The Top 22 Automated Process Discovery Tools to Know in 2024

In this comprehensive guide, we will explore the top 22 automated process discovery tools that can help your organization unlock efficiency gains, cost savings, and process improvements in 2024 and beyond.

Introduction: The Power of Automated Process Discovery

First, let‘s understand what automated process discovery means and why it matters. Process discovery tools analyze system logs, databases, ERPs and other enterprise data sources to automatically detect the end-to-end processes and workflows within your operations. Instead of having to manually map out workflows step-by-step, these tools use algorithms, analytics, machine learning, and other technical capabilities to deliver dynamic process visibility.

The insights uncovered through automated discovery can help you identify process bottlenecks, deviations, redundancies and other issues. This allows you to optimize workflows for faster processing, fewer errors and lower costs. Research shows organizations achieve 15-25% gains in efficiency, productivity and cost reduction through process optimization powered by automated discovery tools.

Categories of Automated Process Discovery Tools

There are three major categories of automated process discovery tools:

RPA Software: Robotic process automation (RPA) tools like UiPath and Automation Anywhere that use bots to discover processes.

Analytics Software: Analytics and business intelligence platforms like SAS and Alteryx that analyze data to provide process insights.

Dedicated Process Mining Software: Purpose-built process mining tools like Celonis and Minit that use specialized algorithms to detect processes.

Let‘s explore the leading options in each category and understand their capabilities for automating the discovery and optimization of your workflows.

RPA Tools With Automated Process Discovery

RPA tools use software "robots" that mimic human actions to discover processes. For example, UiPath robots can open applications, read and enter data, fill forms and more to track the flow of a process. The sequence of steps performed by the robot is then analyzed to capture the as-is process model.

Here are the top RPA tools with automated process discovery capabilities:

  • UiPath – UiPath Process Mining auto-discovers processes by tracking UiPath software robot activity. It allows real-time and historic data analysis to detect process changes.
  • Automation Anywhere – Automation Anywhere‘s IQ Bot uses process mapping to document processes as a human would. It generates process documentation with 70-80% automation.
  • Blue Prism – Blue Prism integrates with leading process mining tools like Celonis using ready-to-use connectors that enable automated process discovery.
  • NICE RPA – NICE Enlighten AI provides intelligent process discovery by analyzing user actions on desktop screens. It suggests opportunities for process improvement.
  • Kryon RPA – Kryon Process Discovery uses machine learning algorithms to provide smart process visualization without any coding. It enables real-time process tracking.

RPA-driven process discovery tools are quick and easy to implement. However, the downside is that the bots only capture the subset of processes that they are programmed to follow, resulting in incomplete end-to-end visibility.

Analytics Tools With Automated Process Discovery

Analytics platforms apply data mining, visualization, statistical modeling and machine learning techniques on historical data to detect patterns and reveal processes.

Here are some top analytics tools with automated process discovery capabilities:

  • Alteryx – Alteryx offers the Process Mining Tool and Designer Tool to discover processes from data pipelines and provide insightful process analytics.
  • SAS – SAS Visual Process Mining uses ML algorithms to auto-discover processes from event log data and identify deviations from expected process flows.
  • Qlik – Qlik Sense Enterprise offers process mining and visualization as an add-on capability to its leading analytics platform.
  • IBM – IBM Analytics Engine includes automated process discovery powered by AI to explore and analyze end-to-end process flows at scale.
  • Minitab – Minitab Process Sleuth uses statistical analysis of timeline and event data to discover processes and variations from standard workflows.

Analytics tools can provide a holistic view by tapping into multiple data sources. However, they may require more upfront configuration and data wrangling compared to RPA-based solutions.

Dedicated Process Mining Software

Specialized process mining tools offer the most powerful capabilities for automated discovery and in-depth process analytics. They apply tailored algorithms and data models to unlock deep process insights.

Here are some of the top dedicated process mining platforms with advanced auto-discovery capabilities:

  • Celonis – Celonis uses its patented Process AI technology to automatically surface processes and variants from system-wide data. It also enables real-time process monitoring.
  • Signavio – Signavio Process Intelligence applies smart analytics algorithms to automatically suggest process models based on end-to-end event log analysis.
  • myInvenio – myInvenio provides automated process discovery, analysis and monitoring through preconfigured dashboards, workflow algorithms and prebuilt connectors.
  • QPR ProcessAnalyzer – QPR uses its built-in ProcessAI capabilities to automatically generate AS-IS process maps and identify optimization opportunities based on data.
  • Skan.AI – Skan uses computer vision, RPA and machine learning techniques to deliver quick no-code process discovery and optimization recommendations.

Dedicated process mining tools require upfront data integration but provide unparalleled depth of visibility and analytics compared to other options.

