader of Business Reimagineering, SIA Innovations.
Let’s have a look at intelligent automation to see what all the fuss is about, and why business leaders and analysts insist this is the way to go.
The Basics of Intelligent Automation
IBM defines intelligent automation as the use of automation technologies to streamline and scale decision-making across an organization.
As Alexandre Lanoue, VP & Leader of Business Reimagineering at SIA, puts it, intelligent automation revolves around three main ideas:
- Taking the robot out of the human: Freeing humans from mundane, repetitive, and non-value-added tasks.
- Getting processes to think: Making processes smarter, enabling digital workers or virtual systems to make decisions on their own.
- Turning workflows into business events: Transforming workflow management into event-based processes where actions are triggered by business events.
The intelligent automation journey has to start and end somewhere. We like to break down that journey into three key stages:
- Stage 1: Task Automation
- Stage 2: Decision Guidance
- Stage 3: Reimagine Workflow
Let’s focus on the first stage for now—what task automation entails, how it’s done, and the business value it brings to your organization.
What Is Task Automation?
In this context, a task is a clearly defined step in a business process. For example, one of the tasks in the loan approval process for a fintech company would be reviewing an applicant’s documents for borrower eligibility.
Task automation simply means eliminating or minimizing human involvement in initiating and completing business tasks.
In the example we’ve used above, it’s possible to automate borrower evaluation by having an automated system request and review documents from the loan applicant. The system can then send an informed recommendation to the next task in the loan approval process.
How Does It Work?
Task automation is a systematic and multifaceted process. Based on IBM’s approach to intelligent business automation, task automation involves four key components:
Process mining is a deep analysis of your business processes. Think of this as the discovery phase of task automation. Process mining tools collect data on your workflows and business operations in order to discover and visualize how certain tasks are performed.
The analysis reveals valuable insights into your business tasks, including:
Based on this analysis, you can figure out the tasks that can be improved through AI applications. In this case, improvement means doing things faster and more accurately while using fewer resources.
A big part of process mining involves simulating automation as a way to predict or test the potential outcomes of automating certain tasks. These simulations give you a good idea of how integrating robotic process automation or operational intelligence could affect the performance or outcomes of certain tasks and processes.
Sticking with our example, process mining paints a clear flow of all the tasks and processes involved in approving a loan application. These may include profiling the applicant, requesting supporting documentation, evaluating said documents, and reaching a verdict.
Decision-making is integral to business operations. The outcomes of each business task depend heavily on the decisions made from start to finish.
From a business perspective, decisioning is even more important. A single decision in a customer-facing process could mean all the difference between making a sale and losing a valuable business opportunity. And since every decision must comply with the underlying business rules and regulatory standards, decisioning can have several variables, making even seemingly simplest actions difficult to resolve.
This is where machine learning comes in. ML tools study customer behaviour, employee roles, and business logic to create decision trees leading to the desired outcomes. Additionally, smart decision management systems can accurately gauge and report the level of risk in certain decisions, making it easier to choose the moves worth taking where risk is a concern.
For our fintech company, decisioning involves evaluating a borrower’s risk and pushing a fair recommendation up the chain of command.
A workflow is a series of activities or tasks needed to complete an objective. Intelligent automation optimizes workflows in two main ways: task prioritization and insight generation.
Task prioritization means assigning tasks to the right employees and ranking them based on urgency. For instance, certain tasks are best handled by certain people in an organization, not just because of their qualifications and role but also because of their past experiences handling those tasks. And without considering urgency, tasks with critical deadlines could end up sitting in queues behind non-urgent tasks.
Another good thing about AI-optimized workflows is that they generate informative and actionable insights. Intelligent workflows visualize task/process performance, enabling you to identify areas of improvement. Such insights can even be fed back into the system to fine-tune automation performance.
Task automation systems can capture, understand, and classify unstructured business content. The capture capability means drawing useful information from materials such as documents and submissions. From there, the system “understands” the business context behind that information and classifies the material accordingly.
This means that smart tasks can recognize invoices, receipts, contracts, CVs, job quotations, and other documents without necessarily being trained to do so. Moreover, the AI can extract useful data, such as addresses, identifiers, figures, and dates, providing verified data as input for subsequent tasks in the workflow.
Start Automating Your Tasks Today
Task automation is a crucial stage in overall intelligent automation. Process mining, decision management, workflow optimization, and content management ensure each task is as cost-efficient, fast, accurate, and low-risk as possible.
Besides, it can be overwhelming or nearly impossible to automate all your business processes at once. A more effective automation strategy is to start with the basics and gradually scale your efforts one task at a time. In a way, task automation is a divide-and-conquer strategy toward holistic large-scale business automation.
But there’s more to task automation. And while the general approach is the same, the techniques vary between organizations. Contact us today to learn more about the business automation methods that work best for your company.