Stage a Productivity Revolution with Process Automation, AI and Data Fabric

IT’s constant drive to streamline processes and improve productivity will be even more important in 2024. With many companies still feeling the sting of inflation and high-interest rates, there’s pressure on IT departments to do more with less and consolidate as much as possible while still meeting the needs of the business.

There are dangers to this approach, however. For example, time and budget restraints can harm productivity, increasing business backlogs and fomenting conditions that cause users to download applications without IT’s approval. The resulting increase in shadow IT can lead to security risks and application sprawl, further bogging down productivity as IT professionals attempt to identify and control the applications using their networks.

To prevent this from happening, IT organizations must make improving productivity a top priority this year and beyond. Analyzing and automating processes, infusing them with AI and adopting a more intelligent approach to data management are the keys to achieving this objective.

Analyzing and Optimizing Processes through Process Mining and Automation

The only way to make effective productivity improvements is to analyze current processes and get baseline measurements. Teams must be able to see the bottlenecks that are holding up their processes to make accurate and meaningful adjustments.

Process mining provides the visibility necessary to discover process patterns and identify areas of improvement. Process mining analyzes event logs from business systems to provide a visual representation of workflows. Teams can see where delays are, why they are happening and how workflows are deviating from the ideal process flow. They can use this information as their baseline to make educated assumptions about how to improve processes. They can then have conversations with other lines of business owners about how to implement process improvements.

Incorporating AI into Automated Processes

Process mining provides insights that IT teams can use to determine which aspects of their processes can be improved through automation. For instance, perhaps there are parts of the current operation that are being hampered by time-consuming actions that a machine could easily handle. Process mining can reveal these actions, allowing IT to design an automated process around them.

Process mining analyzes event logs from business systems to provide a visual representation of workflows.

Automating according to pre-defined business rules is a good first step. Eventually, teams may wish to scale their automation to include techniques that offload work from knowledge workers, such as robotic process automation and AI.

AI is rapidly gaining traction in the enterprise IT ecosystem for its ability to boost productivity. It is increasingly being deployed to analyze data patterns and make intelligent recommendations, digitize and process the contents of documents previously handled manually, intelligently route work to the most appropriate teams based on past behavior or summarize and synthesize vast amounts of information for faster consumption. Regardless of the type of AI being employed, it is clear that this new type of worker — one that helps, not replaces its human colleagues — is here to stay.

Building a Data Fabric Architecture for Better Intelligence and Efficiency

Fragmented customer data — where customer information is often siloed between different business units — can be a big productivity killer. Businesses sometimes attempt to solve this challenge by bringing online separate systems to catalog customer information for each unit, but this only increases the number of tools IT needs to manage. It also makes it hard to create a cohesive view of each customer.

It’s far better to adopt a data architecture that integrates multiple systems and provides a cohesive and unified view of customer data. This can be achieved with a data fabric, an abstracted data architecture layer that connects data across disparate systems. A data fabric combines business data so that teams can achieve a complete view of each customer.

A data fabric also eliminates the need to migrate data from one system to another, saving developers and organizations time and money. Developers can accelerate the development of new processes and solutions that serve the entire enterprise, not just individual business units.

By employing these three strategies and their corresponding technologies, IT teams will be able to improve processes, boost efficiency, enhance intelligence — and make 2024 the year they reach their productivity objectives.