Aug 15, 2022

The 5 Components of Intelligent Automation

Regardless of what vertical or the scale businesses operate in, they are always looking for ways to be leaner and more efficient. The art of producing more from less is the key to unlocking innovation, agility, scalability, and the ability to provide more value from the same products and services.

Automation has long been a goal of businesses looking to save costs, increase productivity, and accelerate growth. Where past efforts have focused on industrial automation, we have now well and truly entered the age of intelligent automation.

Today, automation is not only imperative in low-level applications but also for a company’s overall high-level strategic goals. Intelligent automation enables businesses to achieve greater levels of excellence by improving their decision-making, speed-to-market, customer experiences, and the capabilities of their employees.

The five components of intelligent automation influence every facet of the business:

  • Artificial Intelligence (AI)
  • Robotic Process Automation (RPA)
  • Business Process Management (BPM)
  • Tools, and
  • Data

Below, we’ll discuss precisely what intelligent automation is and how the five components come together to transform businesses for the better.

 

What is Intelligent Automation?

Not to be confused with artificial intelligence (AI), intelligent automation (IA) combines cutting-edge technologies to automate low-level tasks within a business context.

However, IA relies heavily on AI-based technologies, such as Machine Learning, Natural Language Processing, Structured Data Interaction, Intelligent document processing, and RPA. Because it relies on AI, IA is also sometimes called cognitive intelligence.

Businesses across various industries benefit from intelligent automation, including banking, finance, insurance, utilities, and more. IA is a vital thread of a larger digital transformation strategy for many businesses today.

IA systems can be as simple as intelligent chatbots or as complex as provisioning engines that commission/de-commission virtual environments on-the-fly based on real-time workload data.

Note that we’re focusing on Intelligent Automation here as a distinct discipline from robotic process automation (RPA) or hyper-automation. We’ll cover the details of how they differ in-depth in future articles.

For now, it’s enough to know that RPA is a sub-field of IA, alongside artificial intelligence and machine learning. Hyper-automation, on the other hand, is concerned with automating as many business and IT processes as possible. So, IA can be used to achieve hyper-automation of a company.

Hyper-automation holistically tackles the challenge of automation by also looking at it from a human and business process perspective, while IA is concerned chiefly with the implementation of automation technologies.

 

What are the Benefits of Intelligent Automation?

When intelligent automation is appropriately implemented, its benefits are felt across every area and level of the business. At its core, intelligent automation empowers your human resources with smart technologies and agile processes to make faster and more informed decisions.

 

  • Reduce operational costs: According to KPMG, IA can help financial organisations cut costs by as much as 75%. Roland Berger found that companies implementing IA could save up to 40% annually and reduce the time spent on processes by 40-70%. IA technologies help optimise processes so that they can be scaled for smaller teams or more clients.
  • Save time: Optimised and automated processes require less manual human interaction to function. RPA can also be used to completely automate specific repetitive, back-office tasks to free up employees to solve more strategic problems.
  • Reduce the impact of human error: Repetitive, high-volume tasks create prime conditions for human errors to pop up. Human error is also especially prevalent in production and invoicing, two critical areas. Automated, rule-based processes eliminate human error risk while strictly enforcing built-in governance and compliance measures.
  • Maximising the value of business data: IBM estimates that bad data costs individual businesses $9.7 million annually, costing the economy $3 trillion in total. IA has the potential to help companies to improve how they collect, process, store, reconcile, and analyse their data.
  • Improving customer experience: Collectively, IA technologies enhance customer experiences by improving speed to market and allowing businesses to improve their products and services by better understanding their customers. High customer satisfaction means higher retention rates which means growth.

With a longer-term strategy in place, IA also holds potential benefits for employee morale, innovation, fraud monitoring and detection, and even cybersecurity.

 

The Five Components of Intelligent Automation

Now that we have a grasp on the role that IA plays in modern-day business let’s look at the nuts and bolts. Each company may (and should) have a tailored intelligent automation strategy that prioritises components based on their unique circumstances. However, these are the core five elements that make up a holistic approach to intelligent automation:

Artificial Intelligence

Businesses want to make better decisions, based on more information, within less time and with high accuracy. There is no better candidate to achieve that goal than through the use of AI. You can think of artificial intelligence as the brain and beating heart behind intelligent automation.

So, in this context, AI is concerned with tasks that are cognitive in nature, such as Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning (ML), and Intelligent Character Recognition (ICR)

AI is the cornerstone of scaling and extracting the maximum value from intelligent automation in a business. By combining complex algorithms with machine learning, companies can efficiently analyse structured and unstructured data at previously impossible scales.

As AI matures, it will be used to process data in increasingly complex and efficient ways. The result is more valuable, actionable, and timely access to information decision-makers can use. This is often what’s referred to as the AI “decision engine.”

Over time, this allows businesses to develop a knowledge base that can be a valuable resource for understanding past events and making future predictions. And, to produce formulas for accelerating the decision-making process even further.

