Need advice? Call Now, Schedule a Meeting or Contact Us
Speak to an advisor
Discover how intelligent automation transforms project management by optimising workflows, enhancing decision-making, and saving time.
Intelligent automation is an optimisation lever that helps projects transition from rule-based fixed automation to judgement-based evolutionary processes. It is a conglomerate of multiple cognitive technologies like machine learning, RPA (Robotic Process Automation), deep learning, video analytics, emotion recognition, neural networks, etc. It creates a virtual workforce to emulate human execution of repetitive processes.
Rules-based fixed automation, also known as traditional business process automation, is modelled from a process perspective. The project manager starts with an outside-in approach where a project derivative models a specific flow with defined process steps. Each process step has a system interface specified in an object. The data from these objects is given to the processes, which further outline the business rules to make decisions on this set of data. All these help in achieving the end goal of the project.
Judgment-based evolutionary automation, or simply intelligent automation solutions, is designed with an inside-out approach, unlike traditional automation. The project manager finalises an end goal in mind and then determines the business rules that are required to achieve the end goal. Once the rules are defined, the next step is to analyse the dataset required to support these business rules, which includes all objects that need to be created and the process steps to run these objects.
1. Identify: The project manager will perform a thorough analysis of the current state and processes to determine the repetitive tasks that can be a candidate for automation.
2. Select: The next step is to select the tools/utilities that can help transform these manual, repetitive tasks into advanced AI-automated steps. Some of the advanced features include NLP (Natural Language Processing), advanced data analytics, content classification, video analytics, speech-to-text capability, etc.
3. Plan: Plan the intelligent automation strategy involving cognitive decision-making and procure the dataset to run the algorithms in order to produce the expected results. This phase also includes defining the acceptance criteria for each use case and the reporting metrics to measure the automation success.
4. Execute: Set up the environment to run these automated tests and schedule the unmonitored batch jobs to get the savings efficiency from this intelligent automation.
5. Report: With the intelligent automated tools, the reports are published to the stakeholders in real-time without any delay. The project manager evaluates these reports for efficiency and addresses any concerns highlighted in the automated results to leadership.
A project manager has a crucial role to play when it comes to implementing intelligent automation solutions. Primary responsibilities include, but are not limited to:
To get started, complete a high-level automation capability and maturity evaluation within your organisation to identify areas of opportunity and then understand the opportunity potential and effort estimation to implement the intelligent automated solutions.
One-time offer, don’t miss out. Your next career milestone starts here.
Enter your email to receive your code instantly. By signing up, you agree to receive our emails. Unsubscribe anytime.
IPM75BSP6
Don’t forget to copy and save this one-time code. It is valid until 31 July 2025.
We use cookies to ensure you get the best experience of our website. By clicking “Accept”, you consent to our use of cookies.