Intelligent Automation (IA) is a way for IT teams to automate the processes that they perform most often – the ones that are pre-defined, with clear outcomes – such as spinning up a server in the cloud once demand reaches ‘X’. Intelligent automation is enabled by a combination of:
Uses machines to simulate human intelligence. AI incorporates machine learning, or the ability to extract value from data, by analyzing data from previous events, to make intelligent decisions about workflows.
Uses software ‘bots’ to perform basic, repetitive functions that emulate human tasks such as screen scraping to reduce time spent compiling monthly reports.
Enables the coordination of users, data, and systems for the automation of workflows and incorporates Robotic Process Automation.
By leveraging these capabilities, IT teams can automate many of their workflows, reducing the time that teams spend on manual tasks and freeing up resources.
Data from the Project Management Institute’s ‘Pulse of the Profession® 2020’ report revealed that “IT organizations waste an average of $101 million for every $1 billion spent on projects and programs due to poor project performance”. The main reason for this is that most IT programs rely on manual tasks and workflows. This means that every stage of the project is labor-intensive and lengthy, and the possibility of human error is high.
In a 2019 workflow management system report, Grand View Research noted that the need to streamline business processes is driving workflow management system growth. It expects to see a compound annual growth rate (CAGR) of 27.7% between 2019 to 2025 across industries. The report notes that automated workflows increase productivity, which in turn drives revenue, while resulting in reduced errors. A report from Meticulous Research showed the AI market growing 45.3% between 2019 and 2027.
For IT teams to really benefit they need to automate as many of their IT transformation program workflows and processes as possible – which means they need more than intelligent automation.
To benefit from large scale automation, rather than just being able to automate the tasks they perform most often, they also need to implement automation to efficiently manage change – for those times when outcomes are not so predictable. To do this, they need a way to quickly understand how any change will affect the IT landscape and any areas of risk that they need to address, before rolling out programs.
For many IT teams, the barrier to large scale automation is the number of disparate tools they use to monitor and manage their estate on-prem and in the cloud. To leverage automation on a grand scale and streamline all their IT transformation programs, they need to cut through the siloes created by these tools and find a way for them to interact.
A digital platform conductor (DPC) tool connects to and orchestrates all existing enterprise infrastructure management tools across any domain. It leverages AI, machine-learning and intelligent automation to analyze the entire IT environment and define the rules for how those changes should be implemented. It goes beyond simple automation of tasks with known outcomes to define what needs to be done to efficiently and effectively roll out change at scale.
Essentially a DPC bridges the gaps created by your disparate infrastructure management tools to: