That’s where AI comes in.

In our previous blog, we explored how AI-powered task mining and process intelligence can help uncover hidden inefficiencies and build a roadmap for transformation. Now, we’re diving deeper into the five steps that finance transformation leaders and CFOs can follow to take action and deliver real, measurable impact.

1. Plan your initiative around cost savings KPIs

Start with clear goals and stakeholder alignment. Before jumping into any transformation effort, define the business outcomes that matter most – whether it’s reducing hours spent on low-value tasks, cutting costs in a specific department, or improving the accuracy and speed of your financial close.

To stay focused, stakeholders should determine:

• What is the initiative, and how will it be measured?
• What does a successful outcome look like?
• What are the anticipated findings and blockers?
• Who is responsible for driving the initiative?

Having these answers documented up front ensures that the insights you uncover will be relevant, actionable, and tied to enterprise value.

2. Start with high-volume processes in finance

Finance is the ideal place to begin. It’s home to complex, high-volume processes that span systems and regions – and where inefficiencies have a direct impact on the bottom line. Accounts payable, payroll, procure-to-pay, and order-to-cash all involve manual desktop work, frequent handoffs, and often inconsistent execution.

By applying task mining to these processes, you can capture granular insights including:

• Steps and sub-tasks within the process
• Time spent in each application
• Bottlenecks and workarounds
• Variations between teams and regions

This gives you the data needed to identify inefficiencies that would otherwise go unnoticed.

3. Analyze the process using task mining technology to uncover hidden inefficiencies

Once you’ve mapped out the full process with task mining – including exceptions and variations – the real opportunities emerge. Look for the “invisible work” like:

• Copying and pasting between systems
• Manually entering the same data in multiple places
• Searching through folders or inboxes
• Free-text entry into legacy systems

These actions may seem minor in isolation, but they add up to thousands of inefficient hours across a large, global team. With AI-powered insights, you can prioritize improvements based on time savings, ROI, and ease of automation.

4.  Eliminate unnecessary steps to streamline the process

Now that you have the data, it’s time to act. Start by eliminating unnecessary steps, streamlining the ones that remain, and retraining employees on a more efficient path. Then automate the repeatable parts – confident that you’re improving the process, not just speeding up a broken one.

Examples of high-impact changes include:

• Embedding free-text fields directly in systems to reduce app-switching
• Standardizing templates and work instructions
• Replacing manual steps with RPA or GenAI-powered assistants

These types of improvements don’t just cut costs, they also increase consistency, reduce risk, and improve employee experience.

5. Build intelligent automation using process maps

With clean process data and a library of prioritized opportunities, you’re ready to scale and build intelligent automation. Task mining gives you export-ready documentation and process maps that make it easier to build a business case, estimate savings, and choose the right automation tech – whether it’s RPA, IDP, GenAI, or agentic AI.

And because this data is continuously collected, you can track performance over time and adjust as needed, transforming cost-cutting from a one-off project into a continuous improvement engine.

Want to see real-world examples of successful cost reduction?

These five steps are based on our comprehensive guide, The CxO’s Guide to Data-Driven Cost Cutting, which outlines how task mining helps Fortune 500 enterprises eliminate waste, standardize processes, and scale automation with real-world examples from companies like Goodyear and ClearBank.