Digital Twins: How Technology Is Changing The Game For Business Efficiency

This article explores how the approach to analyzing process efficiency has evolved in recent years—and why, despite all the tech and tools, some optimization initiatives fall flat.

Classical analysis vs. digital reality

In today’s ultra-competitive landscape—marked by a constant push for operational efficiency and a growing talent crunch—cutting costs has become more critical than ever. One way to make real progress is by identifying inefficiencies and optimizing increasingly complex, interconnected processes. When done right, cost-cutting efforts often lead not only to cost savings but also to better performance metrics—another compelling reason to take a closer look at how your processes actually run.

Traditionally, organizations relied on a ‘classical’ method of process analysis: interviewing employees, cross-checking their responses with paper records and timing each task with a stopwatch—assuming that people work the same under observation as they do in their normal routine. But that era is quickly coming to an end.

Digital twin at the process level

As technology advances, digital tools—like process mining systems—are becoming increasingly common. These solutions analyze process data pulled from a variety of sources, such as ERP, CRM and other platforms, to build a digital twin of a business process. This virtual model helps identify loops, deviations, bottlenecks and gaps in process standardization.

That said, process mining has its limitations. While it can deliver valuable insights, it is often heavy, resource-intensive and requires significant effort to launch and analyze in depth. And even then, it tends to leave several blind spots untouched. In reality, a hefty portion of everyday work happens outside core systems—through tools like MS Excel, Word, PowerPoint, corporate messengers, email, phone calls and so on. That’s why visualizing data only from transactional or accounting systems rarely gives a full picture of the business process—and often fails to uncover areas where efficiency is being lost.

Digital twin at the task level


To truly see the whole picture, process mining needs to be paired with task mining—an approach that traces every action employees take across various IT systems and applications. These ‘digital snapshots’ of the workday reveal how tasks are actually performed within broader business processes.

Task mining offers clear advantages: it is fast to deploy, easy to configure and can deliver actionable insights in just one week. Plus, it provides a high level of detail on how individual tasks are carried out.

Some platforms on the market combine the capabilities of both process and task mining. For example, experts at BaOne often rely on their proprietary tool, B1 Discoverit, to run digital diagnostics that reveal inefficiencies and patterns traditional analysis or process mining alone can’t detect. By blending both process and task mining, companies can not only identify weak spots and areas for improvement, but also discover smarter ways of working that boost team performance across the board.

When spying on employees is no longer the end goal

In just a few years, task mining has undergone a notable shift in how it’s being used. Originally, it was mostly seen as a motivational tool—to identify high and low performers, measure productivity, assign KPIs and hand out bonuses. To put it bluntly: managers wanted to know that Smith was running at 80% productivity and deserved a reward, while Richardson was spending 40% of his time on social media instead of doing his job.

Today, the emphasis is on comprehensive task analysis. Leaders now seek a clear, end-to-end view of how work actually gets done—across all systems, applications and stages. Their goal is to understand how their teams function in practice and find ways to improve both efficiency and quality.

These systems often help uncover best practices within a process. Over time, someone on the team might find a faster, more efficient way to complete a task—and begin outperforming the rest. Meanwhile, new joiners may struggle, even when following the instructions, and cause delays that go unnoticed by management.

Task mining systems shine a light on how processes are actually carried out by individual employees, and offer clear paths for improvement. That could mean scaling a best practice across the team, updating policies or procedures, trimming unnecessary steps, or even bringing in a software bot to take over routine work.

Here are just a few real-life examples the BaOne team has seen time and again across companies in a range of industries.

In one case, digital diagnostics powered by B1 Discoverit revealed that employees in a transactional unit were constantly switching between system windows—leading to as much as 30% of their workday lost to context switching. The root causes? Poorly structured task handling, uncoordinated communication between team members, and an overwhelming attempt to process multiple documents at once instead of working through them in sequence. Interestingly, none of this came up during traditional interviews or appeared in standard process maps. On the surface, everything looked fine—because when people know they’re being watched, they tend to follow the rules by the book.

Sometimes, a process appears to run exactly as planned, with no obvious deviations. But when you dig into task-level data, a very different picture emerges. In one company, digital analysis showed that during each cycle of a routine process, an operator switched to the corporate chat twice. All in all, nearly 20% of their time was spent messaging colleagues to resolve work-related issues. And while there’s nothing wrong with internal communication, this particular pattern pointed to regular, unstructured back-and-forth within a single process.

Cases like this often signal broader, systemic issues: outdated regulations, processes in need of redesign, or corporate tools that simply aren’t user-friendly. Sometimes, the intranet or chat system is not structured by topic or request type. Employees often grow so used to working around inefficiencies that they no longer recognize them as problems. But with the right tools and a comprehensive approach, these hidden pain points come to light—revealing clear opportunities for improvement.

These examples also highlight why it is essential to combine process and task mining: only by looking at both can companies spot inefficiencies, streamline operations and ultimately boost team performance.

The biggest mistake at the start

A common mistake is not thinking ahead about how to interpret the data and how it will feed into decision-making. This often leads to companies rolling out advanced IT tools, collecting their first round of data from process and task mining, but then struggling to make sense of it. They are unable to draw meaningful conclusions or figure out how to use the insights to make improvements. Many of these initiatives end up failing. Some leaders view the systems as mere monitoring tools instead of performance boosters and motivation drivers. Others invest in overly complex systems that require more resources to set up than the value they ultimately deliver.

The truth is that data alone doesn’t drive better processes or higher profits. You need more than just the right tools. From the outset, it’s essential to:

• Set clear goals of what you want to analyze
• Develop a structured approach to interpreting the results
• Introduce a practical methodology for leveraging the insights
• Establish a decision-making framework for management

It’s also important to define key performance indicators upfront, such as overall process speed, number of transactions, service quality, average handling time, effort per task, team size and employee utilization.

Where to look for efficiency gains?

BaOne’s experience shows that companies typically turn to comprehensive digital analysis to drive improvements in the following areas:

And of course, success isn’t just about the tools. You’ll need experienced analysts on board—or external experts—who can help interpret the data, recommend process improvements and guide the creation of a decision-making framework using digital insights.

Turning analysis in action: what’s next?

Companies typically have three types of initiatives to consider for improving efficiency. These vary in the level of changes required, effort involved and associated cost.