From niche technologies to marketing spin, Simon Perks, Head of Robotics and AI at Agilisys, cuts through the confusion surrounding intelligent automation today.

No one enjoys dull, repetitive jobs. Rather, it’s when we use our most human qualities—creativity, empathy, intuition, and so on—that work becomes truly engaging. While many people are understandably fearful about the rapid rise of intelligent automation, as someone who used to prepare files for microfiche, it’s actually great news that machines will increasingly take over mundane and monotonous tasks.

Like the past industrial revolutions, this unprecedented change will create new livelihoods, as well as transforming existing roles. However, the future looks bright: according to the World Economic Forum, “133 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms”, creating a net gain in global jobs.

Now is the time for organisations to explore the potential of intelligent automation, as well as prepare and upskill employees for the new world of work. However, one of the first stumbling blocks in planning for the future is simply the huge and confusing array of technical terms in use today.

To help you map out the right path for your organisation, let’s look at the most common terms and decode what they really mean.


The highest level of intelligent automation deals with cognition. As new and emerging technologies continue to impact working life, terms relating to this area see a lot of media coverage. However, it’s not appropriate for every organisation to be at the forefront of innovation. Tried and tested solutions should be the primary focus for most. Let’s examine what some of these terms are trying to describe:

  • Artificial Intelligence (AI): Systems that display a human-level of understanding and judgement in their responses. However, AI is often used as a ‘catch-all’ term for any systems with elements of knowledge, vision, speech, language and search.
  • Machine learning: Rather than programming systems for every eventuality, machine learning takes a leaf from childhood development and allows automated systems and AI to learn new processes through pattern recognition.
  • Deep learning: A type of machine learning that enables automated systems to mimic human tasks, such as interpreting natural language or understanding images.


At the next level we find intelligent automation tools designed to interact with people. With organisations facing ever more unstructured communication across a host of different channels, these technologies can help interface with citizens and process enquiries.

  • Chatbots: These systems simulate human conversation and are often used in customer services and marketing to field enquiries. While still most commonly text-based, they’re now evolving to include speech and video capabilities as well.
  • Computer vision: Allows automation software to identify and interact with information from various sources or images.
  • Natural language processing (NLP): Enables computers to comprehend and simulate natural language, including real-time machine translation.
  • Virtual/Digital assistants: A software entity that can engage with users in a human way to provide help and support. Apple’s Siri and Microsoft’s Cortana are good examples.


As this level of intelligent automation, we find technologies which often offer the most immediate and proven value to organisations. Automating repetitive activities enables substantial time-savings, giving staff back around 40% of their day to focus on other tasks.

Repetitive activities can be found right across organisations—in HR, customer services, finance departments, and more. Everything from onboarding new employees, to customer notifications and contract creation can be automated.

By replacing the need for manual activities across different systems, it’s possible to speed up tasks considerably. Better still, intelligent automation doesn’t make mistakes, ensuring greater accuracy and eliminating wasted time and effort.

  • Software robots: As opposed to the physical robots found in factories, these carry out routine tasks digitally.
  • Robotic process automation (RPA): Software robots that can tackle a variety of digital tasks, such as recording data, running applications, or triggering processes.
  • Unattended/Attended RPA: Software robots that either need no human input, or alternatively rely on it in carrying out tasks.
  • Workflow automation: Applying RPA to automate the steps in routine business tasks, creating efficiency gains and allowing people to focus on more engaging work.

Thanks to our new partnership with award-winning intelligent automation provider Thoughtonomy, and two decades of experience enabling the public sector to go further and faster, we’re in a unique position to support you on this journey.

Automating Work. Humanising Jobs.

From building the business case for change, to collaborating closely with you to identify the workflows where intelligent automation can best be applied, our expert consultancy will help you take the right first steps. We’ll enable you to assess and apply the best-suited solutions on a small scale—ensuring a firm return on investment and training your staff to make the most of new capabilities. We’ll then help you build on this foundation, extending the benefits across your organisation.

Learn more about the potential of intelligent automation in the public sector and how Agilisys can help you get started.