Data-driven decision-making is a critical aspect of modern business operations, with most organizations leveraging enterprise applications to execute their business processes easily and effectively. However, the users of these tools have historically needed to make a majority of their decisions manually, outside the software, creating inefficiencies that have a negative impact on operations. Businesses are increasingly turning to intelligent automation, enabled by the current generation of enterprise applications, as the solution.

Intelligent automation delivers automated decision-making and execution while allowing for human control. By leveraging artificial intelligence (AI) to synthesize thousands of data points – in real time – it provides superior decision-making, productivity, and business process automation.

There are five key tenets of intelligent automation:

• Automated decision-making: Organizations can inform their decisions accurately through the use of data including historical activity, ongoing operations, and future events (e.g., weather, holidays, etc.) to create an optimal business plan without human intervention. Automated decision-making requires a platform to collect and analyze trillions of data points. The data must be organized, so models are continuously trained for optimal execution.
• Automated execution of decisions: Once the software platform surfaces those decisions, intelligent automation can put them into motion with little to no human interaction.  
• Continuous improvement by learning from data and user actions: The platform must be self-learning with the ability to adapt to changing patterns in the data without requiring human intervention. Autonomous operation requires advanced data science, such as optimization techniques and neural networks. Automated machine learning (ML) selects and tunes models, makes iterative adjustments, and reselects models as needed. 
• Build trust through transparency: To adopt a platform, users must be able to understand the rationale behind the automated decisions.
• Always enable human control: The platform must allow human control and learn from users’ positive actions, as well as warning them of possible compliance issues.

Intelligent Automation in Action

The process begins with the application analyzing historical data, e.g., day-to-day foot traffic, sales, and transactions for a brick-and-mortar retail business. The technology captures this information while simultaneously taking into consideration how events like holidays and weather can affect demand and labor needs. It then uses the data to intelligently – and automatically – predict demand and how many employees are needed at any given time to address that demand.

Taking it one step further, intelligent automation that is powered by modern workforce management can create the optimal employee schedule – one that automatically matches business needs with employee skills and preferences. In addition, these modern applications offer employees gig-like schedule flexibility and control. They can update their work preferences at any time and use self-service tools to swap shifts, claim open shifts, and request time off. Managers reduce the time spent on schedule creation and maintenance by 50% or more if they are moving from a manual or spreadsheet-based approach. 

Intelligent automation has many positive outcomes. In the automated scheduling scenario, for example, it allows companies to operate more efficiently by streamlining labor scheduling based on intelligent demand forecasts and providing employees with a better work experience. 

Many industries have already automated various parts of their business. Retailers, for example, have invested in automated self-check-out systems. Incorporating intelligent automation into workforce management solutions is the final frontier. It enables employers to remove the guesswork from decision-making, allowing them to focus on more important matters like interacting with customers and training their teams.