AI is also not a one-trick pony but can be utilised within individual processes, teams, departments, or an entire organisation. AI not only has the potential to revolutionise business processes but also to improve the quality of life and productivity of employees and the value provided via customer experiences.

Robotic Process Automation (RPA)

If AI is the mind, then robotic process automation, or RPA, is the body. Unlike AI that’s primarily concerned with cognitive processes, RPA is involved with the automation of repetitive, rule-based processes. At a low-level, RPA bots are adept at mimicking human interaction.

RPA mostly takes the form of software robots, or bots, that are capable of carrying out back-office tasks. Think of high-volume, time-consuming processes that require little critical thinking. For example, data scraping, compliance reporting, customer order processing, claims administration, or scheduling systems.

To see why RPA is necessary, you only need to know that 50% of companies spend up to $25 per manually processed invoice. And that sales reps spend up to 64% of their time on non-revenue generating tasks.

The more cognitively demanding tasks are for humans or AI. However, they can be combined with AI or human resources to considerably scale up the completion of more complex tasks.

The main goal is to free up employees from having to deal with tasks that don’t require human-level intelligence. So, employees can use their time and decision-making capabilities where needed most. For example, where there’s a need for strategy, creativity, and innovation.

Because of the relative simplicity of these bots and the operations they are tasked with, they can be implemented relatively quickly and easily. RPA can also rapidly be scaled across projects, processes, or teams.

As a result, the ROI for implementing RPA can be quite high. According to the institute for RPA, businesses stand to enjoy immediate cost savings of between 25-50%. It’s no wonder that 20% of organisations adopted RPA by 2021.

 

Business Process Management (BPM)

In an effort to save cost, time, and effort, businesses are moving toward adopting the principles of Lean Programming. This involves an in-depth process of re-examining all business processes as well as their sub-routines and tasks. Not to mention how, when, and why humans are involved with various business processes.

Through this process, the performance, as well as the positive and negative contributors to said performance, are identified. The processes are then stripped of any redundancies or inefficiencies, and unnecessary human participation is replaced with AI or RPA. So, in the era of IA, business process management is also often referred to as “workflow automation” or “business process automation.”

The end goal is to limit time spent on non-value-adding activities and improve the efficiency and accuracy of essential processes. That’s especially true for processes involved in production or service delivery.

Aside from making employees more efficient, optimised business processes can also improve customer-facing experiences and increase the speed of business.

It’s estimated that inefficiencies cost companies between 20-30% of their annual revenue each year. So, this is one area that can yield massive results.

 

Tools

Companies look to specific technology solutions to solve challenges or address inefficiencies. Third-party tooling can offer cost-effective and high-ROI solutions on an as-needed basis.

That is particularly true as SaaS platforms and endpoint software are becoming increasingly advanced in terms of AI, machine learning, and automation.

However, companies must be careful about what tools they adopt and how they integrate them into their operations. Unconsciously adopting tools without thinking of the whole picture can leave independent teams operating disjointedly.

The company’s overall technology ecosystem can become siloed according to specific business functions. That can lead to company-wide visibility, data sharing, and collaboration problems. At some point, an overall technology strategy will be needed to ensure you maintain coherence across all your teams, systems, and processes.

That being said, tooling can result in some quick wins for businesses looking to make inroads into intelligent automation.

 

Data

Data is playing an increasingly important role in the success of organisations. It determines a business’ ability to strategise, make snap decisions, adjust to emerging trends, and offer value to its customers.

In fact, statistics show that data-driven businesses are 23x more likely to retain customers and increase their profits by up to 8%. What’s more, it has the potential to help companies to grow by better understanding their customers.

 

It’s no exaggeration to say that for many businesses, their data IS the business.

 

In the lifecycle of intelligent automation, data is what feeds all the other components. It’s used, consumed and processed using RPA and AI software. It’s also internal data regarding performance and other metrics that drive the acquisition of tooling as well as the optimisation of business processes.

As such, data collection tends to be constant and large-scale. Compliance and governance measures also need to be in place to ensure the data supply chain is transparent, trustworthy, and accessible. Automation and intelligent data handling are required to ensure businesses can extract value from their data efficiently, accurately, and within reasonable timeframes.

This creates a loop where AI, smart/predictive analytics, and automation can be used to scale data operations. At the same time, the enhanced data can be used to further AI systems through machine learning and further optimise business operations.

 

Conclusion

While we can talk about the individual components of intelligent automation as distinct concepts, they all have the same overarching goal. That is to help businesses foster greater efficiency, consistency, and productivity while saving costs and time and freeing up a company’s cognitive resources to focus on more strategic challenges.

As an immediate and short-term goal, IA will help businesses achieve bottom-line and top-line growth. Going forward, IA will also play a pivotal role in companies being able to outmaneuver competitors or industry disruptors.

If you haven’t done so, the best time to assess your business’ IA maturity and develop your own IA strategy is now.