Let's begin by defining intelligent automation. It is said to span the entire automation journey − automating both front- and back-office business processes − and is an enabler, indeed an accelerator, of digital transformation.
Deloitte notes intelligent automation has great potential to automate non-routine tasks involving intuition, judgement, creativity, persuasion, or problem solving. It highlights that the decreasing costs of data storage and processing power are driving rapid developments in the field of artificial intelligence (AI), creating a new breed of cognitive technologies.
When combined with robotic process automation and powerful analytics, states Deloitte, these cognitive technologies can form intelligent automation solutions that either directly assist people in the performance of non-routine tasks or automate those tasks entirely.
Pioneering enterprises around the world are leveraging intelligent automation in many ways, including wealth management companies using it to generate meaningful performance reports for customers, and global financial institutions utilising it to continually strive to improve compliance, and more.
The uses of intelligent automation are potentially limitless. They are also typically more expensive and take longer to implement than robotic process automation (RPA) tools.
Unlike RPA tools, which can be broadly applied, AI solutions require more extensive configuration and machine learning (ML) that is specific to a much narrower business purpose and the complex scenarios it may encounter.
According to leading authorities on intelligent automation, Alan Pelz-Sharpe and Dan Lucarini, when tackling intelligent automation, companies need to remember the following:
- The core purpose of automation is to reduce human work activities.
- Automation entails digitising (encoding) work activities.
- Employees and customers will always be impacted by automation.
Automation requires − as a foundational step − a detailed understanding of the activities making up the business process that is to be automated; without this, the automation project will, in all likelihood, fail.
The uses of intelligent automation are potentially limitless.
Pelz-Sharpe and Lucarini highlight the fact that businesses are reframing, rethinking and reimagining their activities and determining how to intelligently improve them. The technology sector has responded with a plethora of automation tools and terms, such as RPA, AI, ML and even 'hyper-automation’. The choices and different approaches to automation can appear daunting.
What is driving uptake of intelligent automation?
According to McKinsey, when done right, it has proven to deliver real benefits, including:
- Distinctive insights: Hundreds of new factors to predict and improve drivers of performance.
- Faster service: Processing time reduced from days to minutes.
- Increased flexibility and scalability: Ability to operate 24/7 and scale up or down with demand.
- Improved quality: From spot-checking to 100% quality control through greater traceability.
- Increased savings and productivity: Labour savings of 20% or more.
Delaying automation initiatives that enable remote working, reduce tedium, and improve customer and employee experiences brings the risk of stagnation to businesses that could otherwise be flourishing.
What is the intelligent approach?
Pelz-Sharpe and Lucarini say the first thing to understand is that one size does not fit all, and every enterprise has different requirements.
Moreover, every organisation has different resources, whether human or technical, to draw upon. They emphasise that traditionally, automation projects have been driven by technology advances. Today, however, they are increasingly being driven by a more considered, holistic and intelligent mindset that starts with the business challenges to be resolved rather than with the technology.
McKinsey highlights the four most important practices that are strongly correlated with success:
1. Understand the opportunity and move early: Start taking advantage of automation and AI by assessing the opportunity, identifying the high-impact use cases, and laying out the capability and governance groundwork.
2. Balance quick tactical wins with long-term vision: Identify quick wins to automate activities with the highest automation potential and radiate out, freeing up capital; in parallel, have a long-term vision for comprehensive transformation, with automation at the core.
3. Redefine processes and manage organisational change: Since 60% of all jobs have at least 30% technically automatable activities, redefining jobs and taking an end-to-end process view are necessary to capture the value.
4. Integrate technology into core business functions: Build AI and other advanced technologies into the operating model to create transformative impact and lasting value, support a culture of collecting and analysing data to inform decisions, and build the muscle for continuous improvement.
What are the different types of intelligent automation tools?
Pelz-Sharpe and Lucarini note there are many:
- RPA tools leverage bots to perform and automate repetitive tasks.
- Case management is used to manage highly-volatile and human-centric process activities, where multiple different sources of data and information are required to feed a single 'case'.
- The rules engine automates the application of predefined business, compliance, or procedural rules (ie, an organisation's policies and practices).
- Cognitive capture tools are a modern progression of traditional document capture technologies.
- Business process management is used to automate complex processes, even an organisation's entire business activities.
- Customer communication management: Communicating with customers through an ever-growing number of channels is challenging. Automation is required to deliver consistent, accurate, engaging and compliant messages in bulk, while at the same time personalising them to an individual customer’s needs.
- Content services platforms capture, organise and manage all the content in a library from across the enterprise.
- AI and ML are used extensively, either as embedded elements of broader automation systems like cognitive capture, or as standalone automation systems.
- Integration between content platforms and business applications.
In my second article, I will reveal how you can decide how intelligent your systems need to be.