Comparing Capabilities of Top Tools Across Categories

ToolDiscovery TechnologyData SourcesAnalytics CapabilitiesEase of Use
CelonisMachine learning process algorithmsAll major ERPs and systemsReal-time monitoring, process simulation, root cause analysisModerate configuration and training
UiPathRPA robot trackingRPA robot activity logsHistoric and real-time discovery, flow visualizationQuick implementation through RPA bots
MinitabStatistical analysis and data miningDatabases, ERPs, CSVsStatistical insights into process variations, six sigma analysisEasy to use for statisticians
SASCombination of algorithms, ML and statsWide range of sourcesAnomaly detection, predictive modeling, process simulationSignificant upfront data configuration
Skan.AIComputer vision and machine learningUI-level dataRapid discovery, optimization recommendation, real-time insightsVery user friendly with no-code setup

This comparison shows how the most advanced process mining tools like Celonis combine multiple techniques like ML, data mining, visualization and statistics to provide the deepest discovery capabilities. RPA-based solutions are fastest to implement but provide limited data access. Analytics tools require more upfront configuration work but provide flexibility.

Major Open Source Libraries for Process Discovery

For expert users like data scientists and developers, open source process discovery libraries allow enabling custom automation using Python, R, Java or other programming languages.

Here are some leading open source libraries for process mining and discovery:

  • Fuzzy Miner – Provides process discovery, visualization and abstraction capabilities for exploratory process mining.
  • Heuristics Miner – Uses heuristic algorithms to mine processes from event data and build hierarchical process models.
  • Alpha Miner – Efficiently discovers complex workflow process maps from event logs using the alpha algorithm.
  • Inductive Miner – Specialized for discovering block-structured hierarchical process maps by analyzing event sequences.

These libraries implement common process mining workflows and algorithms in code and can be customized for specific process automation needs. However, they require skilled resources to implement and maintain.

Key Capabilities to Look For in a Process Discovery Tool

Now that we‘ve explored the leading options, let‘s discuss what core criteria you should evaluate when selecting an automated process discovery tool:

Integration capabilities – Look for prebuilt connectors or APIs to easily link up enterprise systems like your ERP, CRM, databases and extract process data.

Analytics power – Seek advanced analytics like drill-downs, root cause analysis, anomaly detection that help optimize processes. AI-powered analytics are ideal.

Ease of use – Pick tools that offer simple drag-and-drop interfaces, customizable dashboards and require minimal coding.

Process visualization – Interactive process maps, custom hierarchies and swimlane views provide intuitive visibility.

Scalability – Ensure the tool can handle large data volumes and complex processes based on your environment.

Collaboration – Capabilities to share process models, insights and optimization ideas across teams.

Process monitoring – Real-time tracking helps assess impact of changes and continuously improve processes.

Selecting the right solution depends on your existing infrastructure, technical expertise, budget and analytics needs.

From Technology to Impact: Real-World Process Improvement Results

Beyond the technology, what results are organizations seeing from automated process discovery? Here are a few examples:

  • An insurer using Skan.AI cut new policy setup effort by 35% through process optimization and automation.
  • A retailer using UiPath Process Mining reduced inventory carrying costs by 28% by improving supply planning processes.
  • A bank using myInvenio achieved 17% faster mortgage application processing by identifying and fixing bottlenecks.
  • A software company used Celonis Execution Management System to optimize their lead to cash process, driving a 42% reduction in DSO.

The common thread is dramatically improved efficiency, cost reduction and customer experience through data-driven process insight. The above examples showcase only a fraction of the potential – automated process discovery tools can transform processes across finance, HR, supply chain and customer service.

Key Implementation Challenges and How to Overcome Them

However, real-world implementation of these tools also brings some key challenges that you should keep in mind:

  • Data quality – Inaccurate or incomplete data can skew discovered processes. Invest in cleaning data beforehand.
  • Algorithm tuning – Discovered processes may reflect "noise" unless algorithms are tuned. Work closely with the vendor.
  • Stakeholder resistance – Business teams may push back on transparency or changes. Get them involved early.
  • Legacy IT barriers – Connecting multiple old systems can be difficult. Consider API-based integration.
  • Tool overload – Too many tools can create fragmented insights. Consolidate through a data hub or pipeline.

The key is to start small, deliver quick wins, and get stakeholder buy-in across business and IT to expand scope. With careful change management, organizations can overcome these hurdles.

Conclusion and Key Recommendations

In summary, automated process discovery represents a modern platform every organization needs to enable continuous improvement. These tools shine a spotlight on process performance through detailed data analysis – eliminating reliance on assumptions or anecdotes.

Powerful algorithms, machine learning and visualization accelerate fact-based process insights and optimization. Leading solutions like Celonis offer the end-to-end capability to discover processes, monitor changes, analyze bottlenecks, model improvements and track impact.

My recommendation is to start by auditing your process pain points, data environment, and improvement goals. This will guide your tool selection process. Dedicated process mining solutions offer the deepest capabilities, albeit with more implementation effort. Leverage free trials and consult with experts to find the best fit.

With the right platform in place, you can rapidly transform reactive process management into proactive, data-driven decisions and automation. The efficiency and competitive edge gains are proven and substantial. Let automated process discovery supercharge your continuous improvement journey.

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