AI and Automation

Build, test, and deploy systems that change how humans use technology for process-based work.
Ivy Simon
Editorial Writer
Build, test, and deploy systems that change how humans use technology for process-based work.

Though task automation has long been a key goal of software development, using artificial intelligence (AI) to more intelligently augment automated work processes is now the next frontier. 

In recent years, AI has been a game changer for businesses around the world in automating areas such as big data analytics, delivering personalised purchasing experiences, and better customer service support. Artificial intelligence algorithms are also increasingly used to automate rote payroll, HR, and accounting processes. 

The overall goals of developing AI are twofold. The first is to seamlessly automate mundane tasks so that humans can be freed up to focus on more cognitive, higher-value tasks instead. And the second is to more efficiently execute large data-driven processes that would otherwise take a human too much time to sort through.  Some of the business benefits of AI and automation are:

  • Improved data security
  • Increase work efficiency and productivity
  • Lower costs
  • More consistent process management

Career overview

A related degree in IT or engineering will give you an advantage if you are keen on working in this sector, but it is not required. What’s more important is knowing how to code, along with other soft skills such as situational and commercial awareness, client management, and project management skills. 

There is a broad variety of employers in the playing field for this sector, including tech companies, consulting firms, or software providers. Larger organisations typically offer graduate programmes that allow fresh hires to specialise in artificial intelligence and other automation-focused technology. There are plenty of start-ups and smaller enterprises in this field as well, but those typically require new hires to take more initiative to learn on the job.

Much of the work around AI and automation at the moment involves building and deploying increasingly complex machine learning processes/algorithms, and then “training” those to recognise and respond to visible patterns of human behaviour. Expect to do a lot of work with large datasets, isolating patterns for the machine learning processes to focus on.    

Career progression is similar to that of a software developer. As you gain experience, you will be assigned to larger development projects, before moving on to project management and leadership responsibilities. 

Further up the ladder, you may need to start liaising with clients to get a first-hand understanding of their problems. This is key to understanding what AI automation should more practically address, and where to focus future developments towards.

Trends and development

Numerous emerging technologies also have the potential to integrate into the overall automation ecosystem. 

For instance, DevOps discipline, which is now standard process for how large development teams build and manage software, relies heavily on automation to reduce a lot of redundant work. Immersive technologies such as augmented reality (AR) and virtual reality (VR) are increasingly leveraging on AI as well, particularly in sectors such as engineering (e.g. automated diagnostics for complex machinery), retail (e.g. interactive shopping experiences), and healthcare (e.g. pre-surgery diagnostics and telemedicine).

The increased rollout of 5G technology in the next few years will also open up new doors for automation. As 5G speeds bring lowered latency and thus faster processing and response times between devices, widescale implementation of AI-enabled processes will become far more practical across all industries.   

Pros and cons

Diversified field

Because AI and automation has broad-based applications across various industries, you can expect to see your work applied in interesting, cutting-edge ways that further modernise existing ways of working.

Highly experimental

On the flip side, AI is still very much an experimental technology with huge room for improvement. You will need resilience as you face constant roadblocks and logical setbacks in training algorithms to respond in desired ways.  

Required skills

  • Commercial awareness
  • Resilience
  • Problem solving skills
  • Interpersonal skills
  • Analytical skills and attention to detail
  • Ability to work in a team